Enterprise chatbots your security team will actually approve.
BroutonLab's enterprise AI chatbot development service builds bots that pass the reviews consumer tools fail: self-hosted models inside your perimeter, SSO and role-based access, audit trails, and live integration with your systems of record. Built and operated by a dedicated senior team.
The demo isn't the hard part. The review is.
Any team can wire a model to a chat window in a week. Enterprise deployments die later — in security review, in procurement, in the integration with a fifteen-year-old ERP nobody wants to touch. That's the part we're built for: our engineers have shipped ML into production environments since 2017, and self-hosted deployment isn't a special request for us — it's a checkbox.
Four things your review will ask about.
Your data stays inside your perimeter
Self-hosted models on your infrastructure when policy demands it — prompts, documents, and transcripts never leave your environment. We've shipped systems built exactly this way.
Grounded in your systems, not just your docs
Retrieval over knowledge bases plus live integration with the systems of record — ERP, CRM, ticketing, internal APIs. The bot answers from what's true right now.
Access control that mirrors yours
SSO, role-based access, per-department knowledge boundaries. The chatbot respects the same permissions your people have — finance doesn't leak into support.
Audit trails & evals
Every answer traceable to its sources. Accuracy evals on your real queries before launch, monitoring and drift alerts after. Built for the compliance review, not around it.
Built to survive the review, step by step.
Most enterprise AI chatbot development services die in security review, not in code. Ours is sequenced so the review is part of the plan:
- 01
Paper first
Mutual NDA, architecture documentation, data-flow diagrams — before anyone asks for system access. Your security team talks to the engineers who will build the system, not to a sales layer.
- 02
Pilot inside one department
$30–50K fixed, 2–4 months, one workflow, scope in writing. A bounded line item procurement can approve without a committee — and a working system your stakeholders can poke at.
- 03
Pass the review together
SSO and role-based access wired to your identity provider, audit trails demonstrated on real logs, support during penetration testing. We sit on your side of the table for this part.
- 04
Roll out and operate
More departments join on the same core. Monitoring, drift alerts, access reviews, and a roadmap — run by the dedicated team that built the pilot.
Procurement-friendly by design.
Start with a fixed-price pilot ($30–50K, 2–4 months): one department, one workflow, scope in writing, mutual NDA, your IP from day one, 30-day notice both ways. A bounded line item your procurement can approve without a committee. Rollout scales from there with a dedicated team from $15K/month — details on how we work. For the broader chatbot practice, see AI chatbot development.
What enterprise buyers ask us.
Can the chatbot run fully on our infrastructure?
Yes. For sensitive data we deploy open-weights models inside your perimeter — cloud VPC or on-prem — so prompts, documents, and logs never leave your environment. Where policy allows commercial LLMs, we set up the enterprise agreements and data-retention controls properly.
How does an enterprise AI chatbot development service engagement start?
With a fixed-price pilot: $30–50K, 2–4 months, one department or workflow, scope agreed in writing. It's deliberately procurement-friendly — bounded budget, mutual NDA, your IP from day one, 30-day notice both ways. Enterprise-wide rollout follows only if the pilot earns it.
How do you handle security review and compliance?
We work inside your review, not around it: architecture documentation, data-flow diagrams, and access to the engineers who built the system — not a sales layer. Honest note: we bring engineering controls (isolation, audit trails, RBAC); certification of your deployment stays with your compliance team, and we build to make that job easy.
Can it integrate with our legacy systems?
That's usually the real project. Modern LLMs are the easy part; the value is wiring them safely into systems that predate them — ERP, ticketing, internal databases. Our team has done production system integration since 2017, before and after LLMs.
What does it cost beyond the pilot?
Ongoing dedicated teams run $15–65K/month depending on scope. Enterprise costs are driven by integration count, self-hosting requirements, and how many departments the rollout covers — we scope those explicitly, in writing, before you commit.
Who is on the team during an enterprise rollout?
As an enterprise AI chatbot development company we keep the team continuous: the same 3–8 engineers who built your pilot run the rollout, joined by an ML engineer for evals and an infrastructure engineer when self-hosting is involved. No handoff to a separate delivery org — the people your security team interviewed are the people who ship.
What happens after go-live?
The system is watched, not abandoned: response-accuracy monitoring against the eval suite, drift alerts when your documents or systems change, periodic access reviews, and a visible roadmap for the next departments. Enterprise chatbots degrade quietly when nobody owns them — ownership is part of the engagement.