Practical writing from the frontline of enterprise AI.
Studio notes, customer cases, whitepapers, and reference architectures — published when we have something genuinely new to say, not on a content calendar.
State of Enterprise AI: regulated industries
A 28-page analysis of where regulated buyers are spending — and which programmes are delivering measurable returns. Based on 50+ structured interviews with CIOs and CDOs across BFSI, healthcare, and manufacturing.
- Spend patterns by industry, function, and use-case archetype
- What separates production AI from pilot purgatory
- Governance maturity benchmarks for regulated sectors
- The economics of generative AI at production scale
Recent insights from our principals.
No newsletters. No content marketing. Just engineering and strategy notes from people who do the work.
How a Tier-1 bank cut claims processing time by 9× with document AI
Inside the architecture, the rollout, and the governance model that made it audit-ready on first review.
The economics of generative AI at enterprise scale
Tokens, latency, evaluation, FinOps — what actually drives the unit cost of production GenAI.
Why most enterprise AI projects fail at month six (and ours don't)
The managed-operations playbook that keeps AI systems compounding value, not regressing.
Vision-QA reference architecture for high-volume manufacturing
Edge inference, governance, and the model-update cadence that keeps line yields stable.
42% claims cycle-time reduction at a top-five Indian insurer
Document AI, claims-routing automation, and the governance model that satisfied IRDAI.
Buying enterprise AI: a CIO's checklist for vendor selection
The questions every CIO should ask — that most never do — when shortlisting AI partners.
Lakehouse-first data platform for the regulated enterprise
How we design lakehouse architectures that satisfy both data scientists and audit teams.
Building an AI governance committee that actually meets
What works, what doesn't, and the artefacts that keep the committee value-additive past month three.
The vendor-management playbook for hyperscaler AI
How to commit to AWS, Azure, or GCP without locking yourself out of better models tomorrow.
A monthly note from our principals.
Field reports, reference architectures, and one short opinion. Sent monthly. We never write filler — if there's nothing to say, we don't send it.
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If a piece resonates, the principal who wrote it is happy to walk you through how it applies to your environment.