How DABAR works
1. Ingest
Connect any primary source — PDFs, Word, audio, video, SQL, URLs, external APIs, voice notes, maps. Structured and unstructured, all at once.
2. Reason
A Policy Engine governs every output. Your organization defines what sources are authoritative, what thresholds apply, and how answers must be structured.
3. Act
Deploy the verified knowledge as autonomous agents connected to your systems via MCP, REST, custom skills, or browser automation.
Verified outputs
Every response DABAR produces is explicitly labeled:Verified fact, traced to the exact source and page.
No verifiable evidence found in the provided sources.
Inference flagged clearly. Never presented as fact.
Key concepts
Policy Engine
Policy Engine
The core of DABAR. Instead of relying on prompts, organizations codify their reasoning rules — what the AI must consider, what it must flag, and what it must never do.Learn more →
Primary Sources
Primary Sources
DABAR works exclusively with sources your organization defines and controls — internal documents, proprietary databases, specific URLs, or approved external APIs. Never unverified internet content by default.Learn more →
Domain Intelligence
Domain Intelligence
DABAR builds a specialized domain model from your organization’s own sources — like a senior analyst who internalized your entire library and now reasons under your rules.
FusionAI Orchestration
FusionAI Orchestration
DABAR is powered by FusionAI, a multi-model orchestration layer that selects the most appropriate model for each task — optimizing for reasoning accuracy, context size, cost, and latency.
Autonomous Agents
Autonomous Agents
Every knowledge base in DABAR can power agents that take action — not just answer questions. Agents execute multi-step workflows under the same policy governance as the research layer.Learn more →
Who DABAR is for
DABAR is built for organizations in regulated industries where the cost of an unverifiable error is legal, financial, or reputational.- Banking & Financial Services — credit risk, KYC/AML, regulatory reporting, ESG compliance.
- Legal — case research, contract review, litigation strategy, library indexing.
- Agriculture & Supply Chain — field sales intelligence, product knowledge, client history.
- Government & Public Sector — policy research, compliance monitoring, regulatory analysis.
- Consulting & Professional Services — due diligence, market research, client reporting.
Why DABAR
| Traditional RAG | Internet Search | DABAR | |
|---|---|---|---|
| Source control | Partial | None | Full |
| Policy Engine | No | No | Yes |
| Output labeling | No | No | CONFIRMED / NOT FOUND / ESTIMATED |
| Autonomous agents | No | No | Yes, via MCP |
| Multi-model orchestration | No | No | Yes, via FusionAI |
| Auditable by regulators | No | No | Yes |
In production
A bank came to us. A regulated financial institution was spending 2–3 weeks with multiple analysts to produce a single environmental-risk assessment — a static PDF that had to be presented to the board before approving or rejecting a loan. With DABAR the same process now takes 4 hours. Every finding is labeled CONFIRMED, NOT FOUND, or ESTIMATED with source and page. And instead of a static document, the board now has an autonomous agent they debate with in real time before approving a loan. In regulated industries, an unverifiable error isn’t a bug — it’s a legal problem. DABAR is the infrastructure that eliminates it.Security & deployment
- Source isolation — data never leaves the sources you define.
- Role-based access control across users, projects, and policies.
- Full audit trail on every output, with source and page-level traceability.
- Deployment options — managed cloud, VPC-isolated, or on-premise.
- SOC 2 Type II — in progress.
Next steps
Quickstart
Make your first authenticated request in under 5 minutes.
Authentication
How to authorize requests with your API token.
Policy Engine
Understand how DABAR reasons under rules you define.
API Reference
Explore every endpoint in the Politics and Projects APIs.