Notes from
the work.
Essays and field notes on applied AI, delivery and governance. The unglamorous parts of turning a demo into a product that survives a year in production. Infrequent. Considered. Ruthlessly free of buzzwords.
Production AI vs pilot: the complete guide to shipping AI that survives
Most enterprise AI never leaves the pilot. This is the complete guide to the gap between a working demo and a defended production system - scoping, governance, evaluation, operating model, and the numbers that get a programme through its second budget cycle.
Enterprise AI transformation: what it actually means
Enterprise AI transformation is not a platform purchase or a series of pilots. It's an operating-model change. Here's what that looks like in practice, and what to avoid.
Building evaluation harnesses for production AI systems
Evaluation is the single most under-invested control in enterprise AI. A practical guide to designing, building and operating an evaluation harness that catches regressions before customers do.
How to measure ROI on an AI investment
A practical framework for measuring the return on an AI programme - including the costs most teams forget, and the outcomes most vendors won't commit to.
On-premise, sovereign cloud, or public cloud for AI: how to choose
A grounded decision framework for where to deploy AI - public cloud, sovereign cloud, or on-premise - with the trade-offs enterprise teams actually face.
Voice AI for Indian languages: what actually works in production
Deploying voice AI in India is not a technology demo - it is an engineering problem of accents, code-switching, noisy lines and language mix. Here's what works, and what doesn't.
Why AI pilots fail: the production-readiness gap
Most AI pilots work in the demo and quietly stall in production. The gap between the two is not the model - it's the invisible scaffolding around it.
The boring parts are where the money is
Most AI pilots stall at the working demo. The work that lets them survive in production isn't glamorous. It's also where the compounding happens.
The AI governance checklist for enterprise teams
A working checklist of the governance controls every production AI system needs - evaluation harnesses, red-teaming, PII redaction, audit trails and policy-as-code - in the language your CISO will actually read.
AI agents vs automation: when to use which
Agents and automations solve different problems. Using an agent where a rule belongs - or a rule where an agent belongs - is how most production AI systems fail quietly.
What production AI actually costs
A plain-English breakdown of the cost of shipping AI to production - build, run, governance, and the line items most vendors quietly leave out of the first quote.
Evaluation is not a phase-two upgrade
If a system can't be watched, graded and improved, it shouldn't be in production. The case for writing the harness on week one.
How to scope an AI deployment in two weeks
A practical, two-week blueprint for scoping an enterprise AI programme - from opportunity mapping to a signed scorecard. No workshops, no theatre.
Governance is a feature, not an appendix
In regulated and government work, the hardest conversation is rarely about the model. It's about the system around it. Here's the operating model we run instead.
Let’sbuildyoursystemnext.
Thirty minutes with someone who’d be doing the work. No slide deck, no intake form. We’ll tell you what’s feasible, where you’ll hit friction, and what we’d pick up first.