Solution

From requirement
to production code —
AI at every step.

Submit a requirement and get enriched stories. Open VS Code and generate codebase-aware code. Describe an app and watch it appear. Every step governed, tracked, and policy-compliant.

See Case Studies
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Anthropic Claude Code
Codebase indexed in VectorDB
1,247 files · 98k tokens of context
Story enriched in 8 seconds
AC · edge cases · tech context added
user-service.ts
auth.service.ts
▾ src
▾ services
user-service.ts
auth.service.ts
token.service.ts
▾ schemas
user.schema.ts
9import { validateSchema } from '../schemas'
10 
11export async function createUser(req: Request) {
12  const { email, role } = req.body
13  const validated = await validateSchema(userSchema, req.body)Tab ↹
14  // checks role against org policy...
15 
Context: user.schema.ts · auth.service.ts · org-policy.json
3 files · 312 tokens
< 2 min
to turn a raw business requirement into enriched, dev-ready user stories
Full repo
context in every code suggestion — via VectorDB-indexed codebase
60–80%
of unit tests and test data auto-generated per story
Zero
policy violations — org guardrails enforced on every generated commit
Platform Capabilities

Every stage of the SDLC has an AI layer behind it.

01
Story Enrichment Agent
Business requirements come in raw and vague. The Story Enrichment Agent rewrites them as dev-ready user stories — adding acceptance criteria, edge cases, dependency flags, and technical context in seconds. Dev teams start sprints with clarity, not questions.
02
VS Code Extension with Codebase Context
The platform indexes your entire codebase into a VectorDB. The VS Code extension surfaces this context directly in the editor — so generated code matches your naming conventions, reuses existing utilities, and fits your architecture. No more generic suggestions that don't compile.
03
Code Generation
Generate implementation code for a user story with full awareness of what already exists. The AI doesn't just produce syntactically correct code — it produces code that is contextually correct: consistent with your patterns, your abstractions, and your existing service contracts.
04
Unit Test & Test Data Generation
For every piece of generated code, the platform auto-generates unit tests and the test data to run them. Edge cases from the enriched story feed directly into test scenarios. 60–80% of test coverage written before a human touches the keyboard.
05
Token Tracking & LLM Governance
Every AI call is tracked — tokens consumed per developer, per story, per sprint. Engineering leaders get full cost visibility. Budget controls and usage thresholds can be set per team. AI development costs stop being a black box.
06
Security Guardrails & Org Policy
Every generated commit is scanned against security guardrails and your organisation's coding policy — no hardcoded secrets, only approved libraries, required error handling patterns enforced. Compliant code is the default, not a post-merge audit.
How It Works

From raw requirement to compliant, tested code — without leaving the platform.

Input
Business Requirement
Plain language No tech spec
AI Agent
Story Enrichment
Acceptance criteria Edge cases Tech context
VS Code + VectorDB
Code & Test Generation
Codebase context Unit tests Test data
On Push
Track & Govern
Token tracking Change log Security scan
Output
Compliant, Tested Code
Policy-verified 60–80% test coverage
Natural Language App Creation

Describe it. Build it. Ship it — no code required.

Describe what you want to build. The platform generates the UI, data model, and logic. Iterate in plain language until it works exactly how you need it — then the generated code moves straight into the engineering workflow for production hardening.

1
Describe your app in plain language
No wireframes, no spec documents. Just describe what you want it to do and who will use it.
2
Iterate until the MVP is right
Refine in natural language. Change the layout, add a field, adjust the logic — the platform updates the app in real time.
3
Share the codebase with your dev team
One click shares the generated code, the AI's reasoning, and the business context with the engineering team inside the same platform.
4
Developers take it to production
The dev team uses the platform's full AI-assisted tooling — security hardening, integrations, performance tuning — to ship production-grade software from the validated MVP.
App Builder — Vibe Mode
Your Prompt
"Build a lead tracker where my sales team can log calls, set follow-up dates, update deal status, and see a pipeline board view."
Building your app — UI, data model, and logic...
Live Preview
Lead Tracker — Pipeline Board
MVP validated · Ready to share with dev team
Governance Layer

AI-generated code. Engineering-grade accountability.

Token Tracking & Cost Visibility
Every AI call — code generation, story enrichment, test creation — is tracked against the developer, story, and sprint that triggered it. Engineering leaders see exactly where AI spend is going. Budget thresholds can be set per team or per project. No surprise invoices.
Security Guardrails
Generated code is automatically scanned for hardcoded secrets, vulnerable dependencies, insecure patterns, and OWASP-class issues before any commit lands. Security isn't an afterthought — it's baked into the generation step itself.
Org Policy Enforcement
Your organisation's coding standards — approved libraries, error handling requirements, naming conventions, access control patterns — are encoded as policy rules. The AI uses them as constraints, not suggestions. Every generated function is compliant by default.
Get Started

Accelerate your engineering
velocity with AI.

We'll map the platform to your codebase, engineering workflow, and compliance requirements — and define what a deployment looks like for your organisation in a single discovery call.

View Case Studies