Natural-language search and answers over your documents, tickets, and knowledge bases — with citations and configurable behavior.
Ask in plain language; get relevant chunks across docs, tickets, and policies.
Generated answers always link back to sources users can verify.
Tune behavior between strict (only cite-supported answers) and exploratory.
Pull from CMS, knowledge base, ticketing, drives, and structured data sources.
Source-aware chunking that preserves structure and context, not naive splits.
Right-sized vector store with hybrid retrieval (vector + keyword + filters).
Rerankers for precision, eval suites for measurable retrieval quality.
Per-document permissions enforced in retrieval — answers only from what the user can see.
RAG quality lives in data preparation, retrieval design, and evaluation — not just the LLM choice.
RAG pipeline architecture, prompts, retrieval strategy, and evals.
Source connectors, change-data capture, and embedding refresh pipelines.
Structuring source content so retrieval has clean, well-tagged inputs.
Search and answer UX with strong source attribution patterns.
Inventory the content; pick a starting set with high signal and clean access.
Build a focused assistant; evaluate retrieval and answer quality on real questions.
Refine chunking, retrieval, prompts, and UX based on real usage.
Add sources, languages, and surfaces (web, in-app, Slack, etc.).
Context-aware assistants embedded in your products and internal tools.
AgentsAgentic AI that plans and executes multi-step workflows using your tools.
AutomationWorkflows that replace repetitive manual steps with AI-enhanced orchestration.
Tell us where the knowledge lives and who's asking. We'll scope a RAG pilot that's measurable, not magical.
Tell us about your project