AI that finishes the work, not just the conversation.
We build AI agents that handle real jobs inside your business — reviewing cases, triaging tickets, running the process your team repeats every day — and hand off to a person only when it matters.
Is this you?
“Half my team does the same 12-step process across six tools, every day.”
The agent runs the playbook, writes back to your systems, and escalates the genuinely odd ones.
“We demoed an AI feature. Customers loved it. Engineering says it can’t ship.”
You get a version that has tests, limits, and a paper trail — the parts a demo skips.
“Customers want self-serve outcomes, not a chatbot that says “let me connect you”.”
An agent that completes the journey — in your app, your chat tool or over email — with a measured success rate.
“Auditors want to know what the AI did, why, and whether a human signed off.”
Every action is recorded and replayable, every rule is written down, every escalation is yours to define.
What changes
Your analysts read every case, in order, all day.
They read the one in ten that needs a human. The rest is done, logged, and checkable.
“What did the AI do?” is a shrug.
Every step is recorded. You can replay any decision and show it to an auditor.
Each new workflow is a new project.
The same agent learns a new job in weeks, not quarters.
What you get
An agent that finishes the job
Multi-step work, done end to end, that picks up where it left off if something fails mid-way.
It uses your tools, and only those
Connected to the systems you already run, with per-tool permissions and limits you set.
It remembers what matters
Context across sessions, kept separate per customer, forgotten when it should be.
A quality bar you can see
Continuous checks against real examples; every change is measured before it reaches a customer.
Rules that hold
Refuse, redact or hand to a person — the same rule everywhere, written down.
A record you can prove
Every plan, action and decision logged and replayable. Built to pass audits.
How it goes
Discovery
We map one high-value workflow end to end and agree, in writing, what “good” looks like.
Pilot
A working agent against real examples. Connected, guarded, logged. Your team uses it; customers don’t yet.
Hardening
We try to break it. Fixes become automatic checks so it can’t regress.
Live and handover
Phased rollout with a kill switch. Your team takes the wheel; we stay on call.
Pilots run ₹35–80L over 6–10 weeks. Going live is scoped separately once the pilot lands. Stop, ship or extend at the end of every phase.
Questions we get on the first call
Got any questions?
Ask — a founder reads every message.
Will it run on our systems or yours?
Yours. It’s deployed in your cloud account with your access controls. We never become something you can’t replace.
What does it cost?
A pilot typically runs ₹35–80L over 6–10 weeks depending on how many systems it touches and what compliance you need. Going live is scoped after the pilot.
What stops it from making things up or doing the wrong thing?
Three things: it can only call tools you’ve allowed, every change is tested against real examples before it ships, and every action is logged so we can find and fix a bad decision fast.
What about customer data and privacy?
Personal data is detected and masked before it reaches a model, each customer’s context is isolated, and our model contracts forbid training on your data. Data residency is designed in on day one.
Can our team take it over?
That’s the plan. Every engagement ends with tests, a runbook and a paired handover. Most clients keep a small retainer for new capabilities only.
Which AI model do you use?
Whichever fits, and you can switch. We start with the strongest general model for reasoning and cheaper ones for routing, then tune per job.
For your engineering teamarchitecture · stack · how we run it
Chat, web, API, voice, or a scheduled trigger. The agent meets the user where they already are.
Plan → Act → Reflect loop. Resumable, observable, model-agnostic. The brain of the agent.
Tools, memory, model gateway. Versioned, permissioned, and instrumented down to the call site.
Append-only trace ledger and continuous eval harness. Trust is something the system can prove.
Have an intelligent system to build?
Tell us about the messy bit — the legacy system, the model that won’t behave, the workflow no one wants to own. We’ll come back with a discovery plan inside two business days.
