AI-Assisted Development
AI-Assisted Development service track
We turn PRDs, user stories, wireframes, workflow notes, and technical specs into agent-ready implementation plans, then build with AI-assisted engineering under human review.
Approach
The work is structured around explicit decisions and usable outputs rather than a generic delivery template.
Specs-driven development is how we make AI-assisted software delivery controllable. The specification is not documentation after the fact. It becomes the instruction layer for AI coding agents and engineers: product behavior, user roles, edge cases, data rules, acceptance criteria, review checkpoints, and handover notes.
That gives AI room to move quickly without inventing the product as it goes. Agents generate code against explicit behavior, engineers review against the same contract, and changes can be traced back to the source requirement instead of disappearing into prompts, chat history, and silent assumptions.
Fit
The strongest engagements have a clear operating constraint, decision, workflow, or delivery risk to improve.
Best fit
Typical use cases
Deliverables
Each workstream is labelled for the outcome or artifact it is responsible for, not its position in a template.
Agent-ready specification and implementation plan
Acceptance criteria mapped to features, screens, APIs, and data behavior
Production application or feature set built with AI-assisted engineering
Human review notes, tests, and traceability back to the spec
Handover documentation for future agent-assisted changes
Process
A visible sequence of decisions, working outputs, review points, and handover, rather than a black-box delivery cycle.
We review requirements, edge cases, roles, acceptance criteria, constraints, and missing decisions before implementation starts.
We translate the spec into architecture, data models, APIs, screens, release slices, and prompts or tasks that agents can execute safely.
We implement in controlled increments, using AI where it improves speed while reviewing each piece against the written behavior.
We tie tests, handover notes, and implementation decisions back to the spec so future AI-assisted changes have a reliable source of truth.
Outcomes
Faster AI-assisted delivery with clearer boundaries
Less product drift between requirements and generated code
Cleaner review, acceptance, and stakeholder sign-off
A codebase aligned to documented product intent
FAQ
The answers below clarify scope, collaboration, ownership, and the conditions that usually affect delivery.
No. The spec is actively used to guide AI coding agents, implementation tasks, review, tests, and handover. It is the operating contract for the build, not a document that gets ignored once coding starts.
No, but the important behavior should be explicit. If the spec has gaps, we identify and resolve them before AI-assisted development turns those gaps into hidden implementation assumptions.
Yes. Figma, PRDs, user stories, spreadsheets, workflow notes, and technical docs can all become the source material for an agent-ready implementation plan.
Agents help produce code faster once the expected behavior, constraints, data model, and acceptance criteria are clear. Engineers still review architecture, security, maintainability, and whether the result matches the spec.
We update the spec, assess the impact, and adjust the implementation plan. The spec remains the source of truth so future AI-assisted changes do not depend on old prompts or memory.
Related Services
Next step
Bring the PRD, wireframes, user stories, or rough notes. We will turn them into an agent-ready plan and build from it.
