Services
Four kinds of work, one requirement: it has to run in production when we’re done.
Agentic AI Systems
Multi-agent pipelines that do real work – research, generation, classification, decision support – with a person in the loop before anything reaches a customer.
A single model call answers a question. An agentic system completes a task: it gathers information from live sources, evaluates it, produces an output, checks that output against criteria, and routes it to a human when confidence is low or stakes are high.
That last part is what separates a system you can operate from a demo you can’t. We design the approval gate at the same time as the pipeline, not after someone asks about it.
Working system in your environment · Architecture and data-flow documentation · Approval and escalation design · Evaluation criteria and a way to measure quality over time · Handover to your engineers
AI Product Strategy
Deciding what to build, and what not to. Most AI roadmaps fail on selection rather than execution.
The expensive mistake is rarely a badly built system. It’s a well-built system solving a problem that didn’t justify it. Strategy work here means finding where AI creates disproportionate value in your specific operation, sizing it against a real baseline, and being direct about the ideas worth killing.
This work suits teams under pressure to “have an AI strategy” who would rather have one good answer than a slide deck of options.
Prioritised opportunity assessment with sizing · Baseline measurement framework · Technical approach and architecture recommendation · Written specification your engineers can build from
Workflow & Process Automation
Removing manual steps from the systems you already run, without rebuilding them.
The highest-return AI work is usually unglamorous: the report someone assembles by hand every week, the queue someone triages by reading each item, the data re-typed between two systems that don’t talk. None of it is a moonshot. All of it compounds.
Integration-first, by default. Your CRM, support desk, warehouse and internal tools stay where they are.
Live integrations · Monitoring and failure alerting · Runbook documentation · Measured before-and-after on the baseline we set at the start
AI Governance & Compliance
The layer that decides whether a system is allowed to go live. Cheaper to build in than to retrofit.
Most AI systems stall at the same gate: someone senior asks who is accountable when it’s wrong, and there’s no mechanism to point at. Governance isn’t paperwork – it’s the approval gates, audit trails, retention rules and access controls that make a system defensible to a customer, an auditor or a regulator.
We’ve built to India’s DPDP Act and to GDPR principles, including immutable audit logging with cryptographic chaining, analytics that produce useful numbers without storing personal data, and human-review workflows that hold up under scrutiny.
Governance architecture · Audit and retention implementation · Review-workflow design · Documentation suitable for internal and external scrutiny
How engagements are structured.
A fixed-scope diagnostic. We find the real problem, measure the baseline, and produce a written recommendation with a technical approach and an honest cost estimate. It ends with a decision, not a proposal to buy more consulting. You keep everything regardless of what you do next.
Best when: you know something should be automated but not what, or you need a credible internal case before committing budget.
Design and delivery of a working production system, including governance, documentation and handover to your team. Fixed scope with a defined outcome, or a retainer where the roadmap is genuinely open.
Best when: the problem is identified and the constraint is capacity or specific expertise.
A fixed block of time each month for architecture review, roadmap input, and being the person your team can put a hard question to. Suits companies with engineers who can build but want senior AI judgment in the room.
Best when: you have the capability and want the direction.
