Chatbots & RAG
We build chatbots that answer from your knowledge — documentation, policies, product data, past tickets — through retrieval you can inspect source by source. Every answer carries its evidence, so your team can check what the assistant said and why, and it is built by a company that has served 200+ clients across ten years.
Not sure whether your case needs a chatbot, a RAG pipeline or a full agent? Bring it to the free 30-minute session — an engineer maps where your answers live today and tells you honestly which build fits. You keep the notes either way.
Four ways the question reaches us
The request is rarely “build us a chatbot”. It is one of these, and the chatbot turns out to be the answer.
Support answers the same thirty questions every week
The answers exist — in the help centre, in the policy PDF, in a senior colleague’s head — but every repeat still costs a human reply. A grounded assistant resolves the routine questions on the spot, around the clock, and hands the genuinely new ones to your team with the full conversation attached.
The knowledge exists, in eleven places
A wiki, two shared drives, a ticketing history, product specs, onboarding decks and a few veterans who “just know”. RAG — retrieval-augmented generation — turns those scattered sources into one base the assistant can query, with permissions respected and every document traceable to its owner.
Legal wants to know where the answers come from
In regulated and customer-facing settings, “the model said so” is not an acceptable provenance. We build retrieval that logs which passages produced each answer, cites them to the reader where appropriate, and refuses questions that fall outside the approved knowledge — a design compliance can sign, in writing.
The first demo impressed everyone for a week
A prototype bot wired to a folder of PDFs looks convincing until real users arrive with real phrasing. The distance from demo to production is evaluation sets built from your actual questions, freshness pipelines that track document changes, escalation rules, and cost control at real volume — engineering work, and exactly the work we sell.
Trust in an assistant is built one verifiable answer at a time. Retrieval with provenance means every reply can be traced to the document that produced it — by your team, your auditor, or the customer reading it.
What we build
A grounded assistant is a system, and each part of it is a deliverable you can point at.
The knowledge base, made retrievable
Ingestion from your real sources — docs, wikis, tickets, databases — with chunking, embeddings and permissions-aware indexing chosen for your content, and a pipeline that re-indexes as documents change so the assistant answers from the current version.
You get: a retrieval pipeline that stays current as your knowledge changes.
Retrieval you can audit
Every answer records which sources served it. Readers see citations where you want them shown; administrators see the full trace — question, retrieved passages, answer — for any conversation. Provenance is a feature, designed in from the first day.
You get: per-answer provenance your compliance owner can inspect.
The assistant itself
The conversational layer on the channels where your questions actually arrive — website, helpdesk, Slack, internal tools — with your tone, defined refusal behaviour for out-of-scope questions, and a clean handover that gives your team the whole conversation, never a cold start.
You get: an assistant on your channels with graceful human handover.
Evaluation before anyone external meets it
We build a golden set of real questions from your tickets and inboxes, score the assistant against it, and re-run the set on every change — so accuracy is a measured number that moves visibly, and launch is a decision made on evidence.
You get: a scored evaluation report before launch, re-run on every change.
Operations and cost control
Token budgets, caching for repeat questions, model choices sized to each step, and monitoring that shows usage, answer quality and spend per channel — so the assistant’s running cost is a line item you can read, at your volumes.
You get: a monthly usage, quality and cost view per channel.
From knowledge audit to live assistant
A typical first assistant reaches its first live channel in about a month; your sources and channels set the exact path.
Week 1 · Knowledge and channel audit
Where the questions arrive, where the answers live, who owns each source. We take read-only samples under NDA and pull the real questions your team answered last month — they become the evaluation set.
Weeks 2–3 · Retrieval and first answers
Ingestion and indexing built on your actual content, then the first grounded answers against the evaluation set. You see accuracy as a number from the first demo, with every answer traceable to its sources.
Week 4 · Hardening and boundaries
Refusal rules for out-of-scope questions, escalation to humans, tone and citation display, permissions checks. The design is walked past your compliance or data owner while changes are still cheap.
Week 5+ · Launch and tuning
Live on one channel first, watched daily, then widened by evidence. The evaluation set keeps growing from real conversations, so quality is re-measured continuously rather than assumed.
The people who design your retrieval are the people on your calls — no account-manager layer, and a senior architect signs off the system design before development starts.
Proof, not claims
The team behind your assistant ships grounded, voice- and avatar-fronted AI systems in production. The discipline is the same across all of them: the model answers from evidence, the evidence is logged, and quality is a measured number.
Every answer arrives with its sources
Our retrieval designs log the passages behind each answer and surface citations to the reader where the use case calls for it. When a stakeholder asks “where did the bot get that?”, the trace is one click away — question, retrieved evidence, answer, timestamp.
Measured against real questions before launch
Every assistant we ship is scored against a golden set built from the questions your team actually received, with refusal and escalation behaviour tested alongside accuracy. The score is re-run on every change, so quality moves visibly instead of anecdotally.
An assistant that cites its sources turns every answer into a checkable claim — that is the difference between a chatbot your team tolerates and one it trusts.
“Great overall experience! Excellent communication at every step from initial consultation to incremental delivery, we were able to work around our time-difference without any difficulty. The development was delivered to expectation and in a professional manner. I look forward to working with this team again.”
Chatbots are the newest surface on an old discipline: take a messy body of company knowledge, structure it, and serve the right piece at the right moment. We have built that pipeline for 200+ clients across ten years.
Scope your assistant on a free call
Bring the channel where the questions pile up and one or two documents the answers should come from. In 30 minutes an engineer maps your knowledge sources, flags what retrieval needs before it can be trusted with your audience, and gives you an honest read on whether a chatbot, a RAG pipeline or an agent fits the job.
A 30-minute working session with an engineer, not a sales call. You keep the notes either way, and nobody follows up more than once.
When you need an agent instead
A chatbot answers; an agent acts. If the job ends with a reply — a support answer, a policy lookup, a product recommendation — a grounded chatbot is the right tool and usually the cheaper one to run. If the job continues after the reply — updating the CRM, issuing the refund, booking the appointment, chasing the missing document — you are describing an agent with defined boundaries. We build both, so the recommendation follows the job rather than the fashion. See AI Agent Development for how an agent engagement runs.
Clients also buy
AI Agent Development
Production agents with defined boundaries and measurable ROI — not demos. An Agent Pilot in 30 days.
AI Integration
AI into your existing product and workflows this quarter — scoped as product features with usage metrics attached.
Voice AI Agents
Phone, support and sales calls answered 24/7 by a voice agent that knows your business and hands off cleanly.
Your knowledge already holds the answers.
The questions buyers actually ask
What is RAG, in plain terms?
Retrieval-augmented generation. Before the model answers, the system retrieves the most relevant passages from your own documents and hands them to the model as the material to answer from. The model writes the reply; your knowledge supplies the facts. That is what keeps answers specific to your business and checkable against real sources.
Can the assistant answer only from our documents?
Yes — that is the default design. The assistant answers from the approved knowledge base, cites what it used, and politely declines questions that fall outside it, offering a human handover instead. How strict that boundary is set depends on your audience and risk profile, and it is agreed in writing before launch.
How do you keep answers accurate over time?
Three mechanisms: an evaluation set built from your real questions that is re-scored on every change, a freshness pipeline that re-indexes documents as they change, and monitoring of live conversations with escalation for low-confidence answers. Accuracy is treated as a number to maintain, and it is reviewed on a schedule.
Which channels can it live on?
Wherever your questions arrive: your website, a helpdesk such as Zendesk or Intercom, Slack or Teams for internal use, WhatsApp, or embedded inside your own product. The same knowledge base can serve several channels with different tone and permission settings per channel.
What does it cost to run?
Running cost depends on conversation volume and how much retrieval each answer needs. We model it at your volumes before launch and engineer it down with caching and right-sized models per step, so the monthly figure is a line item you approve in advance rather than a surprise on an invoice.
What happens to our data?
Sources are shared under NDA, read-only, and indexed with your permission model respected. We agree before the build which data may reach third-party model APIs and which must stay inside your infrastructure, and the boundary is documented so your compliance owner can sign it.
What AI can actually carry in your business — the one-page version
The nine AI & Agents services on one printable page: what each one is, the situation it answers, and the first engagement that proves it. Built to be forwarded to whoever holds the budget.
Your knowledge already has the answers
Book the free 30-minute session and an engineer maps where your answers live today, what retrieval would take, and whether a chatbot is even the right build — you keep the written notes either way. Prefer to write first? Two sentences about where the questions pile up, and an engineer replies within one business day.
- 30 minutes, an engineer on the call
- You keep the written notes either way
- Nobody follows up more than once
