Services
AI agent development
A chatbot answers questions. An agent gets work done: it reads your documents, checks your systems, takes the next step and knows when to stop and ask a person. We build agents with clear limits, tested on your own data before they meet a customer.
What is an AI agent, and what does it cost to build one?
An AI agent is software that uses a large language model to understand a request, look up information, call tools such as your CRM, database or calendar, and complete a task, passing to a person when it should. Infovion Technologies, a software company in Pune, India, builds AI agents for customer support, lead qualification, internal knowledge and back-office work using the Claude and OpenAI APIs, retrieval-augmented generation (RAG) and tool calling. A proof of concept typically costs ₹50,000 to ₹1.5 lakh ($600 to $1,800) and a production agent ₹2 lakh to ₹8 lakh ($2,500 to $10,000), plus model usage.
Updated 11 November 2026 by Infovion Technologies, Pune.
Scope
What we deliver
Customer support agents
Answers from your help centre and past tickets with sources, account look-ups, safe actions through your API and hand-over to your helpdesk.
Sales and lead qualification agents
Instant replies to new leads on WhatsApp, web chat or email, qualifying questions, meeting or site-visit booking and a summary for the salesperson.
RAG chatbots on your documents
Staff or customers ask questions of manuals, policies, contracts and product data, and get answers with the source passage cited.
Voice agents
Phone agents for appointment booking, reminders, order status and first-line enquiries, in English and Indian languages, that identify themselves as automated.
Workflow and back-office agents
Agents that read incoming documents and emails, update the ERP or CRM, prepare reports and raise exceptions for approval.
Agents inside your product
Assistants, natural-language search and task automation added to an existing web or mobile application.
Chatbot, RAG or agent: which do you need?
- Fixed questions with fixed answers: a menu or FAQ bot is enough, and cheapest
- Answers that live in your documents: a RAG chatbot that retrieves and cites them
- Tasks that need your systems, such as booking, updating or checking status: an agent with tools
- Several steps across systems with approvals: a workflow agent with a person in the loop
- Phone calls: a voice agent, usually after the text version works well
How we keep an agent reliable
- Answers must come from retrieved content; the agent declines when nothing relevant is found
- A short, approved list of tools; anything involving money or deletion needs a person
- A test set of real questions run before every release, with pass rates reported
- Every conversation, tool call and hand-over logged for review
- Personal data masked where the task does not need it
- A clear hand-over to staff, with a summary, in the tool they already use
Custom code or n8n, Make and Zapier?
No-code tools such as n8n, Make and Zapier are a good fit for simple flows: a form to a sheet, a lead to a CRM, a notification. We use them where they are enough, because they are quicker and cheaper. We write custom code when the agent needs your own data model, strict permissions, high volume, automated testing or data that must stay on your servers. Many projects combine both.
What we need from you to start
- The task, in one paragraph, and how you would judge a good result
- Sample data: documents, past tickets or conversations, with personal details removed if needed
- Access to a test environment or API for the systems involved
- One person who knows the process and can review the agent's answers
Sources: Anthropic: Building effective agents · Model Context Protocol · OWASP Top 10 for LLM Applications
Deliverables
- Use-case assessment and success measures
- Proof of concept on your own data, with measured accuracy
- Production agent with tools, guardrails and hand-over
- Evaluation test set and report
- Monitoring dashboard: resolution, escalations and cost per conversation
- Data-handling documentation and source code
Typical clients
- Support teams answering the same questions every day
- Sales teams that reply to leads hours late
- Companies with knowledge locked in documents
- SaaS products adding AI features
- Agencies that need an AI development partner
Technologies
Claude API, OpenAI API, Retrieval-augmented generation (RAG), PostgreSQL with pgvector, Model Context Protocol (MCP), Node.js / TypeScript, WhatsApp Cloud API, n8n / Make where suitable
Cost and timeline
Cost of AI agent development
Infovion's indicative prices for 2026, in rupees for clients in India and US dollars for clients abroad. Model usage is billed at cost and estimated before the build.
| Project type | Typical budget |
|---|---|
| Proof of concept on your data | ₹50,000 – 1.5 lakh · $600 – 1,800 |
| RAG chatbot on your documents | ₹1.5 – 4 lakh · $2,000 – 5,000 |
| Production agent with tools and hand-over | ₹2 – 8 lakh · $2,500 – 10,000 |
| Voice agent | ₹2.5 – 7 lakh · $3,000 – 8,500 |
Timeline. Two to three weeks for a proof of concept; six to ten weeks to a production agent.
Factors that affect cost
- Number of systems the agent must read or update
- Volume of documents and how often they change
- Languages and channels: chat, WhatsApp, email, voice
- Accuracy required and the review steps around it
- Where data must be stored and processed
Process
How the project is delivered
01Week 1
Discovery
An initial consultation, followed by a requirements workshop for larger projects, to understand processes, users and objectives.
02Within 2 business days
Proposal
A written scope, milestone plan and fixed-price proposal, so cost and timeline are agreed before work begins.
031–3 weeks
Design
Wireframes and a clickable prototype reviewed with your team before development starts.
042-week sprints
Development
Fortnightly releases to a staging environment, with a review at the end of each sprint.
051–2 weeks
Testing and launch
Functional and security testing, user acceptance testing, data migration, training and go-live.
06Ongoing
Support
Thirty days of post-launch support, followed by an optional maintenance contract.
FAQ
Frequently asked questions
What is the difference between an AI chatbot and an AI agent?
A chatbot replies to messages. An AI agent can also act: it looks up information, calls tools such as a CRM, calendar or database, completes steps in a task and decides when to pass to a person. Agents need tighter limits and testing because they can change things.
How much does it cost to build an AI agent?
With Infovion, a proof of concept on your own data is typically ₹50,000 to ₹1.5 lakh ($600 to $1,800), and a production agent with tools, guardrails and hand-over ₹2 lakh to ₹8 lakh ($2,500 to $10,000). Model usage is a separate running cost, estimated in advance.
What is RAG?
Retrieval-augmented generation. Before answering, the system searches your own content for relevant passages and gives them to the language model, which answers from them and cites the source. It keeps answers tied to your information and makes them checkable.
Which AI models do you use?
Mainly Claude and GPT models through their business APIs, chosen per task for accuracy, speed and cost. Where data cannot leave your infrastructure, we can use models hosted in your own cloud account.
Is our data used to train the AI model?
No. We use business API plans under which the provider does not train on customer data. Your documents and conversation logs stay in your own database, and personal details can be masked before processing.
Can an AI agent work on WhatsApp?
Yes, through the official WhatsApp Business Platform. See WhatsApp chatbot development for booking, order and support assistants on WhatsApp.
Do you build AI agents for companies outside India?
Yes. We work remotely with startups, SaaS companies and agencies in the US, UK, Europe, the Gulf and Australia, invoiced in USD with no Indian GST, with the code in your repository from the first day.
Related services
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Further reading
Guides on this topic
Discuss an AI agent for your business
Share a brief description of your project. We respond within one business day with questions, an approach and a fixed-price proposal where the scope allows.