Enterprise Data Governance Platform
From a single requirements document to a production-grade, multi-tenant SaaS platform in under 12 weeks. An expert-directed, AI-executed pipeline replaced the traditional8 to 12 person team and 14 to 20 months timeline, delivering an automated, compliant, auditable system.
Expert-Directed
AI-Executed vs. Traditional 8 to 12 Person Team
Under 12 Weeks
vs 14 to 20 months (Traditional)
12
AWS Services Integrated
80+
API Endpoints Delivered
The Challenge
Data Governance Trapped in Email and Spreadsheets
Organizations that share sensitive data (research institutions, healthcare systems, government agencies) are legally required to execute Data Use Agreements (DUAs) before any data transfer. But the reality of managing those agreements was painful, manual, and fragile.
There was no system of record. No audit trail. No enforcement. Just email threads, PDF attachments, and spreadsheets that someone had to remember to update. The pain was real, and it was slowing down programs that depended on timely data access.
Data Use Agreements were negotiated over email chains with no version control, so teams lost track of what was agreed, when, and by whom
Signatures were collected as PDF attachments, offering no legal traceability and requiring manual re-filing for every amendment
Agreement status lived in spreadsheets: no centralized view, no automated reminders, no audit trail
Data access restrictions agreed in a DUA had no automated enforcement; once signed, compliance was purely manual
Cycle times stretched to weeks, delaying research programs and data sharing initiatives that depended on timely approvals
Scaling to dozens of simultaneous active agreements required proportionally more staff, which is an unsustainable model
How We Build with AI
The Multi-Agent Pipeline
One requirements document. Six specialized AI agents. A production platform. Each agent performs a discrete role: no context drift, no assumption shortcuts.
Input
requirements.md
Requirements
spec-requirements
EARS-format acceptance criteria derived from a single requirements document: complete, unambiguous, testable.
Architecture
spec-design
Complete system architecture, data models, API contracts, and 45 architecture decision records, all defined before a line of code.
Task Planning
spec-tasks
Atomic, reviewable implementation tasks breaking architecture into discrete, dependency-ordered work units.
Implementation
spec-impl
Production code generated per task: pure feature logic across backend, frontend, database, and infrastructure.
Security Audit
spec-review
Autonomous security review: RBAC, RLS, OWASP Top 10, injection risks, and access control, applied to every implementation task.
Test Generation
spec-test
Independent test suites generated by a separate agent: 197 unit tests + 86 Playwright E2E + full backend test suite.
Output
Production Platform
The Solution
End-to-End Data Agreement Governance
A secure, multi-tenant SaaS platform that manages the full lifecycle of Data Use Agreements, from drafting through enforcement.
Full DUA Lifecycle Management
From creation through negotiation, signing, amendments, and expiration: the entire agreement lifecycle in one place. Auto-generated DUA numbers, HHS DUA Policy fields pre-built, status tracked at every step.
Multi-Party Clause Negotiation
Both parties discuss and negotiate individual terms inline with threaded comments, with no email required. Every round of negotiation creates a new version with a complete edit history.
Digital Signatures with Legal Traceability
Canvas-based drawn signatures with exact timestamps and immutable signing history. Not a checkbox: a real signature, captured with full audit context.
Machine-Readable Rule Export
On signing, agreements are automatically exported as structured JSON policies, enabling external data platforms to programmatically enforce DUA restrictions without manual intervention.
Immutable Audit Trail
Every action (DUA creation, status changes, term edits, signatures, comments) is logged at the database level. Records cannot be altered or deleted. Compliance-ready from day one.
Multi-Tenant Architecture
Schema-level tenant isolation, not just row-level filtering. Multiple organizations share one platform with zero cross-tenant data leakage and independently configurable workflows.
Also Delivered
AI Value-Add
What Our AI Approach Added Beyond Code
The AI didn't just write code. It brought architectural rigor, security discipline, compliance intelligence, and operational quality that traditional development rarely achieves consistently.
Architecture-First Thinking
45 architecture decisions documented before a single line of code, ensuring system integrity by design.
Security by Default
OWASP Top 10 reviewed on every implementation task. Security isn't a phase; it's built into every step.
Compliance Intelligence
Full HHS DUA Policy (HHS-OCIO-CDO-2023-01-001) compliance built in, not bolted on after delivery.
Multi-Tenant Isolation
Schema-per-tenant, RLS on every table, ~$28/mo per organization. Isolation at the database level.
Async Event Architecture
18+ notification event types via SES + SNS async pipeline: scalable, reliable, loosely coupled.
Living Documentation
5 role-specific user guides auto-generated: Admin, Data Provider, Data Recipient, Reviewer, Auditor.
Zero-Downtime Migrations
23 Alembic migrations: every schema change forward-only, tested, and zero data loss.
Full Infrastructure as Code
9 CDK stacks, 12 AWS services, reproducible environments: dev, staging, and production from one codebase.
Self-Improving Quality Loop
3-agent review cycle on every change: implement → security review → independent test generation.
Expert-Validated Output
Every AI-generated artifact — architecture decisions, implementation tasks, security reviews, and test suites — was reviewed and approved by the senior architect before integration. AI executes; the expert decides.
The Impact
Enterprise-Grade. Production-Ready. Delivered at Speed.
1 vs 8 to 12
Experts to deliver enterprise platform
AI-powered delivery vs. traditional 8 to 12 person team
Under 12 Weeks
Delivery timeline
AI-assisted engineering vs. 14 to 20 months traditional
80+
API endpoints built
Across 12 AWS services with full test coverage
84%+
Automated test coverage
283 tests: 197 unit, 86 E2E, full backend suite
$55/Mo
Infrastructure cost
Pay-per-request · scales with usage · multi-tenant
72K+
Lines of code
32K backend · 37K frontend · 3K infra
20
Database tables
Row-Level Security enforced on every one
45
Architecture decisions logged
Before a single line of code was written
By the Numbers
A Different Kind of Development
The direct comparison: what this engagement delivered vs. what traditional development would have required.
Traditional Approach
This Engagement
Proven in ProductionTechnologies Used
The Stack Behind the Platform
Frontend
- Next.js 16 (App Router)
- React 19
- Tailwind CSS 4
- shadcn/ui
- TanStack Table
- React Hook Form
- TipTap (Rich Text)
Backend
- FastAPI (Python 3.12)
- SQLAlchemy 2.0 (async)
- PostgreSQL 16
- Pydantic 2.7
- Alembic (23 migrations)
- pytest / pytest-asyncio
AWS Cloud (12 Services)
- Lambda (API + Email)
- RDS PostgreSQL 16
- AWS Cognito (Auth)
- S3 (Storage)
- CloudFront (CDN)
- SES (Email)
- SNS / SQS
- Amplify
- ECR (Docker)
- EventBridge
- Secrets Manager
- CloudWatch
Security & Compliance
- JWT RS256 / JWKS verification
- Row-Level Security (RLS)
- Schema-per-tenant isolation
- HHS DUA Policy fields
- Immutable audit logging
- AWS Secrets Manager
- 3-tier VPC network
DevOps & Testing
- GitHub Actions + OIDC
- Infrastructure-as-Code (CDK)
- Vitest (197 unit tests)
- Playwright (86 E2E tests)
- Multi-environment (staging / prod)
- Automated deployment pipeline
Have a Similar Requirement?
This platform proves that production-grade, enterprise software doesn't have to take a year to deliver. An expert-directed, AI-executed pipeline can accomplish what once required a full team over 14 to 20 months. Tell us your requirement — we'll show you what's possible.