Articles 11–20 of 101
Our step-by-step process for a retrieval assistant over company documents: source audit, chunking, hybrid search, citations, evals and permission-aware launch.
What a paid discovery phase includes — workshops, user flows, architecture, risks, a fixed quote — and why it saves more than it costs.
The software handover checklist: code, credentials, infrastructure, docs, test suites, AI evals and a recorded walkthrough — before the final payment.
What India’s DPDP Act means for AI projects: consent, purpose limits, data minimisation, processors and breach duties — in plain terms.
Validate a startup idea in 2–4 weeks before paying for an MVP: customer interviews, a landing page test, a concierge version and the signals that mean go.
What data a custom ML model needs and how to prepare it: history, cleaning, labelling, defining the target and avoiding leakage — before you hire anyone.
A practical 90-day plan to take an AI pilot to production: evals, integrations, supervised launch, monitoring and handover — with the gates between each phase.
A scoring method to choose your company’s first AI project: volume, clarity, data access, risk and measurability — with examples of good and bad first picks.
How to make sure you own your software: IP assignment, NDAs, repositories and accounts in your name, open-source licences and a clean handover at the end.
Nine warning signs in a software or AI development quote — from no discovery phase to vague ownership terms — and what a trustworthy quote includes instead.
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