Guides
A practical path from task to evidence
Implementation guides for AI in products, software delivery and company work, alongside established QA, automation and compliance resources. Each opens with a chapter index so you can go straight to the decision you need.
What a guide gives you
Start with the scope and expected behaviour, then follow the workflow, evaluation and operating decisions. Technical examples are labelled with their limits. Illustrative scenarios are not presented as client engagements.
For AI work, useful output and enforceable authority are separate concerns. The guides keep permissions, independent checks, human approval and ongoing costs visible alongside implementation.
Use the related buyer articles to compare tools, estimate effort or decide whether the task is worth automating. Existing QA and compliance guides remain available; AI does not replace those disciplines.
For a product assistant, begin with the company-document guide. It separates source preparation, retrieval permissions, answer evaluation and escalation. That order matters: a better prompt cannot recover knowledge that was never found, and filtering a final answer cannot undo a retrieval step that exposed forbidden records. The guide keeps the user task visible through implementation and operation.
For engineering teams, the SDLC guide helps choose the first pilot. Code review asks whether a change introduces an engineering risk; ticket review asks whether it fulfils agreed requirements. Development and E2E bots propose patches or tests through a reviewable boundary. The QA bot works earlier, identifying missing decisions before they turn into implementation assumptions.
For company-process work, follow the readiness and adoption guide with the person who owns the task. It includes the exception path and human review effort, because moving work into a proposal queue does not necessarily reduce it. The worked ROI article uses fictional numbers to show the arithmetic; replace them with observed task volume, adoption and correction time.
The established automation guide addresses suite ownership and reliable release signals. Performance and accessibility, nearshore engagement and EU compliance guides cover different constraints around delivery. Use those resources when their scope fits the project; adding an AI capability does not remove obligations attached to the existing product or change who owns a release decision.
All guides
- The EU compliance surface for a software productGDPR, the European Accessibility Act, the Cyber Resilience Act and NIS2, read for what each one actually asks of an engineering team — with the dates, the scope tests and the places teams get caught.
- A test automation strategy that survives contact with a roadmapWhat to automate, in what order, at which level, and how to stop a suite becoming the thing the team works around — written for teams who have tried this once already and got a suite they do not trust.
- Choosing and running a nearshore QA engagementHow the commercial side actually works — what drives the price, which model fits which problem, what to ask before signing, and the failure modes that have nothing to do with the day rate.
- Performance and accessibility as one workstreamThey are usually run as separate projects by separate people at separate times. They share most of their causes, most of their fixes and all of their failure mode, which is drift.
- Build an AI code reviewer for pull requests and merge requestsA review assistant that reports evidenced engineering risks: event handling, diff context, permissions, finding quality and a rollout that does not drown engineers in comments.
- Build a development bot that proposes reviewable fixesFrom a reproducible problem to an isolated patch: how to bound execution, preserve test independence and hand an engineer a change they can actually review.
- Build a QA bot for requirements and test scenariosTurn a ticket into reviewable test ideas and explicit questions, without letting a model invent the product rules it is meant to verify.
- Review tickets and merge requests for missing requirementsA traceability reviewer that connects acceptance criteria to implementation and test evidence, while distinguishing omissions from unresolved product decisions.
- Build an E2E testing bot without manufacturing green buildsGenerate and maintain browser tests from approved behaviours, then independently check whether those tests detect a broken journey rather than mirror the implementation.
- Test AI-generated code before it reaches a releaseA release verification workflow built around independent expectations, compatibility, permissions and honest evidence of what ran.
- Build a support assistant using company documentsPermission-aware retrieval, source-backed answers, difficult-question evaluations and an escalation path that survives stale knowledge and failed dependencies.
- Integrate AI into an existing software delivery lifecycleChoose one bottleneck, establish a baseline, pilot a bounded workflow and measure accepted work rather than generated activity.
- Enable AI in company workflows and business processesA practical path from use-case selection to a controlled pilot, existing-tool integration, staff adoption and an honest business-impact decision.
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