Field notes on Context Engineering.
Essays from the engagements — what we shipped, what failed, what the next regulator visit will ask. Published roughly twice a month, written by the people on the floor.
— No SEO churn. No "ten things". Just field notes.
Two we wrote first.
Why "Context Engineering" is the next buyable category.
Every enterprise AI project that survived its first year did one thing first: it built the semantic layer. We argue this layer deserves a name, a budget line, and a buyer — and explain how the FlexiContext framework makes it procurable.
Microsoft Fabric vs Databricks for Indian BFSI: a partner's view.
We've shipped both, in production, for regulated financial-services clients in India. Here is the honest decision tree — including where Fabric loses, where Databricks loses, and what RBI / SEBI care about that the vendors don't lead with.
Next four in the queue.
We publish what we're learning, in the order we learn it. The list shifts when an engagement teaches us something worth writing.
The Governance Trinity, in production: lessons from a Top-5 NBFC.
What it took to put lineage, KPI registry, and exception spine into a live RBI-supervised entity — and what we'd do differently next time.
Why every "AI strategy" we've seen this year is wrong.
The honest critique. CXO-level AI strategies that confuse model selection with system design — and the four corrections that turn a slide deck into a roadmap.
Decommissioning a model: the part nobody writes the playbook for.
Models retire. Most teams have no plan. The pre-mortem we now write before any model goes to production.
FlexiAnalyst — what we learned in three pilot deployments.
The product post-mortem on the first three pilots: where the agent shone, where it tripped, and what changed in the underlying context layer as a result.
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