The AI-Native
SDLC Framework
Five disciplines, applied to every artifact, by every SDLC role.
Most teams using AI in engineering treat it as a developer-side accelerator. Copilot in the IDE, ChatGPT in a tab. That’s where the discipline problem starts.
Our framework treats AI as a layer across the entire SDLC, from requirements through run, and applies the same five disciplines to whatever the AI produces, no matter who’s holding the keyboard.
01
End-to-End Coverage
AI is governed across every SDLC role, not just developers.
- BA, design, dev, QA, DevOps, SRE, doc: each role has its own configured Claude Code surface
- No ‘shadow AI’ use; if a role uses AI, the use is sanctioned and standardized
- Same framework applies whether the engagement is greenfield, modernization, or run-state
02
Collaboration
Role-to-role handoffs are contracts, not goodwill.
- BA → Dev → QA handoffs have machine-checkable definitions of done
- AI output from one role is a structured input to the next, not a Slack message
- Intra-engagement cohesion holds up even when teams are distributed
03
Standardization
CoE-managed hooks, exemplars, and gates make quality the floor, not the ceiling.
- Prompt patterns, output formats, and review criteria curated centrally
- Hooks enforce the rules at commit-time, with no reliance on engineer discipline
- Same quality floor for every artifact, every engagement, every engineer
04
Measurement
Leading and lagging indicators only. No vanity dashboards.
- Time-to-merge, defect-escape rate, AI-vs-human authorship ratios, tracked per engagement
- Outcome metrics tied to the original engagement scope, not the tool’s activity
- Quarterly review at portfolio level; per-account governance monthly
05
Security
Client data stays isolated. Audit trails are the default, not an add-on.
- No training feedback, no retention, no cross-engagement model contamination
- OWASP and secure-coding gates enforced deterministically, not by review checklist
- Every prompt, response, and merge is logged and replayable
The same framework we deliver for
clients powers our own AI agents.
We don’t deploy a framework on clients we wouldn’t run on ourselves. Our own AI products, the ones we sell, support, and stake our name on, are built using the exact disciplines on this page.
If the framework couldn’t produce maintainable, secure, audit-clean code for PROTEUS or NYX, we’d have replaced it before we ever offered it to you.
This is the operating model behind every engagement, not a methodology we wheel out for the sales conversation.
PROTEUS
Database migration agent. Built on this framework, deployed to enterprise migration engagements.
NYX
Intelligent ops agent. Built on this framework, powering SourceFuse's AI-led Managed Services engagements
ARGUS
QA and test-generation agent. Built on this framework, integrated into the SDLC for every engagement that ships code.
30% – 50%
Average SDLC time savings across delivery
40% – 50%
Peak efficiency in development phases
6
Deterministic check layers on every AI-authored change
100%
Audit-traceable AI activity, prompt to production
Four things you can point
to on day 90 or day 900
A repo you can read
Every commit, whether AI-authored or human, passes the same review trail and conformance checks. Maintainable by your team, not just ours.
An audit trail you can show
Prompt-level logging, model-version pinning, and replayable build artifacts. Pass an internal security review or a regulator’s question without scrambling.
Metrics tied to scope
We report against your engagement outcomes: time-to-cutover, modernization completion, defect-escape. Not against tool usage or token counts.
An exit you can take
No proprietary lock-in. ARC is open-source; the framework lives in your engagement repo. If the engagement ends, the code, gates, and audit logs stay with you.