Every enterprise has bought AI. Almost none of it can reach the business. Datafi is the governed layer that connects the AI clients employees already use to the systems a company runs on, so AI can move from answering questions to solving business problems.
The missing piece in enterprise AI is not another chat tool. It is a governed contextual layer that connects the AI seats companies already own to the ERP, TMS, CRM, and on-premise data those tools cannot currently reach.
Every employee has an AI tool. None of them can reach the business.
Enterprises now license Copilot, ChatGPT, Claude, and Gemini as standard issue. But those clients stop at the edge of Microsoft’s or Google’s cloud. ERP, TMS, policy administration, and on-premise data stay out of reach, and security teams cannot see or control what AI touches.
The consequences are predictable. Shadow AI grows while governed AI stalls. Pilots never reach production. Decisions stay slow and manual, and risk rises with every new tool a team adopts.
AI that solves problems, not just answers questions
At Datafi, we see customers asking AI to take on critical thinking roles: workflow automation, analysis, and operational decisions that used to require a person stitching together reports from five systems. Answering a question is useful. Solving a business problem is transformative.
To get there, AI needs three things most deployments lack. First, the full context of the business: how its data relates, what its terms mean, and how its processes run. Second, access to the complete data ecosystem, not a curated slice in one vendor’s cloud. Third, the ability to operate autonomously inside clear policies, so agents can learn, act, and improve on hard problems over time.
Together these form a contextual layer, the foundation every complex agent and workflow depends on. Our conviction is that this layer can only be built well on a vertically integrated data and AI stack, with access, policy, and control designed in from the start and a chat experience built for non-technical users.
One vertically integrated stack
Datafi delivers that foundation as five integrated products. Business Chat lets anyone ask questions of their data. Studio lets analysts and operators build agents and apps without code. Runtime turns those agents into dependable production execution. Control Tower governs every interaction, and Cyber secures it.
Datafi platform · four integrated layers on one context layer
Because the layers are built together, they reinforce one another. Every agent built in Studio inherits Control Tower policies, runs on Runtime without a rewrite, and passes through Cyber risk controls before it acts. There is no integration tax and no control gap between tools.
Customers reach the platform through two offerings. Converse lets people talk to their data: ask complex questions across several systems at once, then generate reports and documents inside the AI client they already use. Activate puts AI to work: teams build and deploy agents, apps, and workflows with human approvals, refining each step visually in Studio. Converse is the natural starting point, and Activate is the built-in expansion path.
Benefit 1: a unified data experience for every employee
Most employees will never write a query or open a data warehouse. With Datafi, they do not need to. Anyone can ask a question in plain language and get an answer grounded in live business data, structured or unstructured, with citations back to the source and only the access they already hold.
Datafi also meets people where they already work. Finance can stay in Copilot, sales in ChatGPT, engineering in Claude, and data teams in their own models. Datafi connects each of those tools to the ERP, TMS, CRM, on-premise databases, file shares, and email behind them, with zero data migrations.
For the business, that means one consistent data experience across the company, regardless of which AI client each team prefers.
Benefit 2: governed, compliance-ready AI
The moment AI touches customer records, financial data, or regulated information, governance becomes the bottleneck. Datafi removes it by design rather than by retrofit.
Every request is checked against the user’s row-level permissions and logged in one place, whichever AI tool it came from, and the data stays in the customer’s environment. Control Tower gives leaders centralized policies, real-time observability across every agent and data access, and immutable audit trails with full lineage. The Cyber layer adds continuous risk scoring, prompt injection detection, data leakage prevention, and zero-trust verification of every agent action.
This matters most as agents gain autonomy, because every prompt, tool call, and workflow step becomes part of the attack surface. Datafi is SOC 2, ISO 27001, GDPR, and CCPA compliant, and agents built on the platform inherit global access policies automatically. Organizations can start read-only with one team on one dataset and expand with confidence, closing the shadow AI gap instead of widening it.
Benefit 3: faster, better operational decisions
The payoff of unified, governed data is speed. Customers put a first use case into production in three days or less through a launch workshop. Studio users build agents in under 30 minutes with no code, and the platform reports agent creation up to 10x faster than conventional approaches.
More important, value compounds. Each new use case inherits the policies, data connections, and security already in place, so the second, tenth, and fiftieth workflow cost far less than the first. Conversations become repeatable operating procedures, and analysis turns into action inside the systems of record.
Oak Harbor Freight Lines, a regional LTL carrier, describes the shift as moving from reacting to problems to preventing them, with teams making faster, smarter calls across thousands of daily touchpoints. Ryan Specialty calls Datafi a proven alternative to building it themselves, and systems integrator Nortal reports that mutual clients get measurably more value from the platforms they already own.
Why now
Four shifts make this the moment. AI seats are already bought, so the missing piece is business data, not another chat tool. MCP has made connection a standard, so whoever owns the governed layer behind every client owns the enterprise data relationship. Per-user, row-level governance with a full audit trail is becoming the bar for AI in regulated industries. And mid-market IT cannot justify building a production MCP server with governance, context, and observability; they would rather ship use cases.
Why Datafi wins
Copilot and ChatGPT are not competitors. They are distribution: Datafi makes the AI seats customers already bought work on the systems outside Microsoft’s and Google’s clouds.
| AI clients alone | DIY MCP build | Palantir-style | Datafi | |
|---|---|---|---|---|
| Time to production | Fast in sandbox, slow in production | Months | Months | 3 days or less |
| Works with every AI client | One vendor’s client | Custom per client | Own interface | Copilot, ChatGPT, Claude, Gemini |
| Per-user governance and audit | Weak outside own cloud | Custom | Strong | Strong, built in |
| Data migration | None, limited reach | Often | Often | None |
| Cost model | Per seat, data gaps | High services cost | High platform cost | $20 to $40 per user plus credits |
Business model and traction
Pricing is published and grows with usage. A Basic license for Converse is $20 per user per month, and a Standard license for Activate is $40. A shared pool of usage credits expands spend beyond seats, and managed services run $50K to $500K a year. At scale, seats, credits, and services build to enterprise contracts from $100K to more than $1M.
Datafi grows through two motions. Enterprise sales focuses on four verticals, Retail, Supply Chain, Insurance, and Cybersecurity, landing with a compliance-driven cyber workflow and expanding into operations within three to six months, alongside partners Nortal and Snowflake. Product-led growth serves the mid-enterprise, where a team can start with 50 users for $12,000 a year.
Customers include Johnson & Johnson, Genuine Parts Company, Meijer, Ryan Specialty, and Oak Harbor Freight Lines, with pipelines across distribution, healthcare, and automotive. Datafi reached $1M in ARR in 2026, targets $4M in 2027, and plans for $12M in 2028.
The opportunity
Datafi is raising a seed round to build a repeatable growth engine: vertical sales and solutions engineering, product-led onboarding, and continued investment in Converse, Activate, security, and observability.
The durable advantage is the governed business contextual layer. Every connection, policy, and workflow a customer adds deepens Datafi’s understanding of that business and raises the cost of switching. AI models will keep changing. The company that owns this layer, and lets any AI client act on it, becomes the operating system for business AI. That is the company we are building.
