Author governed, source-grounded domain AI agents once and run them identically anywhere, including fully air-gapped.
Enterprises in regulated domains cannot send sensitive data to hosted AI services, and generic LLM apps produce answers with no verifiable provenance, no access controls, and no defensible record of what happened. Governance, attribution, and auditability are typically bolted on after the fact, if at all. That leaves teams unable to trust, deploy, or defend AI in the environments where it matters most.
FactoAI Studio is an agentic AI platform for building specialized domain agents that deploy cleanly into a customer's own environment, whether on-prem, private cloud, hybrid, or fully air-gapped. Each agent is a versioned, declarative Agent Bundle of prompts, tools, retrieval, guardrails, governance, evals, and model requirements, authored once in the Studio and executed identically by the Runtime. Every answer is grounded in sources and verified claim by claim, every decision is policy-checked, and every step is written to a tamper-evident audit log. No customer data has to leave the environment.
The data plane runs fully self-contained and air-gappable, with embeddings and optionally models running locally so data stays in place.
Every answer is decomposed into atomic claims and each is verified against the exact source span retrieved, not just tagged with a citation.
No-code guardrails, Open Policy Agent access rules, eval gates, and a hash-chained audit log are part of the core rather than add-ons.
An agent is a versioned, immutable bundle that executes identically across on-prem, private cloud, hybrid, and air-gapped environments.
An agent is a versioned, immutable spec pairing prompts, tools, retrieval, guardrails, governance, evals, and model requirements. Author it once and run it identically in any environment.
Dense semantic embeddings are fused with sparse lexical BM25 ranking, and layout-aware ingestion turns PDF, DOCX, PPTX, and HTML into section-aware chunks carrying human-readable source locations.
Register connections to Anthropic, OpenAI, Gemini, or local vLLM and Ollama with credentials encrypted at rest, and bind any model that satisfies a bundle's declared requirements.
Faithfulness, answer relevancy, citation coverage, and attribution coverage are scored by an in-environment judge plus deterministic checks. A bundle ships only if it passes the thresholds in its spec.
Every policy, guardrail, plan, tool call, attribution, and finalize decision is written to an append-only, hash-chained log, so altering any earlier record invalidates every later hash.
Chain bundles into stages that run sequentially, in parallel, or conditionally with typed data flow, executed durably as independently governed and audited child runs.
LoRA fine-tune a small base model on the customer's own data inside their environment, producing an adapter the gateway serves and a bundle binds to, with eval-gated promotion.
Create and edit agents, configure models, guardrails, and policies with no code, run agents in a Playground, and review the audit trail from a web console.
In the Studio, define the agent as a versioned bundle of prompts, tools, retrieval, no-code guardrails, plain-English access policies, evals, and model requirements.
Ingest documents into the hybrid index, run the agent in the Playground, and trigger the eval gate that must pass before the bundle ships.
The Runtime executes the agent loop, checking policy, grounding each claim to a source span, and writing every decision to the hash-chained audit log.
Package the agent, or a whole workflow, into a self-contained air-gap artifact bundling its policies, guardrails, and model connection as a Helm chart.
Install the zero-egress data plane on-prem, private cloud, hybrid, or air-gapped, binding a local or provider model that meets the bundle's requirements.
Yes. The data plane runs self-contained on-prem, private cloud, hybrid, or air-gapped, and a network policy denies outbound traffic while embeddings and optionally models run locally, so customer data stays in place.
Every answer is decomposed into atomic factual claims, each claim is verified against the specific source span the agent retrieved, coverage is recorded, and answers carrying unsupported claims are blocked.
Yes. You can register Anthropic, OpenAI, Gemini, or local vLLM and Ollama connections with credentials encrypted at rest, and a bundle declares requirements so any model that satisfies them can be bound, with the eval gate re-run on the swap.
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