→ 01
Grounded in your content
Answers come from your docs, your product, your policies — not the model's general training.
03/AI CHATBOTS & ASSISTANTS
THE PROBLEM You want a chat assistant that answers customer or internal questions accurately — without making things up — and you don't want to ship it on hope.
A chatbot that knows your stuff — and admits when it doesn't.
We build retrieval-grounded chat assistants for customer support, internal knowledge bases, and product help. They cite their sources, stay inside your domain, and refuse to bluff. We pair them with evals so the day you ship is the start of the trust, not the end.
→/OUTCOMES
→ 02
Citations + 'I don't know'
Responses point to source material. When the answer isn't in the corpus, the bot says so instead of inventing one.
→ 03
Measured before shipping
We ship with a real eval set — accuracy, refusal-rate, hallucination-rate — so we can argue about it with numbers, not vibes.
→ 04
Owned by you
Knowledge base, prompts, and code in your repo. Update content, redeploy, the bot knows.
→/TOOLING
A representative — not exhaustive — set of tools we reach for on ai chatbots & assistants engagements. We pick by fit, not by brand loyalty.
- Claude / GPT-4
- RAG (retrieval-augmented generation)
- pgvector / Pinecone
- LangChain / LlamaIndex
- Next.js / streaming UI
- Anthropic prompt caching
→/PROCESS
STEP 01
Define scope
What questions should it answer? What's out of scope? What corpus does it draw from?
STEP 02
Build the corpus + retriever
We index your content, set up retrieval, and tune the prompt so the model uses what's retrieved.
STEP 03
Evaluate
We assemble a question set with known good answers and measure accuracy + refusal-rate.
STEP 04
Ship + monitor
Production deploy with logging, feedback capture, and a content-update workflow.
→/FAQ
How do we keep it from hallucinating?
Retrieval-grounding plus careful prompting that instructs refusal when the corpus doesn't support an answer. We measure hallucination rate as part of the eval set.
Can it act as well as answer?
Yes — that crosses into agent territory. See the AI Agents solution.
What about open-source models?
We use them when they fit. For business-critical tasks today, Claude and GPT-4 are usually the right call. Happy to explain the trade-off.