For developers
The financial brain
of the agentic economy
A deterministic financial engine your AI agents call over MCP, REST or API. The output is a certified three-statement model in which every figure is traceable. No hallucinated arithmetic.
Free sandbox · MCP-compatible · first call in minutes
The problem
LLMs are bad at numbers. Your users will notice.
- LLMs are probabilistic text generators, not calculators; arithmetic and financial logic are structurally unreliable in them.
- An ad-hoc calculation in a sandbox gives you a number, not a linked and verified model: the balance sheet doesn't tie, there's no audit trail and no industry methodology.
- Financial output from an agent won't reach production without an audit trail (Gartner: >40% of agentic projects will be cancelled by 2027).
What you get
Not a calculator. A certified model — over an API.
A certified model, not a calculator
A three-statement model (P&L, cash flow, balance sheet) that ties, with an integrity certificate on every build.
Determinism and traceability
One input → one reproducible, auditable output. Every figure traces back to a formula and a source.
Multi-industry plus export
Real estate, SaaS, operating businesses; investment and operating models. Export with live formulas.
Integration
Three ways to connect
MCP — native tools
finmodel_build_from_brief (brief → certified model) and finmodel_chat — conversational refinement via session_id, synchronously, right inside your agent's tool loop. Plus read/export tools. MCP is already the standard: OpenAI, Google, Microsoft; 10,000+ servers; under the Linux Foundation.
REST — the same, asynchronously
POST /api/agent/build and POST /api/agent/chat → 202 {job_id}, poll GET /jobs/{id}. For headless agents and long-running builds.
fmk_ API keys
Scoped tokens (least privilege, RFC 8707 audience binding), workspace-level isolation.
All that leaves your system is a plain-language brief and a certified artifact. Your agent receives a finished, fully linked model and refines it conversationally — without assembling the financial logic itself and without risking a wrong number.
A number is not a model
“Why not just let the agent compute it in code?”
An agent in a sandbox can compute a percentage or a DCF. But building a linked, balanced, industry-specific three-statement model with an audit trail in ad-hoc code is expensive, slow and unreliable. We ship that as a service with an integrity guarantee: if it doesn't tie, the job says so honestly instead of sending broken numbers to your user.
Where it fits
- AI CFOs and financial copilots
- Vertical fintech agents (lending, underwriting)
- Investment-analysis agents (deal screening, IRR/NPV)
- Embedded financial planning inside vertical SaaS
Production-ready
Trust and standards
- The MCP standard (OpenAI / Google / Microsoft, Linux Foundation)
- Determinism plus an integrity certificate on every build
- Scoped API keys from your dashboard, workspace isolation
- Reproducibility and a complete audit trail
Price
Pay per call
A free sandbox to integrate against. After that, usage-based pricing (per call or credits) as is standard for APIs, with a hybrid base-plus-usage plan at volume.
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