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AI Agent Development

An AI agent does more than chat. It reads an email or PDF, decides what it is, updates your system and asks a person only when it is unsure. We build agents with guardrails, review steps and monitoring.

Most useful AI in a business is not a chat window. It is a quiet worker: one that turns supplier quotes into structured data, summarises a long customer conversation for your sales team, routes a request to the right person or drafts a reply for review.

We build these agents on Claude and connect them to your real workflow. Each one has a narrow job, access only to what it needs, a person reviewing anything that matters and logs you can read. In our Trripah CRM, an AI parser reads supplier quotes in email, PDF or DOCX and returns a structured itinerary for a person to confirm before it reaches a client.

We are honest about limits. Agents can be wrong, so we design for review, not blind trust, and we measure accuracy on your real documents before you rely on it.

What we build

01

Document processing

Quotes, invoices, enquiries and forms turned into structured records, with uncertain fields flagged for review.

02

Sales and support assistants

Conversation summaries, suggested replies and lead scoring for your team.

03

Workflow agents

Agents that take a multi-step task, such as collecting information, updating a record and notifying someone.

04

Guardrails and monitoring

Least-privilege access, human approval for sensitive actions, logs and accuracy tracking.

  • Claude API
  • Node.js
  • Spring Boot
  • PDF/DOCX parsing
  • MongoDB
  • Queues and cron jobs

What you get

  • A scoped agent with one clear job
  • Prompting, retrieval and tool access designed around your data
  • Human review and approval steps
  • Evaluation on your real documents before launch
  • Logging, monitoring and cost controls
  • Documentation and a plan to extend

How we work

  1. 1

    Discover

    A call to understand your process, users and goals, and what success looks like.

  2. 2

    Scope in writing

    A written scope, timeline and cost before any build begins, so there are no surprises.

  3. 3

    Design

    Prototypes, data model and architecture you can react to early.

  4. 4

    Build in short sprints

    Each sprint ends in a working demo, with a shared board to see progress at any time.

  5. 5

    Launch and support

    Release, monitoring and support, with documentation so you are never locked in.

What affects the cost

We do not publish a single price, because it would mislead. These are the things that move it. We agree scope and cost in writing before any build starts.

  • How messy and varied your inputs are
  • Accuracy required and the cost of a mistake
  • Number of systems the agent must act on
  • Volume of items processed
  • Review workflow and approvals
  • Model choice and usage cost

Questions, answered

What is an AI agent?

Software that uses a language model to carry out a multi-step task, such as reading a document, deciding what it is, updating a system and asking a person when unsure.

How accurate are AI agents?

It depends on your documents and the task. We test on your real data, show uncertain results to a person for review and track accuracy after launch.

Is it safe to give an AI agent access to our systems?

Give it only what it needs, require approval for sensitive actions and log everything. We never give agents production access they do not need.

Which AI models do you use?

We choose per task. Our Moonlight chatbot runs on Claude Haiku 4.5, and we pick models by quality, speed and cost.

Do you build AI automation with tools like n8n?

We use no-code tools where they fit and custom code where the workflow is core, high-volume or sensitive. See our guide to choosing.

Tell us what you need

A real person calls you back within one business day. We work with clients across India and worldwide.

Start a project