AI · Automation

AI Agents & Automation

Custom AI agents, OpenAI integrations, and workflow automation built directly into your product.

What's included

  • Custom GPT-4 / Claude agents
  • Multi-step workflow automation
  • Tool calling & function use
  • CRM and Slack integrations
  • Background job processing
  • RAG with your own data
  • Human-in-the-loop flows
AI Agents & Automation — service offered by Dharmendra Singh Yadav

AI That Does Real Work

There's a big difference between an AI demo and an AI agent that runs inside a production system. I build the second kind — agents that take actions, make decisions within defined boundaries, and complete multi-step tasks without someone holding their hand.

What I Build

Sales outreach agents that research prospects and draft personalised emails. Support agents that resolve tickets by reading documentation and order history. Internal tools that automate reporting, data entry, and approvals. If there's a repeatable workflow in your business, I can probably automate most of it.

The Technical Side

I work with OpenAI, Anthropic, and open-source models depending on your requirements. For complex pipelines I use LangChain or LangGraph. For retrieval-augmented generation (RAG) I integrate vector databases like Pinecone or pgvector so your agent can work with your own documents and data.

Integration First

An agent is only as useful as the systems it connects to. I integrate with your CRM, your database, Slack, email, and whatever else your team already uses. The agent fits into your workflow — you don't change your workflow to fit the agent.

Reliability & Guardrails

I build in logging, error handling, and human-in-the-loop checkpoints for anything that matters. You'll always know what the agent did, why it did it, and where it stopped if something went wrong.

Frequently asked

A chatbot answers. An agent acts — it calls tools, queries systems, and completes multi-step tasks with defined boundaries. If the value is 'it tells the user something', you want a chatbot. If it is 'it does something on the user's behalf', you want an agent.

It depends on the task, and the honest answer is that you should be able to switch. Route simple classification to a small fast model and reserve a frontier model for reasoning-heavy steps. Building against an abstraction rather than one vendor's SDK keeps that option open.

Constrain what it can do rather than trying to constrain what it says. Give it a narrow set of tools, require human approval for irreversible actions, log every tool call, and make destructive operations impossible at the permission layer rather than discouraged in the prompt.

Token cost is usually the smaller half. The larger costs are retrieval infrastructure, observability, and the engineering time to handle the cases where the model gets it wrong. Budget for the surrounding system, not just the API bill.

Let's talk.

Building production-grade SaaS, AI agents and mobile apps end-to-end.

Hiring for a senior role or have an interesting problem to solve? Drop a note — I read every message.