Enterprise AI Chatbot Development Services

Cost tracks with scope, channels, integrations, and compliance depth. A focused single-channel chatbot typically runs $15,000–$40,000; a multi-channel, RBAC-aware chatbot with system integrations and full audit/compliance runs $40,000–$120,000+. We usually start with a fixed-price PoC (typically $12,000–$20,000) that proves value on your real knowledge in one channel before you commit to the full build, and every engagement opens with a free 30-minute discovery call.

A production-ready enterprise chatbot typically takes 10–14 weeks: roughly 2 weeks of discovery and conversation design, 5–6 weeks of knowledge/model foundation and channel build, 2 weeks of guardrails, RBAC, and compliance, then pilot and production. A scoped PoC runs in about 6 weeks. The biggest variable is knowledge readiness — clean, current content is fast, while scattered or outdated content adds time we'll flag upfront.

It answers from your documents, not from the model's imagination — that's the core of an enterprise chatbot. We connect it to your documentation, policies, wikis, and knowledge base and use retrieval-augmented generation so every response is grounded in your material with a citation the user can check; when retrieval finds nothing relevant, the bot says it doesn't know and escalates rather than guessing. We agree an acceptable error rate and test against a hallucination benchmark before launch, and as your content changes the answers change with it.

Permissions are enforced at the retrieval layer, not just asked for in a prompt: the chatbot authenticates each user through your identity provider and can only retrieve content that user is authorised to access, so no clever phrasing extracts an answer they're not entitled to, and every answer is logged against the person who received it. On top of that we add PII detection and handling, content guardrails and topic boundaries, encryption, and data-residency controls, with a complete audit log of every conversation. It's designed to pass enterprise security review and supports GDPR, HIPAA, SOC 2, and ISO 27001 requirements depending on your context.

Yes — and good escalation is what makes people trust the bot in the first place. It detects when it can't help, when a topic is sensitive, or when a user simply asks for a person, and hands off to a live agent with the full conversation context so nobody has to repeat themselves. Escalations route to the right team or ticketing system, not a generic inbox — handoff is treated as a feature, not a failure.

One chatbot can run across your website widget, Microsoft Teams, Slack, WhatsApp, SMS, mobile apps, and custom interfaces, all sharing the same backend, knowledge, and controls — so adding a channel later is a small change, not a rebuild. On language, modern LLM-based chatbots handle many out of the box; we configure and test the specific languages your users need so quality is consistent, and can add voice interfaces for phone and voice-assistant channels where typing isn't practical.

Yes. Beyond answering questions, the chatbot can check an order status, create a ticket, look up an account, or update a record by connecting to your CRM, ERP, helpdesk, and other systems. High-consequence actions are scoped, gated, and logged, and can require user or human confirmation before they execute — so the bot is genuinely useful without ever being able to do something it shouldn't.

Yes — and we engineer them differently, because they're genuinely different builds. Customer-facing chatbots prioritise brand-safe answers, tone, and guardrails; internal assistants (IT, HR, knowledge) prioritise RBAC and access to sensitive systems. The risks and the success metrics differ, so we scope each accordingly — but they share the same underlying foundation, so your second chatbot reuses the first's infrastructure instead of starting over.

With a dashboard tracking the numbers that matter for your use case — resolution rate, ticket deflection, escalation rate, and user satisfaction — against targets we agree before launch. Unanswered and poorly-answered questions surface exactly what to improve next, and every change is tested against past conversations so a fix in one place doesn't quietly break another. Quality is something you can see and steer, not guess at.

Yes — and a chatbot needs it, because one that isn't maintained slowly degrades as content ages and users ask new things. Every engagement includes 90 days of post-launch support (monitoring, tuning, content updates). Beyond that, retainers cover accuracy monitoring, model-update validation, new channels and topics, and ongoing improvement driven by real conversation data — so the bot gets better over time instead of decaying behind a chat icon.

Completely. All code, prompts, configuration, conversation logs, and documentation are yours, unconditionally. We sign NDAs before any discovery conversation, retain no client data after a project concludes, and never use your data or conversations to train models for anyone else. For stricter requirements we work entirely within your cloud environment so we never hold your production data at all.