AI Agent Development
Agents that do the work, not just describe it.
- From
- ₹1,50,000
- Timeline
- 3–6 weeks
A chatbot answers. An agent acts. The difference is tools: an agent can read your CRM, search your documents, call an API, and write the result back — looping until the job is genuinely done.
The engineering that matters isn't the prompt. It's the guardrails: what the agent is allowed to touch, what it must escalate, how you audit every action it took, and what happens when the model is confidently wrong.
What we build
- Agent architecture design — tools, memory, retrieval and control flow
- Model selection across Claude, GPT and open-weight options with cost modelling
- Tool and function calling wired into your real systems
- Retrieval over your own documents, tickets and product data
- Guardrails: allowed actions, spend limits, confidence thresholds, escalation rules
- Human-in-the-loop approval on anything irreversible
- Full action logging so every decision can be audited after the fact
- Evaluation harness to measure quality before and after prompt changes
Examples of this work
Concrete builds rather than capability statements.
Inbox agent
Reads every inbound email, classifies intent, pulls the relevant order or account, drafts a reply grounded in policy documents, and sends automatically only above a confidence threshold — everything else lands in a human queue with the draft ready.
Lead research agent
Takes a company name, gathers public data, infers fit against the ICP, writes a personalised opener referencing something real, and logs the reasoning in the CRM for the rep to check.
Ops exception agent
Monitors failed orders and shipping exceptions, diagnoses the cause from logs and carrier data, applies the standard fix where one exists, and escalates the rest with a written summary.
What you get
- Deployed agent with documented tool permissions
- Guardrail and escalation policy written down
- Action audit log you can query
- Evaluation set with baseline quality scores
- Cost-per-run estimate and monitoring
Tools we use here
- Claude
- OpenAI
- n8n
- LangChain
- Qdrant
- PostgreSQL
- Webhooks
AI agents — questions we get asked
What stops the agent doing something stupid?
Scope and approval gates. Agents get the narrowest set of tools that lets them finish the job, hard limits on spend and volume, and a human approval step on anything irreversible — refunds, deletions, outbound messages to customers. We also log every action so you can audit what happened.
Where does our data go?
Through the model provider's API only, under their no-training-by-default terms for business tiers. Retrieval data stays in a vector store you own. If your policy forbids any external processing, we can run open-weight models on your own infrastructure instead — slower and more expensive, but fully contained.
How much does it cost to run?
Usually between ₹2 and ₹40 per completed task depending on model and context size. We model it per workflow before building, and design for the cheapest model that passes your quality bar rather than defaulting to the largest one.
How do we know it's working well?
We build an evaluation set from your real cases and score against it. That gives you a number before launch and a regression check every time a prompt or model changes, instead of an opinion.
Often paired with
Want AI agents working in your business?
Book a free 30-minute audit. We'll tell you what it would take, what it would cost, and whether it's worth doing at all.
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