CTOs and technical founders
Get a credible path from AI coding experimentation to accountable delivery.
Speed is no longer the scarce asset. Controlled speed is.
Move faster with AI. Keep control of what ships.
Agents can produce the work. SDF Front Door makes it reviewable: we review your repo, install it where it fits, and help run one governed change before your team decides what ships.
The gap
AI can make delivery faster. It can also make risk harder to see.
Unmanaged AI-assisted delivery can turn speed into unclear intent, unreviewable PRs, fragile changes, weak verification, security or data risk, wasted agent loops, and rework that compounds faster than the team can inspect it.
The bottleneck moves from writing code to deciding what should be done, reviewing what changed, proving what was checked, and handing over work the team can still maintain. Review confidence becomes the constraint.
When something goes wrong, the leadership question is not which model wrote the code. It is what was asked, what changed, what was checked, who reviewed it, and why the work was safe enough to trust.
SDF is not more process and does not force every engineer into the same AI tool, agent loop, IDE, or personal workflow. It packages the delivery habits serious teams already care about - intent, acceptance criteria, scope boundaries, risk notes, verification evidence, review context, and handover - into a shared quality bar at the PR/MR boundary.
Unclear intent
Prompts, scope, evidence, and review boundaries become inspectable instead of disappearing into chat history.
Review overload
AI-assisted PR volume can grow faster than review habits. SDF gives teams a governed path to attach usage signals and review evidence where available.
Excessive agency
Human approval remains the gate. Evidence supports review, not automatic approval, merge, deploy, or repo mutation.
AI-generated defects
Playbooks, acceptance criteria, verification evidence, and risk/confidence limits make the quality bar explicit.
Dependency sprawl
Agents can add packages before ownership, upgrade burden, or hidden-domain impact is reviewed. SDF makes dependency changes reviewable decisions before they land.
Accountability without evidence
The governed record shows what was asked, what changed, what was checked, and where risk remains instead of leaving leaders accountable for undocumented judgement.
What happens next
A simple path: review the repo, install Front Door if suitable, run one governed change, then repeat what works.
1
Review the repo
Share the AI-assisted delivery goal, lightweight repo context, and any safe supporting context so suitability can be checked first.
2
Install Front Door if suitable
Where the repo is a fit, install the check-only SDF Front Door workflow with human review, verification, and explicit non-claims.
3
Run one governed change
Choose one bounded useful change and run it through a governed PR with review evidence, verification status, risk notes, and AI usage signals where available.
4
Keep control
Your team reviews the PR, decides what merges, controls deployment, and uses the proof to decide what should become repeatable.
Who it is for
Built for engineering teams adopting AI coding tools where trust matters.
VPs and Heads of Engineering
Find uneven practices, unclear review expectations, and governance gaps before they scale.
Staff engineers and tech leads
Evaluate whether AI-assisted work arrives with enough intent, evidence, verification, risk context, and ownership to review responsibly.
AI power users and cautious reviewers
Power users can keep advanced workflows while sceptical engineers get a clearer PR/MR surface before being asked to trust the output.
The offer
Start your repo review.
The first step is suitability: whether your repo, review path, and verification surface are a fit for SDF Front Door.
You get a private view of visible blockers, hidden-risk areas, and whether a Front Door install plus one governed change makes sense.
Where the repo fits, the path stays practical: install the check-only Front Door, choose one bounded useful change, and prove the model in a governed PR before any broader rollout. Speed is the promise; governance is how you keep it.
The first governed change is chosen to be useful enough to matter and bounded enough to prove safely. Where suitable, it can be work your team already has lined up, not a throwaway demo.
Full reports stay private and are shared only with the customer or agreed reviewers.
Repo suitability review
Repo signals, visible blockers, hidden-risk areas, and whether SDF Front Door is a good fit. Private customer-facing review, not a hosted scan or public sample.
Front Door install path
Operator-reviewed evidence mapping, blocker classification, risks, limits, and a check-only install path where suitable.
One governed change
One governed PR with useful work, verification, PR reviewer-surface evidence, and explicit boundaries attached, without guaranteeing delivery of any arbitrary requested feature.
Customer-controlled decision
Your team keeps review, merge, and deployment control while deciding whether the pattern should become repeatable.
What you see
Practical delivery outputs, not process theatre.
The first useful output is clarity: where the repo appears ready, where review confidence is weak, where quality or maintainability risk could slow the team later, and what a safe next step should be.
Where a Front Door install is useful, AI-assisted PRs move faster while serious changes still carry clear intent, acceptance criteria, verification notes, risk/limit notes, reviewable evidence, ownership context, and handover guidance.
Private suitability readout
Visible blockers, hidden-risk areas, and a recommended next step based on lightweight repo signals and shared context.
Front Door path
Where useful, deeper evidence review, blocker classification, risk/limit notes, and a check-only install path.
Governed change evidence
A bounded PR path with reviewer evidence, verification notes, and handover context attached before broader rollout.
Oversight and control
SDF prepares the evidence. Your team keeps the decisions.
SDF comes from 20+ years of owning software delivery from idea to production. The aim is not extra process. It is keeping the practices that protect quality, maintainability, security, and customer trust when AI makes the work move faster.
The current workflow is assisted, human/operator-reviewed, evidence-backed, and customer-controlled. SDF prepares evidence, recommendations, and governed workflow guidance; your team approves scope, reviews PRs, and controls merge, deploy, and production decisions.
Human/operator reviewed
A human checks evidence and boundaries before handoff; SDF does not claim automatic approval, merge, deploy, repair, or enforcement.
Customer-approved scope
Your team decides what can be reviewed, approves the first governed change scope, and keeps product judgement.
Customer-controlled delivery
Your team reviews PRs and controls merge, deploy, production acceptance, and any repo access decisions.
Workflow proof
Anatomy of a governed AI-assisted PR.
Agents can produce the work. SDF makes the work reviewable.
In an unmanaged AI coding workflow, the PR is often just a diff. SDF makes the PR the proof surface: what was asked, what changed, what was checked, what risk remains, and how AI was used where available. SDF starts in check-only, review-led mode, and human approval remains the gate before work is trusted, merged, or applied. Governance stays constant; evidence scales with risk.
A controlled Campfire-style proof showed SDF catching an incomplete PR reviewer surface, updating the PR description from governed evidence with explicit permission, and detecting a wrong-base PR publication issue. That supports review confidence; it does not prove code correctness.
Before / after
What changes when delivery becomes reviewable.
The repo review keeps this practical: it looks for the workflow gaps that decide whether AI-assisted delivery can move faster without making the software harder to inspect, change, support, and trust.
Before SDF
- The PR is mostly a diff.
- Intent lives in a prompt or chat thread.
- Acceptance criteria are implied.
- Risk is reviewed late, if at all.
- Verification is scattered across comments, CI, and memory.
- Handoff depends on undocumented judgement.
After SDF
- Intent travels with the work.
- Acceptance criteria are explicit.
- Risk, confidence, and limits are recorded.
- Verification evidence is part of the delivery record.
- Reviewers see what changed, why it changed, and what was checked.
- AI-assisted delivery becomes easier to inspect, govern, and trust.
Why SDF works
Built to protect speed, not slow it down.
SDF came from real delivery pressure: teams want AI speed without losing engineering standards, architecture, testing, review, and ownership.
The operating model is tested before it reaches a customer repo. It is dogfooded across real apps, local and cloud agents, providers, models, reasoning settings, and workflow failure modes so customers do not have to run every experiment themselves.
The point is not governance at all costs. SDF adds the minimum useful control needed to keep agentic delivery fast, reviewable, testable, sustainable, and safe for a team to absorb.
This is credibility support, not a claim of automatic approval, universal repo support, hosted enforcement, measured savings, or production governance.
Visit the Research Lab
Built from delivery pressure
The method keeps the delivery habits serious teams already rely on when AI increases output volume.
Tested before your repo
SDF learns from governed work across apps, agent surfaces, model choices, reasoning settings, and failure modes before customer use.
Governance that protects speed
Controls stay focused on intent, acceptance criteria, risk, verification, evidence, review, and handoff.
Evaluation questions
Questions teams ask when evaluating Software Dark Factory
Based on real evaluation conversations with teams looking at governed AI-assisted delivery, these are the practical questions that usually come up before a first assessment or PoC.
What is the main benefit beyond code generation?
The benefit is not just faster code. It is repeatable AI-assisted delivery with clearer specs, controlled work units, evidence-backed PRs, reviewable handovers, test visibility, and maintainability discipline.
Can we build some of this ourselves?
Yes. Strong teams can assemble parts of this with AI coding tools, CI, PR templates, and internal standards. SDF productizes the shared quality bar so power users can keep advanced workflows while reviewers get a consistent PR surface for intent, evidence, verification, risk, and ownership.
Does SDF only check code and tests?
No. Code and verification matter, but many delivery risks hide outside the diff: product rules, ownership boundaries, provider coupling, permissions, persistence, and approval authority. SDF helps make those domains explicit for human review before work is approved.
Does our source code leave our environment?
The intended model is that source code, branches, PRs, specs, prompts, run logs, test output, and review evidence can remain inside your approved environment. The current workflow is assisted and scoped before deeper integration.
How would we evaluate it safely?
Choose one bounded work item, journey, or workflow. Run the same spec and acceptance criteria through your current workflow and an SDF governed workflow, then compare review evidence, rework, test visibility, handover quality, and cost visibility.
How does this help with AI delivery costs?
Model spend is visible, but review burden, failed work, rework, audit preparation, and unmanaged risk are often hidden. SDF does not claim measured savings. It captures AI usage signals where available so teams can review whether work used the right context, model, scope, and reasoning effort.
What is this not?
It is not a self-serve hosted scan, automatic repair, hosted enforcement, security certification, or automatic approval, merge, deploy, or repo mutation. Start with readiness, then prove the approach on a bounded governed PR where it fits.
Have a different evaluation question? Start your repo review and we will agree the safest next step manually.
Start your repo reviewNext step
See if your repo is suitable for SDF Front Door.
Tell us what you are trying to scale with AI-assisted delivery. We will review suitability first, then follow up manually if a Front Door install and first governed change path makes sense.
Human-reviewed, customer-controlled, and scoped before any install. No automatic approval, merge, deploy, hosted enforcement, or repo mutation.