AI chatbot development services
Custom AI chatbots for WhatsApp, Telegram, Instagram, Slack, and web. RAG on your docs. CRM-integrated. Shipped in 3 to 8 weeks — not 3 to 8 months.
Decision-tree bots break the moment a real customer types.
Most chatbot projects fail because they ship a scripted decision tree and call it AI. The first customer who phrases a question differently from what the designer anticipated falls off the tree — and the bot responds with "Sorry, I didn't understand. Please rephrase." That's the moment trust dies.
A real AI chatbot uses a language model to reason over your documents and past conversations in real time. It handles questions you didn't anticipate — correctly, with a citation. It knows when it doesn't know and escalates clean. It reads the customer's record in your CRM before the first reply and writes the conversation summary back when it ends.
That's the bar. It's not hard to describe. Most shipped chatbots fail it because they were built on the wrong foundation.
We build on the right foundation. Retrieval-augmented generation over your actual knowledge base. Schema-guided response formatting that rejects malformed answers. Confidence thresholds that escalate instead of guess. And the boring production engineering — observability, rate limiting, prompt injection defense, conversation state — that turns a demo into a system that runs on Black Friday.
One brain. Every channel your customers use.
Same core logic, channel-specific UI and handoff rules. No forked codebase.
Six types of AI chatbot we build.
Picked based on your traffic volume, support load, and how much of your knowledge lives in docs vs. tribal memory.
24/7 customer support chatbot
RAG on your help center, past tickets, and product docs. Answers 80%+ of inbound questions autonomously. Escalates with full conversation context when it can't.
Typical deflection: 75–85% of inbound chat trafficLead qualification & booking chatbot
Qualifies inbound web traffic in natural conversation, pushes qualified leads to your CRM, books meetings on your calendar. Replaces most "contact us" forms.
Typical lift: 2–4x meeting-booked rate vs. static formProduct & order chatbot
RAG on your product catalog. Answers sizing, stock, shipping, and return questions on WhatsApp and web. Order-status lookups via your Shopify or custom backend.
Reference: ~70% pre-purchase question deflection on WhatsAppInternal knowledge-base chatbot
Slack-native assistant grounded on Notion, Confluence, Google Drive, and past tickets. Replaces "Hey team, where's the doc for…" pings.
Typical outcome: −40% to −60% repeat internal questionsLead-gen chatbot for services
Captures and qualifies inquiries on Instagram DM and WhatsApp for real estate, clinics, and service agencies. Books viewings, consultations, and callbacks.
Reference: 24/7 capture, same-minute response SLAMulti-tenant chatbot platform
White-label chatbot product for agencies and SaaS platforms serving multiple end-clients. Shared RAG infrastructure, per-tenant knowledge isolation, usage billing.
Built for: agencies · PaaS · vertical SaaSThe same word, two very different products.
The distinction matters because one breaks on real customers and one doesn't.
Traditional chatbot
Scripted decision tree
- Every possible path has to be designed manually
- Breaks on any question outside the designer's script
- Can't cite a source — the "answer" is a hardcoded string
- Updating the bot = editing the tree, redeploying, retesting
- Adding a new channel means rebuilding the conversation flow
AI chatbot (RAG-based)
Language model + retrieval over your docs
- Handles phrasings you didn't anticipate — correctly
- Cites the exact document and section it pulled from
- Stays current when you update a doc — no code change
- One core logic deploys to every channel with channel-specific UX
- Conversation summaries flow back to your CRM automatically
Custom GPT or RAG chatbot? Custom GPTs are fast to ship and useful for internal assistants where your team tolerates ambiguity. For customer-facing traffic where the bot must cite exact sources, stay current as your docs change, and avoid hallucinations, the production answer is RAG — retrieval-augmented generation over a vector store your documents are indexed into. We deploy both, and we'll tell you which fits your use case in the discovery call.
Chatbots billed for every month.
The metrics below are from live deployments.
Multilingual player-support chatbot. 80%+ deflection across 7 languages.
RAG on the operator's help center and bonus rules, fronted by GPT-4o. Deployed on web and Telegram. Handles bonus questions, account issues, and payment status. Hands off to human agents with full conversation context when the player asks.
Customer reactivation chatbot. 3.5x ROI, 192K customer intervals analyzed.
Segment-specific messaging driven by "point of no return" analysis (VIP 163d · Loyal 415d · Newcomers 29d) and uplift modeling. GPT-4 writes personalized reactivation copy. Reduced daily outbound from 4,000 to 1,200 messages at higher ROI.
Patient recall chatbot. 18.7% reactivation across 23 treatment categories.
Treatment-aware recall windows combined with GPT-4 personalized messaging. The chatbot schedules appointments, collects insurance info, and routes emergencies — on WhatsApp and web, with HIPAA-safe PHI handling.
Chatbot projects come in three shapes.
No hourly billing on development. Fixed scope, fixed price. We model ongoing API costs in the discovery workshop so you know unit economics before you commit.
| Engagement | Scope | Price | Timeline |
|---|---|---|---|
| Discovery workshop | Use-case audit, knowledge-base assessment, architecture doc, fixed-scope proposal | $1,500–$3,000 | 1 week |
| Simple FAQ chatbot | Single channel, RAG on up to 200 docs, basic handoff | $3,000–$6,000 | 2–3 weeks |
| Multi-channel RAG chatbot | Web + WhatsApp + Telegram, CRM integration, human handoff, analytics | $8,000–$18,000 | 5–8 weeks |
| Enterprise chatbot | Full CRM + analytics, custom integrations, SLAs, HIPAA/GDPR scoping | $20,000–$40,000 | 8–12 weeks |
| Monthly retainer | Ops, prompt tuning, new knowledge, new channels, observability | $1,500–$6,000/mo | Post-launch |
Per-conversation API costs typically land between $0.01 and $0.05, depending on LLM tier, RAG chunk size, and average conversation length. We model this specifically for your volume in the discovery workshop.
From discovery to production in 3–8 weeks.
Weekly demos on real data from week one. No month-long silences.
Discovery workshop
Paid audit of your use case, knowledge-base quality, and traffic patterns. You get an architecture document and a fixed-price proposal. If you don't move forward, you keep the document.
Build
RAG pipeline, channel integrations, CRM wiring, and guardrails. Weekly demos on your real documents and real questions. Daily Slack access.
Shadow mode
The chatbot runs on real traffic with human review of every response. We measure deflection rate, escalation quality, and hallucination events before cutting to live.
Production
Live traffic, full observability via LangSmith or Helicone, 30-day tuning window included. Optional retainer for new intents and knowledge updates.
Chatbot questions we answer on every call.
How much does a custom AI chatbot cost?
What's the difference between an AI chatbot and a traditional chatbot?
Do I need a custom GPT or a RAG chatbot?
Which channels can the chatbot deploy to?
How long does it take to build a custom AI chatbot?
How do you prevent hallucinations?
Can the chatbot integrate with our CRM?
Who owns the chatbot we build?
Ready to ship a chatbot that doesn't embarrass you on Monday?
One 20-minute call. We'll look at your knowledge base, your channels, your traffic, and tell you what's realistic — and what isn't. If you'd be better off with a different team, we'll say so.