AI Requirement Analysis — cover image
Service · Engineering Capability

AI Requirement Analysis.

AI-assisted project scoping. I use LLMs during discovery to surface edge cases, generate feature maps, and pressure-test data models — before your team writes a single line of code.

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The Shift

From what's slowing teams down, to what unlocks growth.

01The Trap

Requirements phases miss edge cases.

Most software projects overrun because the requirements phase missed the edge cases. Everyone catalogs the happy path. Almost nobody catalogs the twelve failure modes, the seven regulatory constraints, the four adversarial inputs, and the state the system finds itself in when a background job fails midway through.

Those gaps get discovered mid-sprint, and every one of them is a decision that should have been made in week one — being made in week nine with a deadline breathing down the team's neck.

02The Method

Use LLMs as an adversarial scoping partner.

Modern LLMs are surprisingly good at one specific job: given a business context, enumerating everything that could go wrong. I use that systematically — feeding the model your context and forcing it to surface failure modes, adversarial inputs, regulatory questions, and integration cliffs your team might otherwise stumble into mid-implementation.

  • AI-augmented user-story generation from stakeholder notes.
  • Adversarial edge-case enumeration on every critical flow.
  • Draft database schemas and API contracts you can review before commit.
  • Risk register with mitigation options, ranked by likelihood × cost.

Most software projects overrun because the requirements phase missed the edge cases. I use LLMs as a scoping partner — feeding them your business context and forcing them to enumerate failure modes, adversarial inputs, and integration cliffs your team might otherwise discover mid-sprint.

The deliverable is a written requirements pack you can hand to any implementation team (mine or yours), with a risk register and a phased build plan.

What's included in this build

Every ai requirement analysis engagement is a premium, end-to-end delivery tailored to your specific business rules.

Stakeholder Interviews

Structured discovery sessions with product, engineering, and operations — captured, transcribed, and fed into the analysis model with your consent.

AI-Assisted User Stories

User stories generated with LLM assistance from the interview transcripts, then reviewed and refined with you to catch anything mis-summarised.

Edge-Case Enumeration

Systematic enumeration of failure modes, adversarial inputs, and regulatory cases across every critical flow. Delivered as a numbered catalog you can sign off on.

Database Schema Draft

First-cut ER diagram and migration plan for the primary data model, sized against your projected volume so the schema doesn't need a rewrite at scale.

API Contract Draft

OpenAPI or GraphQL schema stub for the primary integrations, versioned and reviewed with your consumers (frontend, mobile, partners) before implementation.

Risk Register

Ranked list of known unknowns and open questions — each with a proposed mitigation and a rough cost/likelihood scoring. Your leadership team can budget against it directly.

At a glance

  • AI-augmented user-story generation
  • Adversarial edge-case enumeration
  • Database schema and API contract drafts
  • Risk register with mitigation options

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Frequently Asked Questions

Common questions about ai requirement analysis engagements.

How exactly does AI help here?
AI accelerates the mechanical parts of discovery: transcribing interviews, deriving user stories, and enumerating edge cases. It does not replace human judgment — every artefact is reviewed with you before it's committed. Think of it as pair-programming for scoping.
Who owns the output? What about IP?
You do. The full deliverable — interview transcripts, user stories, schemas, risk register — is yours under a standard consulting agreement. LLM prompts and templates I've built stay mine, but everything about your project is yours.
How long does an AI-assisted requirements pack take?
For a mid-sized product (three-to-six primary flows), typically two to three weeks: one for interviews, one for analysis and drafting, and half a week for iteration. Larger products scale up roughly linearly.
Does this replace hiring a proper business analyst?
For most small-to-mid engagements, yes — the deliverable is what a good BA would produce. For enterprise programs with dozens of stakeholders across regulatory-heavy industries, no — a full-time BA is the right investment and I'd typically work alongside them.

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