Assessment journey
AI-assisted delivery is already happening. The question is whether your team can review, evidence, and govern it with enough confidence to trust it.
The free snapshot looks for visible workflow gaps and lightweight repo signals. If there is a fit, the paid package goes deeper into evidence, blockers, risk, hidden boundaries, and the safest bounded change to prove first.
Prove your team can use AI-assisted delivery on real work while keeping the review trail, owner control, and accountability visible.
The snapshot is private, lightweight, and read-only. It is not a self-serve scan, not hosted enforcement, and does not mutate your repository.
Free vs paid
The free snapshot is the private entry point. The paid package is the evidence-backed readiness assessment plus a safe first governed change path.
Private entry point
Evidence-backed next step
Buyer journey
Share visible workflow gaps, lightweight repo signals, and shared context. Agree what is safe to review, then receive a private customer-facing snapshot showing likely fit, obvious gaps, and whether deeper assessment is worthwhile.
Use the snapshot to decide whether paid readiness assessment plus first governed change proof is the right next step, or whether another path is safer.
Evidence review, blocker classification, gap discovery, advisory hardening, and selection of a safe first change where suitable. The report explains readiness, risk, why that change was selected, and what operating model should follow. Scope-agreed and human-reviewed throughout.
Run that selected change through one bounded governed PR with evidence for intent, risk, verification, review, and AI usage where available. Where suitable, it can be useful work the team already has lined up.
After the proof PR, SDF helps your team adapt the operating model to your repo, stack, review process, and AI delivery risks.
What we review — and what we do not
Sharing a public repo URL, screenshots, CI/review notes, manual context, or an access preference helps scope the conversation. It does not connect GitHub, clone a repo, start scanning, mutate code, enforce rules, start automatic assessment execution, repair code, monitor activity, or make production changes.
Repo signals and team context help prepare the right conversation and avoid asking for the wrong evidence.
What can be reviewed, what stays out of scope, and what evidence is available are agreed before review work starts.
Early assessments stay review-led. Shared context is not a repo connection, hosted scan, or automation trigger.
V0 boundary
The assessment is assisted, scope-agreed, and human-reviewed. V0 does not claim hosted scanning, hosted enforcement, automatic repair, automatic approval, automatic merge, billing-grade cost, measured savings, or continuous monitoring. `automatic_execution_permitted: false` remains the boundary.
Evidence review
The paid readiness assessment reviews observable delivery surfaces and hidden boundary signals across product, commercial, operational, and governance domains. It is evidence-backed, agent-assisted, and human/operator-reviewed. It is not a deep codebase scan, security certification, legal review, or automated analysis product.
SDF starts with delivery fundamentals that already matter: good boundaries, reviewable changes, verification evidence, risk management, proven design patterns, and disciplined handoff. The difference is that those practices now need to work with agents in the loop.
The assessment also looks at whether the feedback loop can stay useful as AI-assisted work scales: focused checks while work is being shaped, fuller verification before handoff, and clear rationale when a narrower verification path is used.
Whether the repo has visible verification surfaces that can support governed delivery.
How work reaches review, who approves it, and where review expectations are visible.
Run logs, PR evidence, acceptance criteria, and traceable delivery notes where they exist.
Signals for ownership, release confidence, and customer-owned approval boundaries.
Product rules, commercial commitments, operational ownership, permissions, provider coupling, persistence, and approval authority that may need explicit human review.
How AI-assisted work is already entering the delivery process and where governance gaps appear.
Assessment output
You walk away with useful work delivered as a governed PR proof, plus the report that explains readiness, risk, why that change was selected, and what operating model should follow. The package is designed to produce engineering assurance, not just advice: observed repo signals, readiness interpretation, evidence gaps, blockers, a bounded PR, and review evidence attached.
The current cloud-agent proof strengthens that path: SDF can attach governed evidence to agent-generated work, check whether the PR reviewer surface exposes that evidence, and, with explicit permission, remediate a PR description from the governed record. That supports reviewer confidence; it does not claim code correctness or automatic merge.
Paid assessment coverage
The GTM site describes assessment outcomes without exposing full free or paid report artifacts, Markdown templates, or PDF downloads.
Full reports remain private customer-facing artifacts and may be shared in customer delivery or investor/advisor conversations.
Paid readiness assessment + first governed change
Where the repo appears to stand for governed agentic delivery.
The delivery signals, review surfaces, and evidence trails that support the finding.
Gaps that need attention before governed AI-assisted delivery can be trusted.
Practical improvements that make the path clearer or safer.
Where AI usage, review burden, rework risk, verification effort, and evidence gaps may create hidden operating cost.
What is still uncertain, out of scope, or not claimed.
The safe bounded change to prove first, with the PR reviewer surface checked against the evidence where the evidence supports it.
After the report
Recommended next step
The assessment does not automatically remediate or enforce anything. It identifies a practical next step that can become reviewed implementation work when the scope is useful enough to matter and bounded enough to govern safely.
The first governed PR is not theatre. Where suitable, it can be a real change the team already has lined up: a bounded feature, fix, refactor, dependency update, documentation boundary, or workflow improvement. SDF does not rely on one fixed evidence burden for every change: low-risk copy work should not carry the same depth as provider, deployment, security, customer-routing, approval, or production-boundary changes. For teams that need more confidence, the proof can extend across a small sequence of bounded governed changes before SDF helps your team adapt the operating model.
Want to see the end-to-end proof? Watch the controlled demo walkthrough.
Next step
Know where your repo stands, what blocks governed delivery, and what to fix first.
Tell us what you are trying to scale with AI-assisted delivery. We will start with a free repo-backed readiness snapshot and follow up manually to confirm whether a paid readiness assessment plus first governed change is worthwhile.
