Agentic Unconference 2026 - September 19, Chennai
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We build custom AI chatbots that answer from your data, work inside tools like Salesforce, and hand off to people cleanly. Senior engineers who deliver for enterprises test every bot before launch, so your customers never get a confident wrong answer.
AI chatbot development services cover the design, build and integration of chatbots that answer questions and complete tasks from your own data. Most now run on large language models. The work spans conversational AI consulting, search over your content, links to CRM and service desk systems, accuracy testing, security review and support after launch.
✓ Your team answers the same questions all day. Most tickets or internal requests repeat, and the answers already sit in policies, docs or a knowledge base. A grounded chatbot clears that queue and leaves your people the hard cases.
✓ One answer lives in three systems. Customers and staff shouldn't have to check the CRM, the order system and a PDF to answer one question. A chatbot can query all three with permissions intact and reply in one place.
✓ Requests end in a simple action. Resetting access, changing a booking or checking a claim status all end in one system update. The bot can confirm the request and complete it through an API instead of opening a ticket.
✓ You need cover across time zones and languages. Global customers and shift-based teams need answers at 2 a.m. and in more than one language. A chatbot gives that coverage without staffing every hour.
> Your help desk already ships an AI agent. Does your support run on Zendesk, Intercom or Salesforce? If you only need answers from that knowledge base, switch on Zendesk AI agents, Intercom Fin or Agentforce first. Build custom when you hit their limits on data, integrations or control.
> Nobody is actually in a conversation. Some jobs just move data between systems when something happens, like an invoice landing or a status changing. A chat window adds nothing there. Trigger-based n8n workflow automation does that job for less.
> People want the document, not an answer. Sometimes users need the source file, such as a contract, a drawing or a signed policy. Enterprise search on Azure AI Search or Elasticsearch is cheaper there, and easier to trust. Add a chatbot later, once people start asking questions the documents answer.
We build customer-facing or internal assistants that answer from your documents, tickets and product data. Retrieval-augmented generation ties each answer to a source. We design the handoff path and refusal rules before writing prompts, so the bot knows what it must never answer.
We move intent-based bots off Microsoft Bot Framework, Dialogflow ES, IBM watsonx Assistant or Rasa to an LLM-based design. We keep the flows that work and retire brittle decision trees. Both bots run side by side before cutover, as in any application modernization project.
Your chatbot runs inside your own AWS, Azure or Google Cloud account, with private endpoints and single sign-on. The bot retrieves only what the signed-in user may see. Our cybersecurity and compliance team reviews every data flow.
We connect the bot to where work gets done: CRM, service desk, ERP and channels like Teams or WhatsApp. Each system becomes a governed, logged tool through its API or the Model Context Protocol. The same tools carry straight into AI agent development.
A test set of real questions with approved answers runs on every prompt, model or content change. If accuracy drops, the release stops. After launch, our DataOps and MLOps team tracks answer quality, handoff rate and cost per conversation.
We add a chat panel to the portal, CRM or internal tool your users already open every day. Nobody needs another app or login. We start with one high-volume task and use your existing APIs and sign-in, so nothing needs a rebuild.
24%
reduction in manual support responses from automated bot integration
Rebuilt the web platform for a SaaS provider that simplifies compliance for fintechs and banks, and added an AI compliance chatbot to take routine support questions off staff.
5+
Languages Supported across voice and text interactions
Built an AI study assistant for an ed-tech platform that breaks down topics, answers follow-up questions, runs tests and mock exams, and works in voice and several languages.
60%
Cut manual effort by 60% Dropped average response time to under 2 minutes for common queries
Automated ingestion, categorization and replies for a large insurer's 200K+ yearly customer emails, connected to Salesforce, with human-in-the-loop controls for oversight.
Get a 20-minute technical read from a senior engineer. No pitch deck, no sales team.
An AI chatbot has two costs: the one-time build and the monthly cost of running it. The build depends mostly on integrations, content cleanup and the accuracy testing your use case needs. Running cost covers model usage, hosting, the search index and tuning, and it grows with traffic. A scoped pilot on one use case is the fastest way to get a real number, including your cost per resolved conversation.
Rule-based chatbots follow scripts, AI chatbots understand free-form questions, and AI agents complete multi-step tasks across systems. Rule-based bots are predictable but break when users phrase things differently. AI chatbots use a large language model to answer from your content, so they need grounding and testing to stay accurate. Agents can finish work end to end, which means tight permissions, audit logs and approval steps for anything that changes a record.
You stop wrong answers by grounding the chatbot in approved sources, limiting what it may answer, and routing unclear questions to a person. Retrieval-augmented generation ties each answer to your documents. Citations let users check the source. Confidence thresholds send low-certainty or high-risk questions to a human agent, with the conversation attached. Most wrong answers trace back to outdated or conflicting content, so fixing the source material is part of the build.
Enterprise model services such as Amazon Bedrock and Azure OpenAI don't use your prompts, outputs or documents to train their models. Both say so in their published data policies. Your documents stay in your own cloud account. The chatbot applies the same access rules your systems already enforce. For regulated data, the design also covers retention periods, masking personal data before it reaches the model, and logging every retrieval for audit.
Most existing chatbots can move to an LLM-based design without starting over, because their intents, flows and logs already show what users ask. Microsoft ended long-term support for the Bot Framework SDK in December 2025. The recommended replacement is the Microsoft 365 Agents SDK, so those bots are the most urgent to move. Fixed steps such as payments or identity checks usually stay as scripted flows, while open-ended questions go to the LLM. The old bot's conversation logs then become the first test set for the new one.
Tell us the shape of the problem. A senior engineer reads it and replies. You won't get a templated capability deck.
