goldman’s rationale: vertical ai with measurable roi.
by 卡塔尔世界杯常规比赛时间
fieldguide, an engagement workflow platform powered by agentic ai, has raised $75 million in a series c round led by goldman sachs alternatives, valuing the company at $700 million.
more: jin chang: why repeat last year’s questions? | the disruptors, with liz farr | jin chang: why the future is in risk advisory | with donny shimamoto
more tech and assurance
the financing is one of the clearest signals yet that audit technology—long one of accounting’s most conservative corners—is becoming a prime target for growth equity investment.
the deal, joined by new investor geodesic capital and existing backers bessemer venture partners, 8vc, and thomson reuters, brings fieldguide’s total funding to roughly $125 million.
the message from wall street is clear: ai in audit is no longer a speculative side bet. it is priced based on capacity, productivity, and professional resilience.
for cpa firms, the financing is best understood not as a startup milestone, but as a case study in how the audit delivery model is being rebuilt—piece by piece—around workflow platforms, evidence automation, and agentic execution.
a profession facing an existential capacity problem
fieldguide’s founder and ceo, jin chang, has framed the company’s mission in blunt terms. audit is essential, but the labor model is breaking. in comments to fortune, chang described the cpa talent shortage as “existential,” pointing to a pipeline that has fallen to a 17-year low in exam candidates and a looming retirement wave that could remove as much as three-quarters of today’s cpas over the next decade.
the demand side, however, has not softened. public companies still require quarterly and annual assurance. private equity-backed rollups, regulated industries, and increasingly complex reporting regimes continue to expand the need for audit and advisory work. the result is a widening gap between what the profession is asked to deliver and the number of humans available to deliver it.
historically, firms have filled that gap through overtime, offshore staffing, and incremental workflow digitization. but those levers are reaching their limits. younger auditors cite burnout and monotony. offshore work introduces coordination friction. and many audit “technology stacks” remain collections of disconnected tools: document portals, spreadsheets, sampling software, review notes, and legacy engagement binders.
fieldguide’s wager is that ai agents can absorb the first-pass work that consumes junior auditor time—testing, tying, procedure drafting, and repetitive saly execution—while humans move up the value chain toward judgment and client advisory.
goldman’s rationale: vertical ai with measurable roi
goldman sachs alternatives is not a typical early-stage venture investor. its growth equity group enters when a category demonstrates repeatable adoption and operating leverage. darren cohen, goldman’s global co-head of growth equity, told fortune that customer conversations pointed to 30% to 40% efficiency improvements.
that emphasis matters. the investment case is not “ai will change everything,” but rather: firms are already getting measurable throughput gains in engagements that have historically been labor-intensive and margin-constrained.
goldman also highlighted fieldguide’s domain depth. unlike many startups rushing to bolt large language models onto professional workflows, fieldguide began building before chatgpt’s mainstream moment. chang, a former auditor, argues that audit requires more than a chatbot. it requires purpose-built models embedded in methodology, controls, and engagement governance.
fieldguide reports that it is now used by roughly half of the top 100 u.s. accounting firms, including kpmg at varying levels of adoption. the company employs about 160 people and plans to double headcount over the next year, blending engineers with former auditors to build systems that understand audit evidence, procedures, and professional standards.
fieldguide product |
description |
fieldguide ai platform for advisory & audit |
a cloud-based platform that automates and manages the entire lifecycle of audit and advisory engagements to improve operational workflows and client collaboration. |
fieldguide ai |
an ai system purpose-built for audit and advisory firms that automates multi-step workflows and provides intelligent recommendations to enhance productivity. |
engagement hub |
a tool for managing and automating the entire lifecycle of audit and advisory engagements to improve margins and client satisfaction. |
reporting automation |
a feature that enables one-click generation of collaborative audit and advisory reports, reducing time spent on report creation. |
document management |
a system for organizing, associating, and managing workpapers and documents related to audit and advisory engagements. |
request management |
a cloud-based tool for sending, tracking, and managing client requests during audit and advisory engagements to improve communication and client satisfaction. |
client hub |
a client portal that provides a modern digital experience for clients to interact with audit and advisory teams throughout engagements. |
where the audit workflow breaks today
to understand why capital is flowing into audittech, it helps to be precise about where audit work still bogs down.
first, evidence collection remains stubbornly manual. the “pbc” (provided by client) list may be digitized, but the underlying work of chasing documents, reconciling versions, and tying evidence back to assertions consumes enormous staff time.
second, testing and documentation are repetitive by design. many procedures exist because standards require them, not because they are intellectually challenging. junior auditors spend weeks ticking and tying invoices, recalculating schedules, and copying support into binders.
third, review cycles create friction. audit is hierarchical: staff prepare, seniors review, managers re-review, partners sign. every cycle introduces notes, rework, and delays.
fourth, firms still rely heavily on sampling and judgmental selection. while analytics has improved, much of the profession remains rooted in methodologies designed for a paper era.
agentic platforms like fieldguide are targeting these choke points: automate the first pass, structure evidence flows, draft procedures, and allow humans to focus on higher-level risk interpretation.
from offshoring to agentic execution
perhaps the most disruptive claim in chang’s remarks is that ai agents are beginning to replace work that has traditionally been outsourced offshore. instead of sending first-pass testing and documentation to teams in india or the philippines, fieldguide argues its agents can execute initial procedures, with u.s.-based auditors shifting into review, interpretation, and client-facing advisory.
chang suggested that within a few years, a 10-person audit team could potentially deliver an engagement with three people supported by agentic systems. whether that compression proves realistic remains uncertain. but the direction is unmistakable: firms are seeking capacity multipliers that do not require proportional hiring.
if ai agents can reliably perform revenue testing, pbc requests follow-up, audit planning drafts, and procedure documentation, the economics of assurance change. the profession could reallocate scarce human time toward higher-level risk assessment and client trust work. at the same time, regulators and inspection bodies will demand governance, explainability, and audit trails around ai-generated work.
the emerging audittech market map
fieldguide’s series c arrives amid a broader surge of capital into what is increasingly being called “audittech.” but the market is not monolithic. it is splitting into distinct architectural layers, each competing for auditor hours and firm budgets.
layer 1: engagement workflow platforms
fieldguide represents the platform thesis: the audit workflow itself becomes ai-native. engagement execution, evidence collection, review notes, and agentic task completion live inside one system. these platforms aim to replace fragmented stacks with an operating layer for assurance.
the bet is that firms will standardize delivery on platforms, as they standardized tax prep on major software decades ago. if that happens, the platform becomes sticky infrastructure.
layer 2: excel-native evidence automation wedges
not every winner will be a full platform. datasnipper illustrates the wedge strategy: embed directly into excel, where auditors already live. datasnipper raised $100 million at a $1 billion valuation and claims 400,000 auditors across 125 countries.
the wedge is powerful because it requires less organizational change. a senior auditor can adopt an excel add-in without a firmwide platform migration. over time, wedges expand outward—into portals, document ai, and workflow modules.
layer 3: continuous audit analytics and anomaly detection
mindbridge represents a different approach: ai as a risk-scoring engine rather than a workflow platform. mindbridge says it has scored more than 100 billion financial entries and has raised institutional growth investment from psg equity.
these tools push audit from sampling toward population-level monitoring. instead of selecting a subset of transactions, auditors can prioritize anomalies and risk clusters across the full ledger. that shift could redefine what “audit evidence” means in a data-rich world.
layer 4: grc and internal audit infrastructure under private equity consolidation
private equity is also reshaping the landscape. hg’s acquisition of auditboard for more than $3 billion underscores that connected risk and compliance platforms are being priced as durable enterprise infrastructure.
auditboard has surpassed $300 million in annual recurring revenue and serves more than half of the fortune 500, according to company statements. that is a different endgame than venture disruption: pe consolidation treats audit software as long-lived compliance plumbing, ripe for rollups and operational scaling.
layer 5: controller-stack convergence
another frontier is the convergence of audit and finance operations. close platforms such as floqast, valued at $1.6 billion after a $100 million series e, are increasingly positioning themselves as audit-ready systems. if controllers standardize close and reconciliation workflows upstream, auditors may follow the data into those platforms.
similarly, accounting automation vendors such as trullion are expanding from lease accounting and revenue recognition into audit modules. the boundary between “accounting ops ai” and “audit ai” is blurring.
competitor grid
fieldguide — engagement workflow platform (agentic ai); $75m series c; $700m valuation; used by half of top 100 firms.
datasnipper — excel evidence automation wedge; $100m series b; $1b valuation; 400,000 auditors.
mindbridge — continuous analytics engine; $60m psg investment; 100b+ entries risk-scored.
auditboard — pe-backed compliance infrastructure; acquired by hg for $3b+; >$300m arr.
strategic buyers and the big four’s new playbook
fieldguide is not only venture-backed. it has also drawn strategic engagement from the profession itself. kpmg has made a minority investment and is collaborating with fieldguide to develop ai-enabled assurance capabilities.
brian fields, kpmg’s enterprise innovation leader, said ai agents can simplify data collection and accelerate execution while maintaining quality standards. that partnership highlights a likely next phase: large firms will not simply buy generic copilots. they will embed ai into proprietary methodology and delivery models, turning audit execution into a competitive differentiator.
regulation, trust, and the limits of automation
audit is not a consumer workflow. it is a high-liability, standards-driven discipline. ai systems operating inside engagements will face scrutiny from regulators, pcaob inspectors, and risk committees.
key questions include:
- how is ai-generated work documented and reviewed?
- what controls ensure evidence integrity?
- how do firms prevent hallucinations or unsupported procedures?
- who is responsible when an ai agent misses a risk signal?
fieldguide and its peers emphasize that humans remain the reviewers and signers. ai executes first passes; auditors interpret and conclude. but the more the profession relies on automation, the more governance will become part of product differentiation.
what comes next: the audit operating system race
goldman’s lead role in fieldguide’s series c suggests that major allocators now view ai in audit as a category with durable demand, measurable roi, and infrastructure-level significance.
the next phase will likely include:
- consolidation: pe rollups of legacy platforms and niche tools.
- platform wars: workflow systems competing to become the engagement operating layer.
- methodology embedding: firms turning ai into proprietary assurance ip.
- labor restructuring: shifting junior work away from ticking-and-tying toward review and advisory.
- regulatory adaptation: standards evolving around ai-assisted evidence.
fieldguide’s financing may ultimately be remembered less as a startup milestone than as a marker of when audit automation became an unavoidable strategy for the profession.
for cpa firm leaders, the takeaway is not simply that one vendor raised $75 million. it is that the audit tech stack is being rebuilt under the combined pressure of talent scarcity, client expectations, and capital markets that now see assurance workflows as investable infrastructure.