Find the work worth automating
We look at the tasks your team actually repeats and pick the ones where an agent is genuinely better than a script. Some of what people ask for is a cron job wearing a costume, and we will say so.
// AGENTIC AI ENGINEERINGAgents that do the work,
Agents that do the work,
not just the talking.
Most AI pilots stop at a chat box. We build agents that read your systems, take actions through governed tools, and stop for a human at exactly the points where a mistake would be expensive.
Tool-using agentsHuman-in-the-loopModel-agnosticFull audit trail
// WHY MOST AGENT PROJECTS STALL
The demo works. Then someone asks what happens when it's wrong.
An agent that summarises is easy. An agent that acts — files the ticket, revokes the access, issues the refund — is a different engineering problem, because now being wrong has a cost. The projects that reach production are the ones that decided early which actions need human sign-off, what the agent is allowed to touch, and how every step gets recorded. That is the part we build.
// DELIVERY STANDARD
Built for decisions, evidence and accountable execution.
Whether this is a commercial engagement or a government program, the work is structured so technical teams can act and leadership can verify progress.
Design the guardrails first
Before any prompt is written we agree what the agent may touch, which actions need approval, what data it may retain, and what it must never do. This is the design, not the paperwork afterwards.
Build the thinnest version that works
A single agent with a small tool set, run against real cases. Most problems do not need a state machine, and the ones that do reveal it quickly.
Evaluate against real cases
We build a test set from your own historical work and measure against it, so "it seems better" becomes a number you can check after every change.
Ship it, then watch it
Deployed into your environment with logging, cost controls and an effort ceiling, so an agent that gets stuck stops instead of running up a bill.
// PROJECT OUTPUTS
What your team can take into the next review.
- Working agents deployed in your environment
- Tool integrations with scoped, permissioned access
- Approval and escalation rules, configured to your roles
- Evaluation test set built from your historical cases
- Audit logging of every agent action and decision
- Runbook and handover so your team can operate and extend it
- Source code — you own what we build
// BUILT FOR
Designed around the people who own the outcome.
- Operations leads whose teams repeat the same triage every day
- Engineering leads accountable for what the agent is allowed to touch
- Compliance and legal, on data handling and DPDP obligations
- Finance, on model cost ceilings and where the savings actually land
Single-purpose agents
Instructions plus a defined set of tools. The agent reasons, acts, checks its own result and repeats. The simplest thing that works, and often the right answer.
Structured workflows
A defined pipeline — take in, look up, analyse, decide, respond — with branches on the outcome and approval points where they matter. Predictable, and reviewable by people who do not read code.
Stateful multi-step agents
For work that is not a straight line: loops, parallel branches, retries and durable state, so a long-running job survives a restart instead of starting over.
Governed tool access
Every tool an agent can call is explicit, permissioned and logged. An agent gets the narrowest access that lets it finish the job — never a shared admin credential.
Human approval gates
High-impact actions pause for sign-off. You decide which actions qualify, who approves them, and what happens when nobody responds in time.
Evaluation and audit trail
A test set that catches regressions when a prompt or model changes, and a durable record of every step an agent took and why.
// PRICING
Productized rates, not a mystery quote.
Standard-scope engagements at flat starting rates — competitive for the India/Odisha market, not padded for a global enterprise budget.
Agent Feasibility Sprint
₹75,000fixed, 2 weeks
We take one candidate workflow, build a working agent against your real cases, and measure it. You get the agent, the evaluation set and an honest verdict on whether the rest is worth building.
Single-purpose agent
₹1,50,000starting
One agent, a defined tool set, approval rules and audit logging, deployed in your environment with a handover runbook.
Structured workflow agent
₹2,75,000starting
Multi-stage pipeline with branching, human approval gates and integrations across several systems. Priced on the number of tools and approval paths.
Stateful multi-step build
Customscoped per engagement
Loops, parallel branches, durable state and recovery. Scoped after a feasibility sprint, because guessing at this tier helps nobody.
Care and evaluation
₹40,000starting / month
Model and prompt updates, evaluation runs against your regression set, cost monitoring and tuning as your workflows change.
Indicative starting prices for scoping conversations, not a quote. What actually moves the number is the number of systems an agent must integrate with, how many actions need human approval, and whether your data constraints require an open-weight model on your own hardware. Model usage is billed to your provider account, so you keep visibility and control of that spend rather than seeing it marked up through us.
Frequently Asked Questions
Do you have a library of ready-made agents we can start from?
No, and we would be careful with anyone claiming a large catalogue. We build from your runbooks, because the value is in the specifics — your systems, your escalation paths, your definition of done. We do reuse our own engineering patterns for tool access, approvals and evaluation, which is where most of the time goes anyway.
Which AI models do you use?
Whichever fits the task, the budget and your data constraints — we build model-agnostic so you are not locked to one vendor. That matters practically: the price and capability of these models change every few months, and a system that can switch is worth more than one tuned to a single provider.
Can the agent run on our own infrastructure?
The agent and its tooling, yes — that is a normal deployment for us. The model itself depends on which one you choose: an open-weight model can run on your hardware, while a hosted frontier model means the request leaves your network. We will lay out that trade-off honestly against your data-sensitivity requirements before you commit.
What about the DPDP Act if the agent handles personal data?
It applies regardless of whether AI is involved — consent, purpose limitation, retention and breach notification all still hold. The AI-specific complication is that sending personal data to a third-party model provider is a processing decision you must be able to justify. We design for it at the start, including masking sensitive fields before they reach the model where the task allows it. Retrofitting this is expensive.
Will this replace our team?
In our experience it moves where their time goes rather than removing the need for them. Agents are good at the repetitive first pass and poor at the judgement call, so the useful pattern is an agent that clears the routine volume and escalates the rest with the context already gathered. Anyone promising headcount reduction on a first project is selling something.
How do we know it still works in six months?
Because it is tested. The evaluation set built during the project runs against every prompt or model change, so a regression shows up as a failed test rather than as a customer complaint. Agents without an eval set degrade silently — that is the most common failure we are asked to fix.
Let's build together
Bring your runbooks. We'll build them into agents.
Book a free 30-minute consultation with our engineering team — no obligation, just a clear, practical plan.