ServicesCore practice
AI Chatbots & Enterprise Assistants
Most chatbots demo well and then embarrass someone in production: they invent an answer, miss the spreadsheet where the real one lives, or take thirty seconds to say it. We build assistants that are grounded in your own knowledge base, cite the source of every answer, and hand off to a human rather than improvise — because one invented answer costs more trust than a hundred correct ones earn.
Under the hood that means hybrid retrieval (BM25 plus dense vectors) with reranking, separate indexes for narrative text, tables and figures, multi-index routing, conversation-aware query rewriting, and deterministic refusal gates decided in code rather than left to the model's mood. Our founding team built exactly this as a multi-tenant platform for enterprise tenants — the case study below is the evidence.
What's included
Named deliverables, not vague verbs
Grounded RAG assistants
Citation-grounded answers over your documents, spreadsheets and images, with refusal as a first-class output.
Multi-tenant knowledge platforms
Per-tenant ingestion, indexing and configuration, so every client of yours queries only their own data.
Document intelligence pipelines
Layout, table and figure extraction from PDFs, Office files and scans — eight formats in production today.
Latency engineering
Regex fast paths, prompt right-sizing and history compression. One rebuild took follow-up turns from 38 seconds to under 5.
Multi-channel delivery
Web, Microsoft Teams and WhatsApp from one conversation core, with human handoff built in.
The evidence
Where we've done this before
Stack for this practice
What it's built with
- Azure OpenAI
- Semantic Kernel
- Azure AI Search
- Cloudflare AI Search
- Cosmos DB
- Document Intelligence
- Azure AI Vision
- Bot Framework
- .NET 8
- FastAPI
Evaluator questions
Asked by every serious buyer
How do you stop it from making things up?
Grounding is enforced in code, not requested in a prompt. Retrieval scores gate whether the model is allowed to answer at all; weak evidence is labelled as weak; an empty retrieval produces a refusal with a human handoff. Every answer carries its source. The chatbot on this site works exactly this way — test it.
Our knowledge lives in spreadsheets, not documents. Does that work?
Yes, and it is where generic document chat fails. We index tables separately from prose, route exact-value questions to a row store instead of a semantic guess, and disambiguate when two spreadsheets share column names.
What about our data staying ours?
We deliver inside your cloud tenant when required — our founders have shipped platforms inside client Azure tenants, against the client's own data and security review. Nothing trains on your data.
Ready to scope ai chatbots for your business?
Thirty minutes with a founder. You leave with an honest read on feasibility, a first-step recommendation, and no obligation.