AI Automation
— agentic workflows, with guardrails

AI-first workflows that remove manual effort — from RAG assistants grounded in your knowledge to multi-agent systems that execute end-to-end processes under human oversight.

RAG is the baseline, not the product

Every assistant retrieves. The difference is retrieval quality — chunking, hybrid search, re-ranking, agentic query planning — and evaluation that proves it.

Agents act, so they need permissions

MCP-connected agents call tools and change systems. Scoped identities, approval gates and reversible actions are what separate automation from incidents.

Deterministic where it matters

Winning designs mix LLM reasoning with deterministic workflow engines, rules and validation — so outcomes are auditable.

Chunk → embed → top-k similarity → generate. Fast to build; fails on ambiguous queries, long documents and permissions.

Over-retrieve, then a cross-encoder or LLM re-ranker orders candidates by relevance. Large precision gain for modest latency.

Dense vectors plus sparse/BM25 keyword search fused (RRF), with metadata filters — handles codes, names and exact terms embeddings miss.

An agent decomposes the question, plans retrievals across sources and tools, verifies and iterates. Best for multi-hop, multi-system questions.

Knowledge graphs and text-to-SQL for relationships, aggregates and governed data.

Query rewriting, HyDE, parent-child chunking, contextual retrieval, semantic caching, citation enforcement.