Make AI adoption
measurable and compliant.
Your engineers already have AI tools. We install the system that turns scattered, unmeasured use into a capability you can prove to the board and defend to compliance, an agentic SDLC framework, per-developer analytics, and executive ROI reporting, working as one.
You know AI helps.
You can't prove it, or govern it.
Three problems stall AI in most engineering orgs. This service is built to remove all three at once.
You can't show the CFO a number.
The Copilot and Claude seats are on the invoice every month. The return is invisible, no one can put a defensible figure in front of finance, so AI adoption stalls at "we think it helps."
Compliance blocks adoption.
Legal and security won't green-light AI on regulated code and sensitive data without enforced guardrails. So the highest-value teams are the ones forbidden from using it.
Every developer uses AI differently.
One engineer ships gold, another ships plausible nonsense, from the same tool. Output quality swings wildly, nothing is reused, and there's no way to level the floor.
Four parts, working
as one system.
Proven building TAFI, a natural-language app builder, to a 279/279 eval pass across ~1,615 commits. Each part has its own deep-dive, open one to see how it works. Read the TAFI case file →
Agentic SDLC Framework
12 specialist agents across 6 phases, a human approval gate at every one. Compliance enforced by reviewer agents, cost tracked per feature.
Explore SDLC FrameworkAI Engineering Intelligence
Jaadu, a per-developer analytics layer across Claude Code, Copilot and Codex. Seven views, from Anti-Patterns to Context Health.
Explore Intelligence (Jaadu)Automated ROI Reporting
Board-ready ROI at three altitudes, Executive, Team-Lead and Developer. Honest confidence bands, a narrative of what shipped.
Explore ROI ReportingKnowledge Architecture
Typed standing documents, domain rules, ADRs and guardrails, layered Project → Epic → Feature → Story. Knowledge that compounds.
Explore KnowledgeSix outcomes,
across the org.
Not a tool rollout, a change in how the whole engineering function adopts, governs and proves AI.
The variability in AI-assisted output quality goes away, a floor every engineer clears.
Serves six roles, PM/BA, architect, dev, QA, DevOps, leadership, not just developers.
Scoped context means lower token spend and higher quality at the same time.
PHI and compliance rules enforced by reviewer agents, at the framework level, not documented somewhere.
Approval becomes a 30-second decision: AI-ready markdown beside a human-ready summary.
Domain knowledge that gets better with every feature, instead of being re-explained each prompt.
AI as a tenured employee,
not a skilled freelancer.
Off-the-shelf tooling gives every engineer a brilliant stranger. The framework gives them a colleague who knows the codebase.
- Variable quality, every prompt a fresh gamble
- Re-explains the domain from scratch each time
- No memory of past decisions or patterns
- Compliance left to whoever is prompting
- Output you have to fully re-check, every time
- A consistent quality floor across the team
- Knows your domain, modules and guardrails
- Reuses patterns and prior decisions
- Compliance enforced by reviewer agents
- Approval is a 30-second, dual-format decision
Built and proven on five real production modules, Care Plan, Participant Chart, Wound Management, Encounter and Order Management, inside a regulated healthcare SaaS, and on TAFI, where the same agentic pipeline shipped 279/279 eval stories at QA 100 across ~1,615 commits.
Put a number on
your AI adoption.
A discovery call to map your stack, your compliance constraints and your team, then a plan to install the framework, the analytics and the reporting. You'll know what the ROI view looks like before we build it.