Chatbots that answer from your documents, not from guesswork

Retrieval-augmented generation grounds every answer in your own approved content — policies, product information, documentation and SOPs — so responses are accurate and traceable.

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A general-purpose chatbot does not know your business

A model with no access to your content can only produce plausible text. For internal support, customer service or sales enablement, plausible is not good enough — an answer that sounds right and is wrong costs more trust than no answer at all.

  • Answers invented when the model has no grounding
  • Knowledge scattered across drives, wikis and inboxes
  • The same questions answered repeatedly by the same people
  • No way to tell where an answer came from, or whether it is current

What we build

We build the retrieval layer first, because retrieval quality — not model choice — is what determines whether a RAG system is usable. Documents are chunked, embedded and indexed so the model answers from your approved content, and can say when it does not know.

  • Document ingestion from your existing sources
  • Chunking and embedding tuned to your content, not defaults
  • Answers grounded in retrieved passages, with sources shown
  • An explicit escape hatch to a human when confidence is low
  • Evaluation against real questions before launch

How the engagement runs

  1. Content audit

    We review what documentation exists, what is current, and what the system should never answer from.

  2. Retrieval build

    Ingestion, chunking and indexing, tested against the questions your team actually receives.

  3. Interface and guardrails

    The chat surface, plus the rules for when it should decline and escalate instead.

  4. Evaluation and launch

    We test against real queries, tune retrieval, then deploy to the channel where the questions arrive.

Related work

Enterprise RAG: AI-Powered Knowledge Base
Enterprise Knowledge Management

Enterprise RAG: AI-Powered Knowledge Base

Turning Fragmented Company Documents into a High-Intelligence Internal Oracle

Read the case study
AI-Powered Slack Helpdesk & Knowledge Engine
Internal Support Automation

AI-Powered Slack Helpdesk & Knowledge Engine

Transforming Chaotic Team Requests into a Self-Learning Internal Support System

Read the case study
AI-Powered Database Concierge
Data Access Automation

AI-Powered Database Concierge

Unlocking Enterprise Data Through Natural Language Conversations

Read the case study

Frequently asked questions

What is RAG, in plain terms?

Retrieval-augmented generation means the AI looks up relevant passages from your own documents before answering, and writes its answer from those passages. The practical difference is that answers come from your approved content rather than from the model's general training.

What document formats can you ingest?

Typically PDFs, Word documents, spreadsheets, wiki and knowledge-base exports, and web pages. The harder question is usually not format but currency — which version is authoritative, and what should be excluded because it is out of date.

How do you stop it from making things up?

Two mechanisms. Answers are grounded in retrieved passages and cite what they came from, so a wrong answer is traceable. And the system is configured to decline and escalate when retrieval returns nothing relevant, rather than filling the gap with plausible text.

What happens when it cannot answer?

It hands over to a person. We treat the escalation path as part of the build rather than an afterthought, because a chatbot with no exit is worse than no chatbot — it traps the user in a loop instead of getting them help.

Where does our data go?

That is a decision we make with you during scoping, and it depends on your compliance requirements. We use least-privilege access and encrypted secrets as standard, and can discuss deployment options where data residency matters.

How do you keep it current as documents change?

Re-ingestion is built as part of the system rather than a manual chore. How often it runs depends on how fast your content changes; we agree that during scoping.

Tell us what you want to automate

A short call to scope the workflow, followed by a written proposal with scope, timeline and cost before any work begins.

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