Solution

AI Chatbot Development

A chatbot that draws its answers from your own documentation, performs real actions in your systems and recognizes when to pass a conversation to a person.

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How we work

What an AI chatbot is allowed to know and do sets the limit on its quality. A well-built one answers support questions from your real help center content, looks up an order or account status through your existing systems, and passes a conversation to a human agent with full context as soon as it is out of its depth. A poorly built one invents answers with confidence and leaves a frustrated customer in a loop with no exit.

We build the retrieval, the guardrails, and the escalation path as one system, deployed to whichever channels your customers actually use, whether that is a website widget, WhatsApp, Slack, or Microsoft Teams.

Scope

Core features

Multi-channel deployment
The same chatbot runs on a website widget, WhatsApp, Slack, or Microsoft Teams, sharing conversation history and logic across channels rather than being rebuilt separately for each one.
Answers grounded in your own content
Retrieval-augmented generation pulls from your help center, product documentation, and internal knowledge base, so answers are based on what you actually publish rather than the model's general training data.
Structured actions, not just answers
The chatbot can look up an order status, check an account balance, or trigger a defined workflow through your existing APIs, going beyond answering questions into taking a real action.
Escalation to a human agent with full context
When a conversation needs a person, it hands off to your support desk with the full conversation history attached, so the customer never has to repeat themselves.
Guardrails against hallucinated or off-topic answers
The bot is scoped to answer from its grounded sources and declines or escalates questions outside that scope, rather than generating a plausible-sounding answer it cannot back up.
Conversation logging and analytics
Every conversation is logged with outcome tracking, so you can see deflection rate, common unanswered questions, and where the bot is escalating most often.
Multilingual support
The same knowledge base and action set can serve customers in multiple languages without maintaining a separate bot per language.
Human review for flagged conversations
Conversations the bot marks as low-confidence or sensitive get queued for human review, catching quality issues before they show up as a pattern across many customers.

Architecture

Choices that shape the build

Retrieval architecture and vector store choice
The quality of answers depends heavily on how content is chunked, indexed, and retrieved before it ever reaches the model, which matters more to accuracy than which underlying model is used.
Escalation logic that does not trap the user
The bot needs a clear, low-friction path to a human agent, with a limit on how many times it will retry an answer before escalating, so a confused user is never stuck looping with no way out.
Cost control on model usage at scale
Token budgets, response caching for repeated questions, and using a smaller model for simple queries keep per-conversation cost predictable as volume grows.
Data privacy for customer conversations
Conversation logs often contain personal information, so retention periods, access controls, and what gets sent to a third-party model provider need to be defined up front, not discovered during a compliance review.
An evaluation loop for answer quality over time
A held-out set of real questions gets checked against the bot's answers on a regular basis, since model updates and new content can silently shift answer quality without anyone noticing.

Build vs buy

When to build and when to buy

Intercom Fin, Zendesk's AI agents, Drift, and Ada are strong choices when you already run support through that platform and mainly need FAQ-style deflection wired to your existing help center content. They are fast to set up and priced per resolution, which is often cheaper than a custom build for that specific job.

A custom build earns its cost when the chatbot needs to take actions inside your own systems that those platforms cannot reach, reason over proprietary or structured data those platforms cannot ingest well, or when you need direct control over the model choice, the escalation logic, or how conversation data is stored and retained.

Delivery

How delivery is phased

Define scope and knowledge sources
We identify what the bot should and should not handle, and which documentation, APIs, and systems it needs access to in order to answer and act on real requests.
Build retrieval and guardrails
The retrieval pipeline over your content and the guardrails that keep answers scoped and escalate low-confidence responses get built and tested before any channel integration.
Integrate channels and escalation
The bot gets deployed to the channels you use and connected to your support desk for handoff, with conversation history passed along automatically. This typically takes a few weeks per additional channel.
Pilot with human review before full rollout
A pilot period with active human review of flagged conversations catches quality issues while volume is still manageable, before the bot handles the full support queue.

Technology

Stack we usually choose

FAQ

Questions about AI Chatbot Development

Can an AI chatbot answer questions using our own documentation?

Yes, that is the core of how we build it. Retrieval-augmented generation indexes your help center and internal documentation, and the chatbot answers from that indexed content rather than from general model knowledge, which keeps answers accurate to what you actually publish.

How do you stop an AI chatbot from making up answers?

Guardrails scope the bot to answer only from its grounded sources and require a minimum confidence level before responding. Questions outside that scope, or where confidence is low, get escalated to a human agent instead of receiving a generated guess.

Can an AI chatbot hand off to a human support agent?

Yes. When a conversation needs a person, whether the customer asks for one or the bot's confidence drops below its threshold, it hands off to your support desk with the full conversation history attached, so the customer does not have to repeat their question.

What does an AI chatbot cost to run at scale?

Cost scales with conversation volume and model usage, controlled through token budgets, caching answers to repeated questions, and routing simple queries to a smaller, cheaper model. We size this during scoping based on your expected conversation volume.

Is Intercom Fin enough or do we need a custom AI chatbot?

If you already run support through Intercom or Zendesk and mainly need FAQ deflection from your help center, their built-in AI agents are usually the faster and cheaper path. Build custom when the bot needs to take actions in your own systems or work with data those platforms cannot access.

Services

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