Fogoarai lets large companies put AI to work inside their own systems. The data never leaves their network, the company's IT team controls what each AI worker does, and every action gets recorded.
Most AI today sits outside the company. It can't see internal systems, can't be governed, and can't safely handle the data that matters. For a regulated business, that means it stays at the edges instead of doing real work.
Most enterprise AI work doesn't need the most expensive model. Companies that route to smaller specialized models save 50–70% on cost.
Contracts, customer records, financials, tickets, internal documents — none of this can go to OpenAI, Google, or Anthropic without breaking compliance.
Real work needs the AI to read from CRM, ERP, databases, documents, and approval flows — not just chat.
Without controls, audit logs, and approvals, AI can't get past a security review at a regulated company.
Seven layers turn a single AI request into a governed action the company can trust — from who asked, to which model answered, to which system was touched, to who approved it, to what got logged.
A person, a ticket, a schedule, or another system kicks off the work — with the user's role and the request's context attached.
Before any work begins, the company's rules decide what this user, this worker, and this workflow are allowed to do.
The worker plans the steps, remembers context across them, and uses the right tools to complete the task end to end.
Routine work goes to smaller, cheaper specialized models. Harder work escalates to a frontier model. The company sees what was chosen and why.
CRM, ERP, databases, documents, ticketing, email, internal APIs — the worker reads and writes only what policy allows.
Sensitive or irreversible actions stop and wait for the right person to approve before anything is committed.
Every input, model choice, tool call, approval, and output is recorded — searchable, exportable, and built for security and compliance review.
Most of the AI work inside a company is narrow and repeatable — classify a ticket, pull data out of a document, validate a field, summarize a report. That work belongs on smaller, cheaper, specialized models. Frontier models only step in when the task actually needs them.
The target cost reduction on high-volume workflows when routine work runs on smaller specialized models, with quality protected by validation and confidence-based escalation.
// design target · varies by workflow · not a universal guaranteeIn active design. Today, routing happens at the MCP client layer (Claude Desktop). The internal router shown above is the next major release.
Fogoarai is built for companies that can't send sensitive work to an outside black box. It runs inside the company's own cloud, dedicated VPC, or on-prem environment — with the company in control of where data sits, which models are used, what gets logged, and what each worker is allowed to do.
Two real workflows, in production, in regulated industries. Both are companies that could not send their data to OpenAI, Google, or Anthropic — so the AI runs inside their own systems instead.
An AI worker that reads competitor disclosures and filings, builds side-by-side comparisons across coverage, pricing, and policy terms, and points out gaps for the company's analysts.
An AI worker that connects the engineers to their archive of mining experiments — surfacing prior findings and outcomes so teams can use relevant past work instead of repeating expensive studies.
The control plane is what the company's IT, security, and operations teams use to run AI workers like any other production system — track cost, latency, errors, approvals, and every action recorded against policy.
Every layer of Fogoarai is built around the controls enterprise security, risk, and compliance teams expect from production infrastructure.
Fogoarai starts with one workflow that matters. Over time, every team can run AI workers on the same private layer — same data controls, same tools, same approvals, same audit. That's how a company moves from AI experiments to AI doing real work, everywhere.
One workflow in production with measurable ROI and a clear success metric.
The first AI workers go live with routing, tool access, approvals, and full audit.
Reusable AI workers spread across departments, on the same controls and quality gates.
AI workers run alongside human teams across the company, on a shared, governed layer.
There are four ways a large company usually tries to solve this. Here is where each one falls short for a regulated business.
Those are libraries for engineers, not products a company can install. They have no controls, audit, or security review story.
They tie the company's AI to the cloud provider's infrastructure. In regulated industries, that is the deal breaker before evaluation starts.
Those are good at one specific job. A company that buys Moveworks for IT still needs something for finance, legal, document review, and competitive intelligence. That something is Fogoarai.
Most large companies start by assembling LangChain + OpenAI + custom access controls. They end up with brittle systems they can't evaluate, version, or hand to compliance. Fogoarai is the buy option.
Pick one workflow that matters. Connect it to the right internal systems. Add the approvals and audit a regulated business needs. Measure the result inside six weeks.