Take one important thing from backlog to production.
Have a product feature that matters but hasn't made it into production? Bring it to Olensys. We'll assess the scope, define the delivery plan, and tell you honestly whether it's a good Sprint candidate.
4–6 weeks · One production-ready slice · Senior pod · Your repo, your cloud · Fixed fee, from $25,000
The bottleneck moved.
In short
The constraint has moved from writing code to making the right decisions, verifying changes, and getting them into production.
AI has reduced the effort required to produce code. It hasn't removed the engineering work required to release reliable software.
What changed for AI users, across 10,000+ developers
| Measure | Value |
|---|---|
| Tasks completed | +21% |
| PRs merged | +98% |
| PR review time | +91% |
| PR size | +154% |
| Bugs per developer | +9% |
Higher is worse for the last three.
Speed with AI: what 16 experienced developers believed, and what was measured
| Measure | Value |
|---|---|
| Believed | +20% |
| Measured | −19% |
METR’s Feb 2026 follow-up points the other way, on what METR calls weak evidence. The point stands either way: typing speed was never the constraint.
Velocity Sprint in brief.
01 · Defined delivery
One important product feature. One agreed scope. We select a feature with clear business value, define acceptance criteria, and agree on what production-ready means before implementation begins.
02 · Senior execution
A senior Embedded Pod owns the work. A pod lead directs architecture and technical decisions. Engineers implement, verify, and prepare the feature for release.
03 · Clear economics
Fixed scope. Agreed price. Your code. You know what is included, what is excluded, and what acceptance means before work begins. The resulting software and agreed engineering assets belong to you.
The tool changed. The system didn't.
In short
AI accelerates execution. Engineering controls determine what gets released.
AI changes how quickly engineering work can be produced. It doesn't replace specification, technical judgment, verification, or production controls.
What AI does
AI-assisted execution.
- Requirement breakdown
- Code generation
- Test generation
- Documentation
- Implementation assistance
What engineers do
Engineering judgment.
- Review architecture
- Approve specifications
- Evaluate alternatives
- Review generated changes
- Own technical decisions
What the system enforces
Delivery controls.
- Repository readinessGATE
- Approved specificationGATE
- Approved implementation planGATE
- VerificationGATE
- Production readinessGATE
- Release and measurementGATE
- GATE A check that must pass before work moves on.
Every change passes six gates before it counts.
In short
Nothing goes live until a named engineer has read it and the tests pass. Each gate leaves a record you can open.
AI can produce a change quickly. That doesn't make it ready for production. Every change must meet defined acceptance and verification criteria before release.
- GATE 0
Repo-ready
Correct repository, access, tooling, and environment.
Record: checklist committed in docs/agent-readiness.md
- GATE 1
Spec approved
Requirements and acceptance criteria agreed.
Record: the spec, its approval in the tracker, and the size class locked
- GATE 2
Plan approved
Implementation approach and technical decisions reviewed.
Record: plan.md and tasks.md
- GATE 3
Verified
Automated tests, evaluations, and relevant security checks completed.
Record: tracker status Verified, with its timestamp
- GATE 4
Production-ready
Deployment, operational readiness, and release requirements satisfied.
Record: release checklist in the PR or release ticket
- GATE 5
Released & measured
Change released and production performance measured.
Record: ledger row; tracker status Released
A gate that always passes isn't a gate. A change that fails verification returns for correction. It does not silently move forward.
Nothing gets released unverified. Here's what that means.
Production readiness is defined before implementation. A named engineer reviews the changes, automated checks provide evidence, and unresolved findings remain visible. Nothing is considered complete merely because the code runs.
Human engineering review.
A named engineer reviews technical decisions and generated changes against the agreed specification.
Automated verification.
Tests and evaluation suites check expected behavior before a change is accepted.
Workload-specific controls.
Security and operational checks reflect the requirements of the application. Controls are defined for the workload, not copied from a generic checklist.
What changes for you
Something real releases.
A production-ready feature replaces an item that has been sitting on your roadmap. Acceptance criteria define what complete means.
You get a baseline you'll keep.
You know the starting point, the scope, and the agreed measures. The delivery record shows what changed.
You see the delta.
You can see what was released, how it performs, and what remains. Results are measured against the agreed baseline.
Know your own numbers first.
In short
This works out what a feature cost you last quarter. If you have no engineering team yet, skip it; the Production Readiness Review gives you a starting point.
The Sprint starts with an agreed scope, baseline, and acceptance criteria. You see the delivery economics before implementation begins.
A size sets the features, the days, and the PRs per feature below. You can still move any knob.
Size M: a new screen or report. Move any of these and the figures follow; a new size brings its own.
96 PRs merged: 24 features × 4
× 8 engineers = 24 features released, rounded
Olensys’s illustrative assumptions for a typical feature of each size, not measured figures. Your tracker and git hold the real ones: type them under “Use your exact numbers.”
- Cost per released feature
- $15,000
- Team cost for the quarter ÷ features released
- Cycle time
- 21 days
- Median, accepted → live
- Review wait a quarter
- 2,304 hours
- 96 PRs × 24 hours each
- Rework rate
- 15%
- About 4 of 24 features changed again
Last quarter, a team of 8 cost $360,000 and released 24 features. Each one cost $15,000 and took 21 days from accepted to live, and 4 came back within 30 days. Review wait comes to 2,304 hours a quarter, and the rework rate is 15%.
Use your exact numbers
From your tracker and git, last quarter. Anything you type replaces the knob above it; leave a field blank to keep the knob.
Illustrative until you enter your own · your numbers only
Assumes 4 PRs per released feature. Cost per released feature = engineers × monthly cost × 3 months ÷ features released. Review wait a quarter ≈ median wait × PRs merged. Rework rate = changed within 30 days ÷ released.
The scope defines the price. Changes to scope are discussed and agreed before additional work begins.
How it runs
- WEEK 0
Scope.
One feature. Clearly bounded. Define the feature, dependencies, acceptance criteria, and production requirements in writing.
Artifact: Signed scope - WEEK 1
Baseline.
Your starting point. Your numbers. Agree on delivery expectations, current performance, and the measures used to evaluate the result.
Artifact: Baseline sign-off - 2–4 WEEKS
Build.
Senior pod. AI-assisted delivery. The Embedded Pod implements the feature, reviews changes, and verifies progress against the approved specification.
Artifact: Working releases in staging - LAST WEEK
Read-out.
Working software. Measured delivery. Review the released feature, delivery evidence, and remaining recommendations. Decide whether further work makes sense.
Artifact: Production software and the read-out
4–6 weeks from kickoff. Week 0 is scoping, before the clock starts; the build runs 2–4 weeks, set by the slice.
Clear scope. Clear price. No surprises.
One agreed price for the defined scope. You review the deliverables, exclusions, and acceptance criteria before work begins.
One agreed price.
A fixed price for the agreed feature and scope. Any change to the scope is discussed before additional work begins.
No lock-in.
Nothing here ties you to an ongoing engagement. Further work is a separate decision.
You keep it all.
The software, code, and agreed engineering assets created through the engagement belong to you, subject to the contract.
Three ways in.
Start with clarity. Prove delivery. Scale what works.
Production Readiness Review
01
1 week
$999fixed fee
Know what you're inheriting before you invest further.
Have an AI system that works in a prototype but isn't ready for production? Assess its architecture, evaluation, security, reliability, and operating economics.
Scope One system · Up to three integration points
Velocity Sprint
Core engagement02
4–6 weeks
Starting at$25,000
One production-ready slice. Measured against a signed baseline.
Have one important product feature waiting in your backlog? Define the scope, agree on the price, and take it through implementation and production release.
Fixed fee. Scope agreed before kickoff. Additional work is assessed and approved separately.
Scope One service · One environment · Defined acceptance criteria
Embedded Pod
03
Ongoing
CustomScope and pricing agreed before the engagement begins.
Your roadmap. Senior engineering. Sustained delivery.
Need ongoing engineering execution beyond a single feature? An Embedded Pod works alongside your team, takes ownership of defined engineering work, and releases into your environment.
Team composition and capacity are agreed upfront. Priorities reset monthly. Thirty days' notice to end the engagement.
Scope Defined team capacity · Agreed monthly priorities