Why most chatbots fail
Then a real user asks something unexpected.
Most chatbot projects fail for the same three reasons: the bot was trained on general knowledge instead of your company data, there's no guardrail when it doesn't know the answer, and nobody tested it against real inputs before launch.
We build against all three. The bot answers from your data, hands off to a person when it isn't confident, and passes testing before launch. Monitoring then surfaces problems before your customers find them.
Proof
None of these is a chatbot. They show the engineering a bot depends on in production: role checks on every request, approval flows, audit trails and media pipelines on AWS.
20,000+
member records moved off paper
Dalmia Resorts International · Hospitality / Timeshare
Nothing a support executive types changes a member record until a verifier approves it, and the API checks each user's role on every request.
How every change to a Dalmia Resorts member record gets approved →1,100+
automated tests across web, API and mobile
Healthcare marketplace · India
PostgreSQL row-level security keeps each tenant's rows out of every other tenant's queries. The web app is live in beta.
How row-level security keeps each hospital's data apart →15 TB
footage archive on Montage India's NAS, moving to AWS
Montage India · Stock Media / Marketplace
Uploads run through SQS, Lambda and AWS MediaConvert, with an append-only audit table the application can add to but not change.
How Montage India's media pipeline runs on AWS →Use cases
A bot scoped to one job holds up under real traffic better than a general-purpose one.
What's inside every build
Answers from your docs, CRM and catalog, not general AI knowledge.
Confidence thresholds, topic boundaries and human handoff, on by default.
Hundreds of real inputs, scored for accuracy and refusal rate before launch.
Cost, latency, escalation rate and satisfaction, logged from day one.
Web widget, Slack, Intercom or Zendesk, with auth and PII handling.
Every chatbot connects to your indexed documents, so its answers come from your data instead of guesswork.
How it works
01
We map your use case, data and success criteria.
02
We connect your data and define what a good answer looks like.
03
We build and tune until test pass rates hit the agreed threshold. You review each round.
04
Production deploy, live dashboards, and a runbook your team can own.
The number of data sources sets the pace. Our guide to how long a build takes goes phase by phase.
Get a written scope
Use cases, data sources, integrations and acceptance criteria are agreed in writing before we start.
We'll send a written scope and estimate within 48 hours, and nothing is committed until you accept it. For what moves the number, see our AI chatbot development cost breakdown.
AI chatbot development is building conversational software powered by AI models like GPT-4o or Claude. Unlike rule-based bots that follow rigid scripts, AI chatbots understand natural language, handle unexpected phrasing, and generate context-aware answers. Development covers the conversation flow, connecting the bot to your data, adding guardrails, and deploying it into your product or support stack.
Cost depends on the number of data sources, integrations, and deployment target. After a 30-minute discovery call we send a written scope with an estimate of effort, and billing follows the work against it. If the scope changes, we agree a written change order first, so there are no surprise invoices.
A focused chatbot (single domain, one integration) takes 3-4 weeks. Systems with multiple data sources and a custom UI take 6-8 weeks. The timeline covers scoping, build, testing against real inputs and production deployment, so it ends with a live bot rather than a prototype.
Rule-based bots follow fixed scripts: if the user says X, the bot says Y. They break the moment someone phrases a question the script didn't expect. AI chatbots understand intent and handle variation across a conversation. The trade-off is cost per conversation and the need to test for accuracy. For high-volume simple queries, a rule-based bot is sometimes still the right call, and we'll tell you if that applies to you.
Yes. We connect the chatbot to your documentation, CRM records, product catalog or support history, so it answers from your data instead of general knowledge. Bots that need to take actions, such as creating tickets, looking up orders or updating records, get tool-calling too.
We combine three layers: the bot can only answer from your verified sources, confidence thresholds send unclear queries to a human instead of guessing, and we run the bot against hundreds of sample inputs before launch. You get logs of every conversation so problems surface early.
We deploy to web apps (chat widget), internal tools (Slack, Notion, Linear), customer-facing products (embedded in your SaaS), and support platforms (Intercom, Zendesk, Freshdesk via API). We can also build a custom interface to your spec.
Book a 30-minute discovery call. We'll map your use case and put the scope in writing.
