AI chatbot development
Assistants that knowyour business, not the internet
A chatbot is only useful when it is grounded in your own material. We build assistants over your documents, your product data, and your policies, with the retrieval trace visible so nobody has to take an answer on faith.
500+
Projects delivered
most under NDA
100+
Clients served
BR · AU · IN · US · EU
20
Systems live
in production now
0
Account managers
you talk to the engineer
What we build
The work itself,component by component
Document-grounded chat
Upload, index, and query your handbooks, contracts, and specs with citations on every answer.
Product and catalogue assistants
Natural-language search over structured data, so a question returns the right SKU rather than a paragraph.
Escalation to a human
Confidence thresholds and clean hand-off with the full conversation attached.
Channel delivery
Web widget, in-app panel, or WhatsApp: the same engine behind each surface.
Guardrails
Scope limits, refusal behaviour, and PII handling defined before launch, not patched after an incident.
Conversation analytics
What people actually ask, what went unanswered, and which documents are missing.
Why it matters
What changesonce it is running
Three things a buyer of this work should be able to hold us to.
Fewer repeat questions: The assistant absorbs the questions your team answers a hundred times a month.
Grounded, not guessed: If the corpus does not contain an answer, it says so instead of inventing one.
A record of the gaps: Unanswered questions become a documentation backlog you can act on.
Stack we use for this
- Next.js
- FastAPI
- OpenAI / Bedrock
- pgvector
- Redis
- WebSockets
How it runs
Four stages,and you can leave after any of them
Scope
We agree what the system does and what shipping means, in writing.
Build
Short cycles in your repository, running before it is finished.
Ship
Deployed into your cloud account, reachable by a real user.
Stay
Maintenance and roadmap by the same engineers who built it.
Proof
The systemsthat back this page
FAQ
Questionswe get asked
How do you stop it making things up?
Retrieval-first: the model only answers from retrieved chunks, and returns a refusal when nothing relevant is found.
Can it read PDFs and scans?
Yes. OCR sits in the ingestion pipeline for scanned documents.
Who can see which documents?
Access is scoped per tenant and per role at query time, mirroring your existing permissions.
AI chatbot development