Ayudh runs entirely on your infrastructure. Three local AI models, five processing roles, zero external API calls. Every decision logged. Every change traceable.
The standard approach to enterprise AI starts with a model and wraps an interface around it. That gives you layers 1 and 6. The four layers in between — knowledge, retrieval, reasoning, and verification — are where production AI systems succeed or fail.
Ayudh covers all six layers. Infrastructure at the base. Knowledge indexing with embeddings and document graphs. Semantic retrieval via Graph RAG. Multi-model reasoning with role-specific AI. Verification through audit trails, source attribution, and confidence scoring. Application delivery through a browser-based interface with live progress and email output.
Built from the bottom up — because that is the only order that works.
Ayudh uses three local AI models — Parser, Oracle, and Drafter — serving five distinct processing roles. No inference leaves your machine. Every document is processed in its own isolated directory with a complete, append-only audit log.
The system runs a Next.js front-end with a FastAPI back-end. PostgreSQL for persistent storage. Redis and Celery for job queuing. FalkorDB for graph-based RAG. Each processing run is isolated — its own working directory, its own audit log.
The Ayudh team installs the system on your infrastructure. Your IT controls network access. Your compliance team reviews the audit trail. Your lawyers review every output.
Ayudh does not rely on policies or promises to keep your data safe. Security is built into the architecture. There is no external data flow to block — because there is no external data flow.