Solution blueprint · SaaS and e-commerce · AI agents
An AI customer support agent for a SaaS product
A support agent that reads the product's documentation, past tickets and the customer's own account, answers with sources in chat and email, performs safe actions such as resending an invoice, and hands over to a person with a summary when it should.
Design mockup with sample data. This is a solution blueprint by Infovion Technologies, not a client engagement.
- RAG
- answers grounded in your docs, with sources
- 8
- account actions through your API
- 6 – 10 weeks
- from kick-off to production
- $4,000 – 12,000
- typical fixed-price budget
The situation
The problem this solves
A B2B SaaS company with a few thousand customers has a four-person support team across two time zones. Two thirds of tickets repeat questions the documentation already answers, first replies take hours outside office time, and senior agents spend their day on password resets and invoice copies instead of the hard problems.
An off-the-shelf chatbot was switched off after it invented a feature. The company wants an agent that answers only from approved content, says when it does not know, can act on an account within strict limits, and leaves a clean hand-over in the helpdesk the team already uses.
The system
What it includes
Knowledge ingestion
Help centre articles, API reference, release notes and resolved tickets are indexed nightly, with stale and conflicting content flagged for the documentation owner.
Grounded answers with sources
Retrieval-augmented generation: the agent answers only from retrieved passages and links the articles it used. If nothing relevant is found, it says so and offers a person.
Account-aware replies
With the customer signed in, the agent reads plan, usage, recent errors and open invoices through the product's API, so answers fit the account.
Safe actions
A short, approved list of tools: resend an invoice, reset two-factor, extend a trial, create a bug report. Anything involving money or deletion needs a person.
Hand-over to the helpdesk
Escalations arrive in Zendesk, Freshdesk or Intercom with a summary, the steps already tried and the customer's sentiment, so nobody asks the same question twice.
Evaluation and monitoring
A test set of real questions runs on every change. Dashboards show resolution rate, escalations, unanswered topics and cost per conversation.
How it works
From input to outcome
- 01
Question
Chat widget or email; the customer's identity and plan come from your app.
- 02
Retrieve
Hybrid search finds the most relevant documentation and past resolutions.
- 03
Answer or act
The model answers from those passages, or calls an approved tool through your API.
- 04
Guardrails
Checks for unsupported claims, sensitive topics and low confidence before sending.
- 05
Escalate and learn
Hand-over with a summary; unanswered questions become documentation tasks.
What the design is built to achieve
- Repeat questions resolved in the first reply, at any hour, with the source shown.
- No invented answers: the agent declines and escalates when the documentation does not cover a question.
- Actions limited to an approved list, each logged with who, what and when.
- Quality measured on a fixed test set before every release, not judged by feel.
Technology
Claude API, Retrieval-augmented generation, PostgreSQL with pgvector, Node.js / TypeScript, Model Context Protocol tools, Zendesk / Freshdesk / Intercom APIs, React chat widget, Evaluation suite in CI
Budget and timeline
| Build, clients in India | ₹3.5 – 10 lakh |
|---|---|
| Build, clients outside India | $4,000 – 12,000 |
Timeline. 6 – 10 weeks: two weeks for a proof of concept on your documentation, then production with hand-over and actions.
Running cost. Model usage per conversation, reported monthly; typically a small fraction of the cost of a human-handled ticket.
Indicative planning ranges for 2026. Every project receives its own fixed-price proposal after a first call. Source code and accounts belong to the client.
Request a fixed-price proposalFAQ
Frequently asked questions
What is a RAG chatbot?
RAG stands for retrieval-augmented generation. Before answering, the system searches your own content for relevant passages and gives them to the language model, which answers from those passages and cites them. It keeps answers tied to your documentation instead of the model's general knowledge.
How do you stop an AI support agent from making things up?
By design rather than hope: answers must be supported by retrieved passages, the agent is instructed and tested to decline when nothing relevant is found, a second check looks for unsupported claims, and a fixed set of real questions is run before each release.
Can the AI agent take actions in our product?
Yes, through tools that call your API. In this design the list is short and approved by you, such as resending an invoice or extending a trial. Refunds, deletions and plan changes go to a person.
How much does it cost to build an AI support agent?
A proof of concept on your documentation is typically $600 to $1,800. A production agent with account access, actions and helpdesk hand-over is typically $4,000 to $12,000 as a fixed-price project, plus model usage.
Is this a project you delivered for a client?
No. This page is a solution blueprint, shown with sample data. It reflects how we build AI features today: grounded answers, limited tools, and evaluation before release.
More blueprints
Other systems we have designed
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