Insights

What breaks in production AI systems, written by the people fixing it

Field notes from delivered engagements: retrieval failures, agent guardrails, when a language model is the wrong tool. Written for engineering leaders who have to make the call, with numbers where we have them.

Articles

3 guides, each one built from a repeating engagement pattern

Retrieval

Why RAG systems fail in production, and the 5 fixes that matter

Teams swap the model, rewrite the prompt and raise the temperature. The answers stay wrong, because the correct passage was never in the context window to begin with.

Muhammad Ghulam Jillani · 18 June 2026 · 9 min read

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Decision guide

When not to use a large language model

A generative model is the wrong tool at least as often as it is the right one. Knowing which situation you are in before the build starts is worth more than any prompt technique.

Muhammad Ghulam Jillani · 9 July 2026 · 8 min read

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Agents

The checklist an AI agent has to pass before it runs unattended

A demo agent runs one happy path in front of an audience. A production agent runs thousands of times a week against APIs that rate-limit and records that contradict each other.

Muhammad Ghulam Jillani · 24 July 2026 · 8 min read

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Your AI system should work, not just demo well

Book a free 30-minute scoping call. You leave it with a recommended approach, a realistic timeline and a cost range, and you talk to the engineer who would design the system.

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Written scoping summaryyours after the call

1Recommended approachRAG with hybrid retrieval, or classical ML, or plain software. Including the option of not building.
2What the system ownsThe steps it resolves alone, and the steps that stay with a person.
3First metric and baselineThe number it has to beat, measured on your own data before any build.
4Timeline and cost rangePhased, with the first scored result inside 14 days.

Written by the engineer who would build itFree, no obligation, yours to take to any vendor

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