LinqAlpha, the New York-headquartered AI startup building intelligence tools for institutional investors, recently raised US$22 million in a Series A round anchored by AVP, Atinum Investment, and GFT Ventures, with a notably Asia-heavy syndicate including SV Investment, Mirae Asset, Samsung Securities, East Ventures, and others spanning Singapore, Hong Kong, South Korea, Japan, and India.

Founded by Jacob Choi, Subeen Pang, Jin Kim, and Hojun Choi — a team of former Goldman Sachs analysts and MIT computer science PhDs — LinqAlpha says its multi-agent platform is already used by more than 70 financial institutions across the US, Europe, and Asia, including buy-side clients such as Causeway Capital Management and Schonfeld Strategic Advisors, collectively managing over US$5 trillion in assets.

The company positions itself against both entrenched incumbents like Bloomberg and LSEG, and a crowded field of AI challengers such as AlphaSense, Hebbia, and Rogo, betting that persistent, firm-specific reasoning — not just faster search — is where the real edge lies.

Also Read: LinqAlpha raises US$22M to bring agentic AI to public-market investors

We spoke with Jin Kim, co-founder and Head of Forward Deployed Engineering about the fundraise, the company’s Asia strategy, and how LinqAlpha plans to compete.

Edited excerpts:

Your US$22M raise features an Asia-heavy syndicate — SBI, Mirae Asset, Samsung Securities, East Ventures. Deliberate strategy or following traction?

Both. Roughly half our revenue comes from Asia Pacific, so the capital base mirrors the client base. But the syndicate was deliberately built: in institutional finance, investors are also distribution. Firms like SBI, Mirae Asset, and Samsung Securities are operating institutions in the markets we serve; that alignment shortens trust-building cycles that normally take years. We didn’t raise Asian capital to enter Asia later; global coverage, Asian languages, and multi-asset support were in the design from Day One.

You’re headquartered in New York, yet clients and capital skew Asia. When does it make sense to shift your center of gravity to Singapore or Tokyo?

We, at LinqAlpha, run a distributed model rather than one centre: New York for business development, Seoul as our product/engineering hub, subsidiaries in Hong Kong and Singapore, with London next. Singapore is where we’ve made the on-the-ground commitment, a dedicated local team, not a sales outpost, but people who co-design deployments with regional institutions. The timing isn’t accidental: MAS just launched the Future of Finance Institute to move AI in financial services from experimentation to deployment. That deployment gap is our entire business.

You count 70+ financial institutions as clients, but client count can be a vanity metric. How embedded is LinqAlpha in daily workflows, and how do you measure it?

We agree it’s vanity, which is why we manage for depth across three layers:

1. Daily-workflow usage: morning briefings, alerts, meeting-prep agents that fire before the user’s day starts, not ad-hoc Q&A
2. Expansion within accounts: trials converting to multi-seat, multi-team deployments
3. Integration depth: clients moving from app to API access, building our agents into their own systems

The pattern we watch: users going from reading our output to relying on it—starting with a briefing, pushing results to PMs, then asking us to build custom trackers for signals nobody else covers, often in Asian-language sources their other tools can’t read.

Hebbia, AlphaSense, Rogo, Dataminr — plus Bloomberg and LSEG embedding AI into terminals. Why should a CIO choose LinqAlpha over waiting for incumbents to catch up?

We’re solving different problems. Bloomberg and LSEG are indispensable data infrastructure; AlphaSense built a strong content library with AI on top. But a platform selling the same content to everyone will, by design, give every subscriber the same AI answer from the same corpus.

What a CIO competes on is the firm’s own frameworks, thesis history, and internal research. We encode that — per firm, permissioned, isolated — in what we call a second brain. Ask two funds “what are the top AI trades today” and a generic tool names the same mega-caps for both. Our platform answers in the context of each firm’s own mandate. We sit on top of data clients already license and reason across asset classes — equities, macro, credit, FX –in one connected system rather than siloed products.

AI hallucinations can directly influence capital allocation. What safeguards ensure accuracy and auditability, and has a failure ever cost a client?

We designed the platform assuming models are fallible, so safeguards are architectural. Every claim is grounded in licensed, vetted data with a citation back to source, auditable in one click. Numbers are computed deterministically through code, not generated by an LLM. We run multiple frontier models and neutralise their individual biases; our research on systematic sector/style biases in base models was accepted at ICLR and presented at a BlackRock quant conference.

Also Read: In the age of AI, people matter more than ever

We call this discipline harness engineering: as models get more powerful, value shifts to controlling them — permissioning, audit trails, human-in-the-loop checkpoints built for regulated finance. On failures: we have not had an incident where a platform error drove a capital loss for a client.

Clients feed you proprietary research and conviction signals. How do you handle data security?

Isolation isn’t a feature; it’s the product’s precondition. Each firm’s knowledge layer is siloed: notes, theses, and research never train shared models and never cross accounts. One client’s second brain is architecturally invisible to every other client’s.

For institutions wanting an even tighter boundary, our API and MCP deployment patterns let agents operate inside the client’s own environment, so sensitive context need not leave their perimeter. The incentive structure matters too: our business model is per-firm value, not data aggregation. Security reviews and regulatory addenda with global banks are table stakes, and we treat them as part of the product.

Southeast Asia is fragmented — multilingual, multi-regulatory, inconsistent data. How does the platform handle Bahasa Indonesia filings, Thai regulatory announcements, and Vietnamese commodity flows simultaneously?

That fragmentation is the inefficiency we were founded to arbitrage. The platform analyzes 20,000+ companies across 80+ markets in 20 languages, reading local-language primary sources natively rather than waiting for English translations that arrive late or never.

In Asia, that’s where alpha lives: information that’s public but not yet priced because it sits behind a language barrier. Clients already track signals in Chinese-, Korean-, and Japanese-language sources that English-first platforms structurally miss; the same architecture extends across Southeast Asia. Equally important is multi-asset design: an Indonesian commodity signal reads through to Singapore-listed equities, regional FX, and credit in one connected graph. Where coverage needs deepening, we build it hand-in-hand with regional clients—that’s the global best practice we’re bringing to Singapore, not a US product with a Singapore price list.

With a Berkeley MFE/Goldman/MIT pedigree, doors open easily, but institutional adoption is slow. What’s been the biggest obstacle converting pilots to long-term contracts?

Never model quality in a demo. The real obstacle is earning a place in daily workflow at a conservative institution—what kills pilots industry-wide is a tool that impresses in week one and is forgotten by week six. We treat every trial as an implementation, instrumenting adoption user by user and workflow by workflow.

The second obstacle is institutional trust: security review, compliance sign-off, data governance. We stopped treating that as friction and started treating it as the sale, because the risk owner is usually the real buyer. What converts pilots is co-designing an AI roadmap with client leadership over the next one to two years, rather than selling seats.

Buy-side clients manage US$5 trillion+ in assets, striking for a Series A. What’s your pricing model? Recurring SaaS, or a services-heavy business that doesn’t scale?

It’s recurring software by design: seat- and entitlement-based subscription with enterprise tiers for API access and advanced modules. The same platform serves a hedge fund pod and a bank’s research floor. The insight most people miss: the “bespoke” part—learning each firm’s framework—is performed by the system itself. The second brain is built by agents from the client’s own permissioned data, not consultants billing hours. That makes personalization compound instead of costing more. The AUM figure is a statement about who trusts us, not a revenue multiplier—but reference clients at that tier are the moat, because institutional buyers follow institutional proof.

If AI “changes what analysts can know,” doesn’t wide adoption commoditise the very edge you promise?

That critique is fatal for generic AI. This is exactly why we built the opposite. If every investor used the same model on the same data, the edge would be arbitraged away in a quarter. Our architecture inverts it: agents reason in the context of each firm’s own thesis history and mandate, so two funds asking the identical question get different, both correct, answers—the platform amplifies different brains. Adoption doesn’t converge outputs; it compounds each firm’s accumulated judgment.

Also Read: Is generative AI the game-changer for productivity?

Think of Bloomberg in the 1990s: everyone had the terminal, yet returns diverged wildly, because the edge was never the tool; it was what each firm did with it. We’ve made “what each firm does with it” the product itself. AI is shifting the scarce resource from information access to quality of questions and speed of connecting dots. The real risk for a CIO isn’t adopting AI too early; it’s letting a competitor’s second brain start compounding a year before yours does.

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The rapid adoption and expansion of artificial intelligence is reshaping global infrastructure demand. From hyperscale data centres to advanced semiconductor fabrication facilities, AI-led growth is driving an unprecedented need for mission-critical assets across Asia Pacific (APAC).

APAC’s power infrastructure is struggling to keep pace. These facilities are highly power-intensive, requiring uninterrupted, high-reliability energy to maintain operations and meet stringent uptime requirements. Mature markets in APAC face grid saturation and limited space for new generation, while emerging markets often contend with reliability issues, transmission bottlenecks or regulatory complexity. The scale and speed of digital and industrial development now risk outstripping expansion in generation, transmission, and distribution capacity.

The next phase of infrastructure growth in APAC will be defined not just by demand, but by how effectively developers plan for and secure power. In an environment of tightening supply, rising costs and increasing volatility, diversification and early-stage power strategy are becoming critical determinants of project success.

The power squeeze

Recent geopolitical developments, such as the conflict in the Middle East, have further amplified these constraints. Linesight’s analysis shows that the impact on APAC is particularly acute. The Strait of Hormuz remains one of the world’s most critical energy chokepoints. Around 25 per cent of global seaborne oil trade and about 20 per cent of global LNG trade move through the Strait, with around 80 per cent of oil flows destined for Asia. Many APAC economies rely on imported oil and LNG, increasing their exposure to supply shocks and cost escalation.

At the same time, the power demands of mission-critical facilities continue to climb. Across markets such as Singapore, Malaysia and Japan, data centre pipelines are expanding rapidly, while governments are actively onshoring semiconductor capacity to strengthen supply chain resilience. Singapore’s Green Data Centre Roadmap, for example, aims to provide at least 200MW of additional near-term capacity (in its second data centre call for applications), while Malaysia’s data centre load could exceed 5,000MW by 2035. This convergence is placing unprecedented pressure on already constrained power systems.

In several jurisdictions, power connection timelines now exceed typical construction programmes, creating a material risk to project viability. With competition for limited power capacity intensifying against a backdrop of continued geopolitical uncertainty, the construction sector is confronting an unforgiving reality: developments that fail to secure resilient and diversified power strategies early risk delay, de-scoping or obsolescence. Power availability should be treated as an early-stage project risk, not a late-stage utility consideration.

Also Read: The AI-quantum collision: Navigating the 2026 infrastructure inflection point

The case for diversified power strategies

In this environment, power diversification is no longer just a strategic consideration; it is becoming as important as land, labour and capital. Reliance on a single fuel source or grid connection exposes projects to volatility, while diversified power strategies provide optionality and stability when systems are under stress.

Across APAC, developers are increasingly integrating alternative energy sources to build a more resilient energy mix and buffer against supply constraints or price shocks. On-site solar generation, battery energy storage systems, microgrids, waste-to-energy solutions and long-term power purchase agreements are being deployed to stabilise supply and manage operating costs. These are generally hybrid solutions, where the combination of grid power, renewables and backup generation enables developments to proceed where grid capacity alone would have been insufficient, whilst maintaining uptime during periods of heightened disruption.

Lower-carbon power as a key part of the diversification playbook

Beyond diversification itself, a critical consideration is the type of power integrated into these strategies, especially given APAC’s decarbonisation ambitions. Governments have announced capacity targets supported by policy reform and investment incentives. Singapore is targeting at least 2GWp of solar deployment by 2030 and, by 2030, expects at least nine hydrogen-compatible gas-fired power plants as part of its longer-term low-carbon transition. Japan’s latest energy policy points to renewables reaching 40 to 50 per cent of its power generation mix by FY2040, supported by targets including 30 to 45GW of offshore wind by 2040.

Diversified and lower-carbon power strategies are no longer just environmentally desirable; they are commercially compelling. Beyond capacity, rising costs associated with carbon-intensive energy are accelerating the case for transition. Carbon pricing mechanisms and reduced subsidies are pushing up long-term costs, while incentive schemes and concessional financing are steadily improving the economics of renewables.

For developers, this shift opens a broader alternative playbook. Distributed energy resources, such as rooftop solar and behind-the-meter storage, can reduce grid dependence and accelerate time to power. Corporate power purchase agreements can provide long-term price certainty while supporting new renewable projects. In select markets, private microgrids and dedicated generation assets are emerging as viable solutions for energy-intensive developments. Diversification into lower-carbon energy not only supports customer commitments around uptime, emissions reduction and supply chain transparency, but also strengthens their ability to attract increasingly environmentally minded occupiers and investors, while reducing exposure to future ESG-related regulatory costs.

Also Read: The rise of AI twins: From assistant to infrastructure

However, these alternatives require early integration into project planning. Site selection, land availability, grid adjacency and permitting regimes all influence the feasibility of diversified power solutions. Successful delivery increasingly depends on aligning technical, commercial and regulatory considerations from the earliest project phases. Optimal solutions will also vary by market. Mature, land-constrained locations may prioritise imported renewable energy, off-site PPAs and energy efficiency, while emerging industrial hubs may have more scope for on-site or near-site generation and private power infrastructure.

What will define winners in APAC’s next infrastructure boom

Power diversification is no longer an environmental or ethical choice alone; it is an operational necessity for mission-critical infrastructure across APAC. In a region defined by rapid growth, constrained grids and geopolitical uncertainty, diversified power strategies provide stability, resilience and confidence.

Assets that integrate multiple power sources benefit from more predictable uptime, stronger investor appeal and enhanced long-term value. To fully realise these advantages, diversification must be embedded from the outset, influencing site selection, design development and capital strategy.

The winners in APAC’s next phase of infrastructure growth will be those that recognise power as a strategic asset, planned early, costed accurately, managed actively and diversified intelligently.

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Product teams are trained to ask familiar questions. Who is the user? What problem are we solving? How often does it occur? How painful is it? What is the willingness to pay? Those questions matter, but there is another one that quietly shapes adoption far more than many teams admit.

When does this problem actually become real in the customer’s world?

That is a different question from frequency. It is not asking whether a problem exists in principle. It is asking when the problem becomes urgent enough, visible enough, or costly enough for a feature to earn attention, budget, workflow change, and repeat use.

Many features fail not because they are weak, but because they are mistimed. They arrive outside the window where the customer is ready to care. Then the team misreads the result. It concludes the feature lacked value when the deeper issue was that the value did not line up with the customer’s calendar.

Most feature adoption is seasonal in ways teams do not name

When people hear seasonality, they usually think of obvious consumer patterns. Retail peaks in holidays. Travel surges in summer. Fitness spikes in January. Those are real, but they are only the visible end of the idea.

In practice, many products live inside less obvious seasons.

Enterprise products have planning seasons, budgeting seasons, procurement seasons, audit seasons, renewal seasons, hiring seasons, transformation seasons, and risk seasons. Internal tools live through quarter-end pressure, annual planning rituals, compliance reviews, and leadership changes. Even collaboration features can behave seasonally because teams communicate differently during launches, restructures, onboarding waves, or periods of cost control.

The feature itself may not change. The customer’s readiness to adopt it does.

Also Read: Product DNA testing: How features inherit traits from parent products

Some features are not evergreen, and that is fine

One of the unhelpful biases in product thinking is the assumption that the best features behave like evergreen assets. They should show steady demand, broad applicability, and consistent usage. That expectation sounds rational, but it can distort judgement.

Some features are not meant to be used evenly. Their value comes from intensity, not constancy.

A planning tool may matter enormously during one month and sit nearly dormant during others. A compliance workflow may become critical during review periods and almost invisible in quieter quarters. A feature that supports hiring, onboarding, migration, or renewal may deliver huge value in concentrated windows rather than through daily engagement.

That does not make the feature weak. It makes it cyclical.

The real mistake is evaluating cyclical value through non-cyclical expectations. Teams look at monthly usage and panic because the graph is uneven. They ask whether the feature is sticky enough, when the better question is whether it becomes indispensable at the exact moment it should.

This is where product maturity shows. 

The market has calendars, even when your roadmap ignores them

Most roadmaps are built around internal logic. Engineering capacity, strategic themes, executive priorities, dependencies, and quarterly planning all shape what gets released when. That is understandable, but it often means the product launches according to the company’s calendar rather than the customer’s.

This is one of the least discussed reasons good features underperform.

A team may launch a budgeting capability in the quarter after customers set budgets. It may introduce governance controls after the compliance window has passed. It may ship a staffing feature after hiring freezes begin. It may release operational tooling during the busiest commercial period, when nobody has the attention to absorb process change, no matter how sensible the new workflow looks in a demo.

The product team then spends weeks trying to work out what went wrong in the positioning, onboarding, or interface. Sometimes the answer is far simpler. The feature reached the market at a time when the customer had no spare bandwidth, no urgent reason to switch, or no practical ability to act.

Also Read: The problem with ‘PM as CEO of the Product’: A myth that hurts more than helps

A badly timed launch can produce false negatives

This is one of the more expensive product mistakes because it leads to the wrong learning.

A feature launches. Adoption is weak. Leadership loses confidence. The team trims investment, shifts attention, or decides the market is not ready. In some cases, that judgement is right. In many others, it is premature.

The problem is that timing failures often masquerade as product failures.

If a capability is introduced outside the season in which customers are willing to act, the team collects weak signals. Low usage, slow setup, muted excitement, limited word of mouth. Those signals look like poor product-market fit, but they may actually reflect poor temporal fit.

This matters because the remedy is different. A weak product needs redesign. A mistimed product may need reintroduction, better sequencing, stronger preparation, or a different commercial motion around the same underlying capability.

The danger is that teams abandon the right idea after reading the wrong evidence.

Features have windows of activation, not just user segments

Most product strategy frameworks focus on segmentation by user type, company size, industry, geography, or maturity. Those still matter, but they are not enough. Features also need to be segmented by time.

A more serious product question is not only who this feature is for, but when it is most likely to activate.

That changes how you think about rollout, education, pricing, and success measurement. If a feature only matters in planning season, then awareness needs to exist before planning season, not during or after it. If the capability becomes crucial during audits, then setup and training need to happen in the quieter period before the audit window opens. If a workflow matters only at renewal, then the product cannot wait until the renewal moment to explain its value.

In other words, teams need to design for the activation window, not just the feature itself.

This is where many companies underinvest. They build the capability and assume timing will sort itself out. It rarely does. Time needs orchestration in the same way as functionality.

Also Read: The systemic minimum effective dose: Redesigning productivity through precision

The strongest features often prepare long before they are used

This is another subtle but important point. A feature’s moment of highest value is not always the same as its moment of highest preparation.

Take any capability that supports a critical but infrequent workflow. The actual use may happen in a compressed, high-stakes period, but the product work that enables successful adoption has to begin much earlier. Permissions have to be configured. Data needs to be clean. Users need to understand why the feature exists. Teams need to trust that it will hold up when the important moment arrives.

That means some of the most important product work sits before the season, not inside it.

Weak product teams focus only on the event. Stronger ones think about readiness. They understand that the feature is not being adopted in the same moment it is being used. It is being adopted in the months when the customer decides whether to rely on it later.

That distinction is hugely important for enterprise products, operational tools, financial workflows, and anything that carries risk. Customers do not place trust instantly on the day of urgency. They decide beforehand what they are willing to trust when urgency arrives.

—

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

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Singapore-based SimpleAI has secured a US$10 million debt facility to acquire accounting and fund administration firms across Asia Pacific, as the startup shifts from selling automation software alone to owning the service businesses where that software can be deployed.

The company has also announced a US$5 million seed round, with a lead investor already committed, following an earlier US$500,000 pre-seed investment from strategic backers.

Also Read: The future of numbers: Automation’s transformative impact on accounting jobs

SimpleAI did not disclose the names of the investors or the terms of the debt facility.

Founded in 2023 by Roger Tan, Shim Youngjun, and Bryan Sng, SimpleAI builds automation agents for accounting and finance teams. Its software reads ledgers, charts of accounts, and existing workflows, then proposes accounting actions while a deterministic module handles calculations. Accountants can review and approve entries before they are posted.

The funding marks a change in strategy. Rather than relying solely on organic software adoption, SimpleAI now wants to acquire established accounting and fund administration firms, keep their client relationships intact, and introduce AI into their workflows.

A roll-up model for professional services

SimpleAI is targeting firms with annual revenues between US$500,000 and US$5 million, although it said it may consider larger transactions above US$20 million. Its immediate focus is Singapore and Australia, with Hong Kong and Mauritius also under consideration. The company said it has more than US$25 million worth of potential deals under review across Singapore and Australia.

Acquired companies will operate under what SimpleAI calls a Partner-and-Operator model. In practice, this means the acquired firms continue serving existing clients while SimpleAI provides technology and operational support. The company said it will assess acquisition targets based on unit economics, cultural fit, and whether its AI agents can be embedded into the firm’s workflows.

That approach reflects a wider trend in vertical software and professional services, where startups are increasingly trying to control both the software layer and the operating business. The model has already been tested in fragmented sectors such as dental clinics, legal services, bookkeeping, and insurance distribution.

In accounting, the thesis is straightforward: many smaller firms have recurring clients, predictable revenues, and labour-intensive processes, but lack the capital or technical capacity to automate quickly.

For SimpleAI, acquisitions could offer a faster route to distribution than selling software firm by firm. The risk is that running services businesses is operationally heavier than selling software, especially across jurisdictions with different tax, compliance, and reporting requirements.

Why Southeast Asia matters

The Southeast Asian angle is central to the story. Singapore has pushed aggressively to digitise financial infrastructure through initiatives such as InvoiceNow, the nationwide e-invoicing network based on the Peppol framework. SimpleAI has partnered with the Singapore Business Federation and SESAMi in support of the Infocomm Media Development Authority’s InvoiceNow initiative, extending its automation agents to help small and medium-sized enterprises adopt e-invoicing.

This matters because accounting automation depends on the quality and structure of financial data. E-invoicing reduces manual entry, improves audit trails, and gives software platforms cleaner transaction data to process. Singapore has been ahead of much of the region on this front, while markets such as Malaysia, Indonesia, Vietnam, and Thailand are also moving towards more formal digital tax and invoicing systems.

Also Read: How Transparently.AI uses Artificial Intelligence to detect accounting manipulation, fraud

Across the region, the broader digital financial services market has continued to expand even as venture funding has tightened. The Google, Temasek, and Bain e-Conomy SEA report estimated that digital financial services revenue in the region could reach around US$60 billion by 2025, driven by payments, lending, insurance, and wealth products. While accounting automation is a smaller segment, it sits underneath many of these activities, particularly for SMEs, funds, and corporate service providers.

The opportunity is also shaped by a funding environment that has become more disciplined. After the 2021 peak, Southeast Asian startup funding fell sharply, forcing companies to show clearer paths to revenue and profitability. In that context, SimpleAI’s acquisition-led strategy is notable: it is using debt to buy revenue-generating firms rather than relying only on venture-backed software growth.

AI in accounting is crowded but still early

SimpleAI is entering a competitive market. Global accounting software incumbents such as Xero, Intuit QuickBooks, Sage, and Oracle NetSuite have been adding AI and automation features to their platforms. In Southeast Asia, SMEs often rely on a mix of cloud accounting tools, outsourced bookkeepers, corporate secretarial firms, and local tax software providers.

The fund administration market is also competitive, with global players such as Vistra, Apex Group, TMF Group, and Tricor serving private funds, special purpose vehicles, and corporate clients across Asia Pacific. SimpleAI’s appointment of Otto Von Domingo as Chief Revenue Officer points to this segment as a priority. Domingo has more than 20 years of experience in private markets and corporate services and previously helped grow Vistra’s funds business in Singapore and Asia-Pacific.

SimpleAI said its platform is deployed across more than 10 markets, supporting more than 1,500 entities and over 2,000 users across more than 18 industries. It is also an app partner of Xero and Intuit QuickBooks, and says it is ISO-certified.

Bryan Sng, co-founder and Chief Operating Officer of SimpleAI, said the company was formed after the founders saw how much accounting work still depended on repeated manual reviews and corrections.

“What struck us was how painful that simple need actually was,” he said. “The endless loop of sending a report, catching an error, amending it, reviewing again, and how familiar the excuses had become for late submissions.”

The execution question

SimpleAI’s ambition is not modest. The company said it plans to strengthen its presence in Singapore and Australia, expand into Hong Kong, Mauritius, and Europe, and consider a public-market exit, including a possible IPO, within 24 months.

That timeline will invite scrutiny. Roll-up strategies can look compelling on paper but often depend on disciplined acquisition pricing, smooth integration, staff retention, and consistent service quality. In professional services, client trust and regulatory accuracy matter as much as automation.

Roger Tan, founder and CEO of SimpleAI, said the company would remain selective. “As an AI-native company, our M&A facility lets us acquire businesses where we can deploy our agents into the workflow immediately,” he said. “We are being disciplined and will only move forward when the economics, talent, and cultural fit are right.”

Also Read: Why the future of AI automation belongs to builders who ship

For now, SimpleAI’s bet is that accounting and fund administration firms will not be replaced by AI so much as reshaped by it. If the company can combine automation with acquired distribution, it may build a defensible services platform. If integration proves harder than expected, it will face the same problem as many roll-ups before it: buying revenue is easier than improving the business behind it.

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Sprouts.ai, a Palo Alto-based startup building AI agents for enterprise revenue teams, has raised US$9 million in pre-Series A funding, as investors continue to back software companies trying to automate parts of the B2B sales and marketing stack.

The round was co-led by True Global Ventures and Accel, with participation from Kickstart Ventures, the corporate VC arm associated with the Philippines’ Ayala group. The new round brings Sprouts.ai’s total funding to US$14 million.

Also Read: AI agents are already inside your systems, but who’s controlling them?

Founded in 2023 by Karan Chaudhry, Kapil Chaudhry, and Avinash Nagla, Sprouts.ai is building what it calls a Deep AI GTM Engine. The platform combines customer intelligence, account data, buyer committee mapping, relationship networks, product heatmaps, complex search, and AI-powered workflows to help enterprises identify, engage, and convert target customers.

The company says the funding will go towards improving its AI agent capabilities, deepening enterprise integrations, and expanding its platform.

A bet on AI-native sales infrastructure

Sprouts.ai is entering a crowded but active category: B2B revenue technology. For years, enterprise sales and marketing teams have stitched together customer relationship management systems, enrichment databases, sequencing tools, analytics dashboards, marketing automation software, and intent-data products. The result is often expensive, fragmented, and heavily dependent on manual data cleaning.

“The B2B revenue stack is broken. Sales and marketing teams operate across more than 20 tools, work off dirty data, and bolt AI on top of infrastructure that was never built for it,” said Karan Chaudhry, Co-founder and CEO of Sprouts.ai. “We built Sprouts.ai to replace that fragmentation with a unified data and agent layer that actually moves the pipeline.”

The pitch is timely. Enterprises are under pressure to show practical returns from generative AI after two years of experimentation. Revenue operations is one of the obvious targets: sales teams generate large volumes of structured and unstructured data, but much of it sits across CRM systems, email, call transcripts, spreadsheets, and third-party tools.

Sprouts.ai says its software connects with enterprise systems such as Salesforce and Microsoft Dynamics, as well as large language models including Claude. It aims to help teams move from prospecting and account research to workflow execution inside the systems they already use.

The company claims customers using its platform have reported a threefold increase in ideal customer profile-qualified leads, a 25 per cent lift in sales qualified leads, a threefold improvement in response rates, and a 35 per cent reduction in GTM tooling costs. These figures are company-provided and have not been independently verified.

Also Read: When AI agents start acting on our behalf, security gets more complicated

Its customers include Razorpay, Hewlett Packard, HighRadius, and Udemy.

The opportunities in Southeast Asia

Although Sprouts.ai is headquartered in Palo Alto, the Southeast Asian angle is not incidental. Kickstarts’s participation gives the company a regional investor with links to one of the Philippines’s largest conglomerates, and the problem Sprouts.ai is trying to solve is visible across the region.

Southeast Asia’s enterprises operate in fragmented markets with different languages, regulations, buyer behaviours, and levels of digital maturity. A regional B2B sales team may need to map accounts across Singapore, Indonesia, the Philippines, Vietnam, Thailand, and Malaysia, each with uneven public company data, inconsistent job-title structures, and different procurement norms.

This makes generic global go-to-market databases less useful than they appear on a slide. Many international sales intelligence tools have stronger coverage in North America and Europe than in Southeast Asia, where company registries, SME data, buyer contacts, and intent signals can be patchier.

Data readiness is also a broader barrier to AI adoption. Cisco’s 2024 AI Readiness Index found that only 13 per cent of organisations globally were fully ready to capture AI’s potential, with data infrastructure and governance among the main constraints. For Southeast Asian enterprises, those gaps are often compounded by legacy systems, business-unit silos, and markets where offline relationships still shape B2B sales.

That is the opening Sprouts.ai is targeting: not simply another sales tool, but an intelligence layer that can make AI agents useful because the underlying account and buyer data is cleaner.

“We’re entering an age where the businesses that win will be the ones who truly understand who their customers are,” said Joan Yao, General Partner at Kickstart Ventures. “As AI agents take on more of the work of finding, understanding, and engaging the right customers, that data advantage is what will set Sprouts.ai apart.”

Competition is already intense

Sprouts.ai will not have the market to itself. The B2B sales intelligence and revenue operations category includes established global players such as ZoomInfo, 6sense, Demandbase, Apollo.io, Lusha, Cognism, and Clearbit, now part of HubSpot. Clay has also gained attention among growth teams for combining data enrichment, prospecting workflows, and AI-assisted outbound execution.

Also Read: Adapting to the new B2B sales landscape: AI and beyond

Large enterprise software vendors are also moving down the same path. Salesforce, Microsoft, HubSpot, and Adobe are embedding AI assistants and automation into their revenue clouds and marketing suites. That creates a difficult strategic question for startups: can they build a defensible intelligence layer, or will incumbents absorb similar capabilities into existing enterprise contracts?

Sprouts.ai’s answer appears to be data depth and agentic execution. Instead of selling only a database or workflow tool, the company is positioning itself as a unified GTM intelligence layer that sits across the full funnel, from ideal customer profile definition to closed-won deals.

The approach could appeal to enterprises that are already paying for multiple revenue tools and now face pressure to rationalise software spending. It could also resonate in markets such as Southeast Asia, where companies want AI adoption but may not have the internal data quality required to deploy autonomous workflows reliably.

Funding follows a broader AI shift

The round also reflects a broader shift in venture capital. Investors are no longer backing generative AI only at the foundation-model layer. Capital is flowing into applied AI companies that target specific enterprise functions, including customer support, software development, legal operations, finance, HR, and sales.

For this part of the world, this is particularly relevant. The region is unlikely to produce many companies competing directly with OpenAI, Anthropic, Google DeepMind, or xAI at the infrastructure layer. But applied AI companies that solve local enterprise pain points may have a clearer path to adoption.

Google, Temasek, and Bain have estimated Southeast Asia’s internet economy at hundreds of billions of US dollars in gross merchandise value, but enterprise software adoption remains uneven across markets. That leaves room for vertical and workflow-specific AI products, particularly in areas where local data, integrations, and compliance requirements matter.

Sprouts.ai’s challenge will be to prove that its platform can deliver measurable revenue outcomes beyond early customer claims. Enterprise sales software is a category full of tools that promise better leads and cleaner workflows. Buyers will want evidence that AI agents can improve pipeline generation without creating compliance risks, inaccurate outreach, or another layer of software complexity.

Also Read: AI lead generation for B2B sales: A practical guide

For now, the company has secured credible investors and a problem large enough to justify attention. The harder part begins after the funding: showing that AI-native revenue operations can move from boardroom talking point to repeatable enterprise deployment, including in messy, multilingual, and data-fragmented markets such as Southeast Asia.

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Talk to anyone in sales, marketing, or ops long enough, and the CRM conversation runs in a loop.

First, it’s “why are we paying this much for a system that basically does nothing?” Then the company adopts a new platform, and for a quarter or two there’s relief: the pipeline is clean, reports actually load, the rep dashboard is in one place.

Then come the integrations, the custom fields, the dashboards nobody asked for. A year later: “Why are we paying this much for a system that basically does nothing?”

The cycle keeps repeating because the conversation about CRM has been stuck in the wrong place. CRM, when it actually works, is one of the most useful pieces of software a company can buy. It pulls a sales team, a marketing team, customer success, and finance into the same set of facts about the same set of customers.

When it doesn’t work, it becomes the most expensive spreadsheet a company has ever owned.

The misconceptions that keep teams stuck

A few ideas about CRM survive longer than they should.

CRM is only for big corporations

Salesforce’s enterprise pricing set this expectation a decade ago, and it stuck even as smaller, more flexible products moved the floor.

A five-person team selling a US$40,000 product has every reason to track pipeline the way an enterprise team does. The math just runs differently. The era of paying enterprise prices for a CRM that barely does anything for you is over.

CRM is just a fancy contact database

This one is fading, but not fast enough. To some, CRM is still just a fancy contact database, a place where you keep all your connections stored but never actually take action on the leads you have.

Here’s where the difference shows up:

A contact list answers “who do we know?” A CRM answers “what should happen next, and who should do it?”

A contact list might only give you a name, a phone number, and an email. A CRM tells you what to do with that phone number, what emails to send, when to follow up, and how.

CRM is only for the sales team

Sales is where most companies start. But the record of who a customer is, what they bought, when they last talked to someone, and what they need next is also the foundation that marketing campaigns, support tickets, renewal forecasts, and finance reconciliation sit on top of.

Treating CRM as a sales-only tool turns every other team into a guest in someone else’s house. CRM systems are most helpful when they’re used across departments, all at once. Your marketing team logs prospects and assigns tasks, checking off requirements. Your sales team reaches out with outbound emails, runs cold calls or cold messaging sequences, and follows up on each action. Your legal team chimes in to check contracts and documents, calculate efforts, and break down costs.

And the list goes on.

Also Read: The problem with ‘PM as CEO of the Product’: A myth that hurts more than helps

Where most CRMs actually break down

Spend a few minutes on r/CRM, and the same complaints come up across companies, industries, and team sizes.

One recent thread on optimisation is a useful catalogue. The patterns, however, are remarkably consistent.

Data clutter and overcomplication

Most CRMs try to do everything, and the result is a homepage with seventeen widgets, a contact record with forty fields, and a sales rep who logs activity into one or two of them and ignores the rest.

And we haven’t even mentioned duplicate data. Duplicate records stored in your CRM lead to confusion, and they can seriously mess with your sales process. In fact, 15 per cent to 25 per cent of the data in a CRM is often duplicated. On top of that, 40 per cent of sales reps say they lack the data needed to effectively target leads.

The system optimises for completeness. The team optimises for getting through the day.

Too many clicks, too little flow

Logging a call should only take ten seconds.

For some reason, in most CRM platforms, it takes four clicks, two dropdowns, a free-text field, and a save button that occasionally fails silently. Multiply that across every rep and every interaction in a week, and you can predict where the data will be in six months: incomplete and quietly distrusted by everyone who relies on it.

Your CRM should work for you, in the most optimised way, on your own timeline. Spending real effort on what should be a simple, mundane task is not the goal of a CRM at all.

Expensive for what you actually use

CRM pricing has crept up faster than CRM functionality for years, partly because vendors keep moving features into higher tiers, and partly because the integrations and add-ons that make a platform usable get priced separately. Teams pay for the base seat, then again for analytics, then again for marketing automation, then again for the enrichment plugin that cleans up the data they were paying to enter.

So how much are we actually spending on CRM? It’s a question with no clean answer, because the add-on fees and extra tool charges keep piling on.

Choosing a CRM that holds up

A CRM you’ll still respect a year from now isn’t picked by a feature checklist. Features can always be upgraded, replaced, or even downgraded. What matters is what the CRM actually does for you, the customer journey it supports end-to-end, and whether it holds up in the long run beyond the flashy features.

Pick AI-native, not AI-bolted-on

A newer generation of CRMs has been built around the assumption that enrichment, summarisation, and follow-up drafting are part of the platform, not bolt-ons. That changes what a sales rep does in a typical hour. Less typing and less hunting for context, more time on the conversation. If a CRM still treats AI as a marketing slogan rather than a workflow primitive, the gap between it and the AI-native category will widen every quarter.

A real free trial, not some “14-day free demo”

Most free trials show a polished demo path and lock the rest behind a sales call. Ask for the full surface area before you sign. A platform that won’t let you stress-test it is telling you something about how confident the team is in the product outside of guided tours.

Check out everything the CRM has to offer. The question isn’t “can this CRM send a follow-up email?” The better one is “how many native integrations does this have to the systems my team already uses, and can I configure them without an admin certification?” Heavy manual overhead is usually a sign that the integrations were an afterthought.

Also Read: The systemic minimum effective dose: Redesigning productivity through precision

Audit the data, weekly or bi-weekly

CRM data decays. People change jobs, companies rebrand, deals stall and never get closed or lost properly. A short-standing review keeps the system trustworthy. A platform that surfaces stale records on its own, without a manual report, removes the meeting altogether.

Usability decides whether the rollout works

This is the unglamorous criterion and the one that quietly decides whether a CRM rollout works. If the interface is clunky, adoption stalls, and any feature on the brochure becomes irrelevant. The cleanest test is a five-minute walkthrough with a rep who didn’t pick the tool. Watch where they hesitate.

If you’re evaluating a CRM right now, the choice worth optimising for is AI-native and consolidated. Alano is one example of the category, designed so that a single workspace handles enrichment, outreach, and pipeline management without an integration layer between them. The point isn’t that any one product solves everything. It’s that consolidation has stopped being a “nice to have” and started being the difference between a CRM that earns its seat cost and one that doesn’t.

What CRM looks like next

The interesting shift in the next two years won’t be about features. It will be about who or what is doing the work inside the CRM.

The current model still assumes a person types most of what gets recorded and reads most of what gets reported. The next model assumes an agent is doing both. A call ends; the summary, the contact updates, the deal-stage change, and the follow-up draft are already in the record by the time the rep opens their laptop. Pipeline reviews stop being a weekly cleanup of bad data and start being a real conversation about strategy. Marketing stops asking sales for the latest contact list and starts triggering plays from the same source of truth.

That’s the version of CRM worth waiting for, and it’s the version a handful of platforms are quietly building toward right now. The companies that get the most out of it will be the ones that stop accepting friction as the cost of doing business and start asking, every quarter, the question this whole category should have been built around in the first place.

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Atome Philippines has secured a PHP 5 billion (~US$88 million) wholesale facility from Asia United Bank, giving the digital finance platform local-currency funding to expand its consumer credit business in one of Southeast Asia’s most underbanked large markets.

The facility will mainly support the Atome PayLater Anywhere Card, which the company says has now been issued to more than three million Filipinos.

Also Read: Atome lines up US$345M debt as Southeast Asia fintechs shun equity

According to Atome, up to 80 per cent of cardholders are first-time card users, while 65 per cent are women. Most use the card for recurring household spending, including groceries, food, household items, telecoms bills and utilities.

That usage profile matters. In the Philippines, credit access remains thin outside the traditional banking system, especially beyond Metro Manila. While digital payments have grown rapidly, formal credit penetration still lags behind demand, leaving fintech lenders, buy-now-pay-later operators and digital banks competing to serve consumers who are financially active but underserved by incumbent lenders.

Local funding for a local credit business

For Atome, the AUB facility adds a sizeable peso-denominated line to its funding base. That is more than a balance-sheet detail. Consumer lenders operating across Southeast Asia often face currency mismatch risks when they raise capital in US dollars but lend in local currency. A domestic funding line can help reduce that exposure, improve pricing discipline and support more predictable expansion.

Atome said adoption of its card has expanded beyond Metro Manila into Luzon, Visayas, and Mindanao. The company’s wider product portfolio in the Philippines includes lending, savings and insurance, positioning it less as a single-product BNPL provider and more as a digital finance platform targeting mass-market consumers.

“The closing of AUB’s PHP 5 billion facility validates Atome’s market position and delivers competitive, PHP-denominated funding at meaningful scale,” said Christian Quiros, President and Country Manager of Atome Philippines.

AUB framed the transaction as part of its support for fintech platforms operating within formal credit standards. “This partnership advances financial inclusion while maintaining rigorous credit standards,” said Ernesto Uy, Executive Vice President and Account Management Head at AUB.

Also Read: Atome defies market headwinds with 63 per cent income surge, US$4B GMV run rate

The phrasing is notable because BNPL and embedded credit players across the region have had to work harder to distinguish responsible lending from unchecked consumer credit growth. Regulators in markets such as Singapore, Indonesia and Malaysia have tightened scrutiny of lending disclosures, affordability checks and debt collection practices, even as they recognise that digital lenders can broaden access where banks have limited reach.

The Philippines remains a large inclusion opportunity

The Philippines has become one of Southeast Asia’s more active digital finance markets, driven by high smartphone usage, a young population, and persistent gaps in banking access. Bangko Sentral ng Pilipinas has reported strong growth in digital payments, with electronic transactions accounting for more than half of retail payment volumes in recent years. The central bank has also set financial inclusion as a core policy priority, particularly for women, micro-entrepreneurs and consumers outside major urban centres.

Still, access to credit remains uneven. Many Filipinos have digital wallets but limited access to formal revolving credit, cards or instalment products. That gap has created room for companies such as Atome, Billease, Home Credit Philippines, GCash-linked lending products, Maya, SeaMoney and other app-based lenders to build credit relationships with consumers who may not qualify for traditional bank cards.

The competitive field is crowded. Home Credit has long focused on point-of-sale consumer finance, particularly electronics and appliances. Billease has built a local BNPL and consumer lending business. GCash and Maya benefit from large wallet ecosystems and payments data. Regional players such as SeaMoney and Kredivo, meanwhile, have used e-commerce, payments and risk-scoring capabilities to push deeper into credit.

Atome’s card-led approach gives it a different route to consumer adoption. Instead of limiting usage to partner merchants or online checkouts, a PayLater card can become part of daily spending behaviour. That also raises the stakes on underwriting. Everyday-use credit products can scale quickly, but they need disciplined credit limits, repayment monitoring and collection practices if they are to avoid overextension among first-time borrowers.

BNPL evolves beyond checkout financing

Atome started as a BNPL platform but, like several players in the sector, has moved into a broader financial services model. That reflects the economics of the category. Pure BNPL margins can be pressured by merchant fees, funding costs, fraud risk and repayment behaviour. Platforms that can cross-sell lending, cards, savings or insurance may be better positioned to improve customer lifetime value, although they also face heavier regulatory and operational demands.

Across Southeast Asia, the BNPL sector has shifted from aggressive merchant acquisition to more disciplined credit growth. Rising interest rates over the past few years made wholesale funding more expensive, forcing lenders to pay closer attention to unit economics and asset quality. Investors have also become less tolerant of growth driven primarily by subsidies.

This is why the AUB facility is strategically useful for Atome. A large local bank facility suggests a degree of institutional confidence in its Philippine book, although the ultimate test will be portfolio performance as card usage expands beyond early adopters and urban customers.

Also Read: Atome secures US$75M facility to expand BNPL reach in Philippines

Atome is part of Singapore-headquartered Advance Intelligence Group, which is backed by investors including SoftBank Vision Fund 2, Warburg Pincus, Northstar and EDBI. The group operates across digital finance and risk technology, and Atome remains one of its more visible consumer brands in Southeast Asia.

For AUB, the deal gives it exposure to a fast-growing fintech credit channel without having to originate every end-borrower relationship directly. For Atome, it supplies domestic liquidity at scale in a market where demand for accessible credit is real, but where regulatory tolerance will depend on whether lenders can prove they are expanding access without encouraging unsustainable debt.

The Philippine opportunity is significant, but not uncontested. The next phase of growth will likely be defined less by card issuance numbers and more by repayment quality, customer retention and whether digital lenders can serve first-time borrowers without repeating the excesses seen in less regulated consumer credit markets.

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I want to tell you about a number that should make every climate founder in Southeast Asia angry.

US$37 billion.

That’s the combined JETP commitment across Indonesia and Vietnam alone. Indonesia signed for US$21.6 billion. Vietnam signed for US$15.5 billion. These aren’t projections or targets. These are commitments. Money that governments and international partners put their names on specifically to accelerate the climate transition in this region.

Now tell me how many founders you know who’ve seen a dollar of it.

I’ll wait.

The gap nobody is talking about

There’s a version of the SEA climate story that looks great on paper. Policy scaffolding going up. Carbon taxes rising. ASEAN sustainable finance taxonomy finally giving investors a common language. International capital showing interest. Conference panels full of optimistic people in linen shirts.

And then there’s the version on the ground.

Founders pitching VCs because they don’t know any other door exists. Climate ventures structured wrong for the instruments available. Development finance sitting in disbursement queues while startups run out of runway. A US$37 billion commitment slowly moving through bureaucratic channels while the companies that should be receiving it are busy preparing their fifteenth investor deck.

The money is not missing. The translation layer is.

Also Read: Funded: I keep a notebook by my bed with one question about SEA climate

Why the capital isn’t moving

JETP money doesn’t flow like VC money. It moves through governments, multilateral institutions, development banks, and implementing agencies before it ever gets close to a founder. Each layer has its own compliance requirements, reporting standards, and risk appetite. By the time it reaches the ground, it looks nothing like what a climate startup can actually absorb.

Development finance institutions want projects at a certain scale. Foundations want specific proof points. Grant programmes want reporting frameworks that most early-stage founders have never heard of. The instruments being offered and the ventures trying to receive them are speaking completely different languages.

This is not a criticism of the institutions. They’re doing exactly what they were designed to do. The problem is that nobody is sitting in the middle translating.

What the best climate funds understand

The funds that have stayed consistent in the SEA climate, and there are very few of them, understand one thing clearly. Commercial viability and emissions impact are not in conflict. The best climate companies create real economic value for their customers first. The impact follows from the business working, not the other way around.

That framing is what makes a climate venture legible to multiple capital sources simultaneously. A venture that creates genuine value can absorb VC, attract development finance, qualify for catalytic grants, and access JETP-linked programmes. But only if it’s structured correctly from the start.

Most aren’t. Not because the founders are wrong. Because nobody showed them the full map.

Also Read: Funded: AI is having its moment, climate is having a crisis. SEA can’t afford to confuse the two

The US$37 billion translation problem

Here’s what the translation layer actually looks like in practice.

A climate founder in Indonesia building in solid waste or energy efficiency has potential access to multiple capital sources. JETP-linked programmes for energy transition. Foundation capital for proof of concept. Development finance for scale. Equity for growth. Each instrument has a different entry point, different evidence requirements, different timeline.

A founder who sequences these correctly can build a genuinely well-capitalised company without giving away equity too early, without taking on the wrong kind of debt, and without spending two years pitching VCs who were never the right fit to begin with.

But the sequencing requires someone who knows all the rooms. Most founders only know one.

The real opportunity

US$37 billion committed to SEA climate is not a problem. It’s an infrastructure waiting for founders who know how to access it and intermediaries who know how to connect them.

The next wave of SEA climate companies won’t be built by founders who pitched their way to a VC term sheet. They’ll be built by founders who understood the full capital landscape, sequenced it intelligently, and used the right instrument at the right stage.

The money is already here. It has been for a while.

The question is who’s going to help founders find the door.

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The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

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