Chatbots that act, not just answer.
BroutonLab runs a full AI chatbot development service: we design, build, and operate conversational agents grounded in your data — customer support bots, internal copilots, and chat + voice systems that call real tools. As an AI chatbot development company we build to production standards: evals, monitoring, escalation paths.
From first message to closed ticket.
Customer-facing chatbots
Support and sales bots grounded in your own data with retrieval (RAG) — so answers come from your docs and policies, not the model's imagination.
Internal copilots
Assistants over your knowledge base, CRM, and internal tools — the fastest way to give every employee your best employee's answers.
Chat + voice in one system
The same agent brain behind web chat, WhatsApp, and a phone line. Our voice stack already makes thousands of calls a day in production.
Integrations that do things
A chatbot that only talks is a FAQ page. Ours call tools: book appointments, update your CRM, check orders, escalate to a human with full context.
Evals, tuning & operations
Response-accuracy evals, hallucination control, latency budgets, monitoring. The unglamorous 60% that separates a demo from a system.
How our AI chatbot development services work.
- 01
Scope the bot's job
One workflow the bot must own end to end — refund requests, appointment booking, tier-1 support. Success metrics agreed in writing before any code: resolution rate, handoff rate, time to answer.
- 02
Ground it in your truth
Retrieval over your docs, policies, and product data. The bot answers from what's true in your company — and refuses gracefully when the answer isn't there.
- 03
Wire the actions
CRM updates, calendar booking, order lookups — the tool calls that turn answers into outcomes. Every action gets guardrails and an audit trail.
- 04
Eval before launch
Accuracy on your real questions, adversarial cases, latency and cost budgets. The bot meets your customers only after it beats the measuring stick.
- 05
Launch one channel, then expand
Web chat first, most often. WhatsApp, Slack, or a phone line come later on the same core — and the AI chatbot development company that built the pilot stays to run the rollout.
Model-agnostic by policy.
Commercial LLMs, open-weights models, or both — chosen per problem, not per partnership. Retrieval over your data, vector search where it earns its keep, and self-hosted deployment when your data can't leave your environment. The vendor landscape shifts every quarter; your product shouldn't depend on one bet. The deeper engineering behind this — custom AI development — is our core service.
Conversational systems in production.
Agent driven by plain English
A sourcing agent that turns one English sentence into a search across millions of candidates — running daily inside a hiring platform.
Read the case →Voice agent at scale
Thousands of phone conversations a day for a US client — same conversational core, different channel. Tool calling, language switching, calendar booking.
Case study comingPilot first. Scale when it earns it.
Every engagement starts with a fixed-price pilot ($30–50K, 2–4 months): a production chatbot on one channel, grounded in your data, with accuracy evals before launch. If it works, the same team stays and scales it — from $15K/month. Details and team tiers — how we work. Rolling out inside a large organization? See enterprise AI chatbot development. Need the bot to own whole workflows, not just conversations — AI agent development.
Questions founders actually ask.
How much does AI chatbot development cost?
A typical AI chatbot development service engagement starts as a fixed-price pilot: $30–50K for 2–4 months — a production chatbot on one channel, grounded in your data, scope agreed upfront. Ongoing teams run from $15K/month. The main cost drivers: how many systems the bot has to act on (CRM, calendars, order data) and whether models must run on your infrastructure.
What's the difference between a chatbot and an AI agent?
A chatbot answers; an agent acts. If the bot only needs to answer questions from your docs, that's a smaller build. If it should book, update, escalate, and follow multi-step workflows — that's agent territory, and it's most of what we build. The line matters because it drives cost and architecture.
Can it answer only from our own data, without making things up?
That's the point of retrieval-grounded design: the bot answers from your documents and refuses when the answer isn't there. Honest caveat — no vendor can promise zero hallucinations. What we do promise is measurement: accuracy evals on your real questions before launch, and monitoring after.
Which channels can it run on?
Web chat, WhatsApp, Slack, Telegram, email — and phone, since we build voice agents on the same core. One brain, many channels, so answers stay consistent everywhere.
How long until it's live?
A production-grade chatbot on one channel is a 2–4 month pilot. A demo takes a week — but a demo hasn't met your real customers, your messy data, or your compliance review. We build for the second meeting, not the first.
Do you build chatbots for websites?
Yes — an AI chatbot development service for websites is the most common starting point: a support widget grounded in your docs and policies, live on your site in the first pilot. The same core then extends to WhatsApp, Slack, or a phone line without rebuilding the brain.
Can the bot hand off to a human?
It must — a bot with no exit is how companies end up in screenshots. Ours escalates with the full transcript, the customer's context, and the reason it stepped aside, so your agent doesn't start from zero. When to escalate is a threshold we tune together during evals.
What do we need to prepare to start?
Less than most expect: access to the docs and policies the bot should know, a sample of real customer conversations, one system it should act on (CRM, calendar, helpdesk), and a product owner who can give us a couple of hours a week. Messy docs are normal — cleaning them up is part of the work.