Custom AI Chatbot Development Services

We build customer support bots, internal knowledge assistants, lead qualification bots, onboarding guides, and e-commerce product advisors. Each is purpose-built for a specific workflow rather than being a generic chat widget with an LLM behind it.

Rule-based bots follow rigid decision trees and break the moment a user phrases something unexpectedly. LLM-powered bots understand natural language, handle novel questions, and can reason across multiple turns of conversation. They still need guardrails — which we build in — but they handle real user behaviour far more gracefully.

We ground every response in your verified content using RAG — the bot cites specific documents rather than generating from general knowledge. We add output validation, confidence thresholds, and automatic escalation to a human agent when certainty is low. We also build evaluation suites that catch quality regressions before any update goes live.

Yes. We integrate with Salesforce, HubSpot, Zendesk, Intercom, Freshdesk, and custom APIs. The bot can look up customer records, create tickets, update deal stages, and trigger workflows — all within the conversation without requiring the user to switch context.

The bot supports any language the underlying LLM handles — which for GPT-4o and Claude covers 50+ languages natively. We deploy on web, iOS, Android, WhatsApp, Slack, and Microsoft Teams. Multi-channel bots share the same backend engine, so knowledge and conversation history stay consistent across touchpoints.

A focused single-channel support bot takes 4–8 weeks from kickoff to production. Multi-channel bots with deep CRM integration and custom analytics take 8–14 weeks. The biggest variable is the state of your existing documentation and knowledge base — well-organised content dramatically shortens the timeline.

A single-purpose chatbot (e.g. support bot for one product) starts at $12,000–$25,000. A full-featured multi-channel assistant with CRM integration and analytics runs $35,000–$80,000. Ongoing model inference and hosting costs are typically $200–$2,000/month depending on conversation volume.

Out of the box, LLMs do not self-update, but we build continuous improvement loops into every project. Conversation logs feed a review workflow where low-confidence or escalated interactions are used to expand the knowledge base and refine prompts. We schedule quarterly evaluation reviews to measure and act on quality trends.

Absolutely. We design escalation logic that detects sentiment, topic sensitivity, or low confidence and seamlessly transfers the conversation to a live agent — with full context passed along so the agent does not have to ask the user to repeat themselves.

We implement sliding-window memory for within-session context and persistent summarised memory for returning users. Long-term memory is stored in your database and retrieved at session start, giving the bot awareness of past interactions without ballooning token costs.

Yes. Our chat UI components are built to WCAG 2.1 AA standards — keyboard navigable, screen-reader compatible, and colour-contrast compliant. We also implement GDPR-friendly session handling with configurable data retention policies and explicit consent flows.

You get a live dashboard covering resolution rate, containment rate, average turns to resolution, escalation rate, and user satisfaction scores. We also surface the top unanswered questions so your content team knows exactly where to invest in documentation improvements.