Fogoarai lets companies deploy internal AI agents across their data, tools, permissions, and workflows. Specialized SLMs handle routine work by default. Larger models step in only when needed. Every action stays governed, observable, and auditable.
Most AI agents fail in production because they sit outside the company's real operating model. They do not understand permissions, cannot safely use internal tools, create unpredictable costs, and leave teams without the audit trail required for critical work.
Large models are used for every step, even repetitive tasks that do not require frontier reasoning.
Business-critical workflows often involve contracts, customer records, financial data, tickets, emails, and internal documents.
Agents are not deeply connected to ERP, CRM, databases, documents, approval flows, or internal APIs.
Teams lack observability, versioning, evaluation, approvals, and traceability across agent decisions.
Fogoarai provides the runtime, routing, permissions, memory, tooling, evaluation, and audit layer required to run AI agents safely inside the enterprise.
A trigger arrives from a workflow, queue, ticket, or human request — carrying role, context, and intent.
Maps agents to user roles, permissions, action limits, and approval rules before any work begins.
Executes multi-step work, maintains state, manages plans, and coordinates tasks across tools.
Uses specialized SLMs by default and escalates to larger models only when confidence, complexity, or policy requires it.
Connects agents to internal systems — CRM, ERP, ticketing, databases, documents, email, and custom APIs.
Routes sensitive or irreversible actions to the right human reviewer before execution.
Logs every input, decision, tool call, model choice, output, cost, latency, and outcome.
In enterprise workflows, most model calls are narrow and repeatable: classify, extract, validate, route, summarize, complete fields, generate structured output, or call a tool. Fogoarai routes these tasks to specialized SLMs first, then escalates to larger models only when the task demands broader reasoning.
Designed to reduce inference cost on high-volume workflows by routing routine tasks to specialized SLMs while preserving measurable task quality through evaluation gates and confidence-based escalation.
// design target · not a guaranteed universal claim · varies by workflow profileFogoarai is designed for companies that cannot treat sensitive workflows as external black boxes. Deploy in a private cloud, dedicated VPC, or on-prem environment while maintaining control over data movement, model access, logs, and execution policies.
Begin with a single high-volume workflow that has measurable ROI, then expand the same governed runtime across teams as confidence builds.
Classify tickets, suggest resolutions, update systems, escalate exceptions, and document outcomes.
Extract, validate, compare, summarize, and route documents across teams with structured output.
Process invoices, reconcile records, prepare approvals, and generate audit-ready summaries.
Review clauses, flag risks, compare versions, suggest redlines, and route approvals.
Handle structured requests, enrich CRM records, summarize interactions, and trigger next actions.
Map policies to documents, flag exceptions, create evidence trails, and prepare review packets.
Fogoarai gives teams the visibility required to run agents in real enterprise environments. Track quality, cost, latency, escalation, tool errors, policy violations, and model performance across every workflow.
Every layer of Fogoarai is built around the controls enterprise security, risk, and compliance teams expect from production infrastructure.
Fogoarai starts with high-value workflows. Over time, every team can run agents on the same private layer of permissions, data, tools, memory, evaluation, and audit. That is how enterprises move from isolated AI experiments to an agent-native operating model.
Launch one high-volume workflow with measurable ROI and a clearly defined SLA.
Deploy private agents into production with routing, tools, and governance.
Standardize reusable agents across departments with shared evaluation gates.
Run enterprise work through a shared agent OS connected to company systems and policies.
Identify a high-volume workflow, route routine tasks to SLMs, connect internal tools, add approval controls, and measure ROI from the first production pilot.