AI Automation & Agents

Where the value actually is#

Most teams come to us wanting a chatbot and leave with something less glamorous and far more valuable: the invoice reconciliation that took two people three days a week, now taking one person two hours to review.

The pattern that works is narrow and deep. A single process, fully automated, including the awkward 15% of cases that the first demo skipped. The pattern that fails is broad and shallow — a pilot that handles the happy path, impresses a steering committee, and never survives contact with the exceptions.

What we build#

Document and data workflows. Extraction, classification, validation and routing across invoices, claims, contracts, KYC packs and forms. Structured output with confidence scores, not prose that someone still has to read.

Conversational and support automation. Agents that resolve tickets by taking action in your systems — issuing the refund, updating the record, rebooking the shipment — rather than writing a polite summary of what a human should do next.

Internal copilots. Retrieval over your own documentation, policies and codebase, wired into the tools your team already has open.

Multi-step orchestration. Long-running processes that span systems, wait on humans, retry on failure, and can be audited afterwards. This is where most naive agent implementations fall over and where a durable workflow engine earns its place.

How we keep it honest#

Every engagement produces an evaluation suite before it produces a demo. It is a set of real cases with known-correct answers, versioned alongside the code and run in CI. It tells you what the automation gets right today, catches the regression when a prompt change helps one case and breaks four others, and gives your team a way to keep improving it after we leave.

Cost per run is instrumented from the start. An automation that saves ₹40 of labour and burns ₹55 of inference is a science project, and we would rather find that in week two than at the first invoice.