Every change starts from a standard spec in Jira or Linear, checked against your architecture rules before any code exists.
Automated checks run on every pull request: your architecture rules, security scanning and test coverage.
Each change gets a risk score. Low risk merges on its own; anything over your limit goes to a named approver.
Changes go out behind a feature flag, watched against your normal error rates, speed and cost, and every decision is logged for leadership.
AI-Native Engineering · for CTOs and CIOs
Ship AI-written code you can trust.
We install a governed delivery pipeline in your Jira or Linear, GitHub and CI/CD, so every AI-written change is checked, risk-scored and logged before it ships.
45 minutes · a written plan, whether or not we work together
Example. A routine change passes every check and merges automatically because its risk score is under your limit. A risky change breaks one of your architecture rules, scores above your limit and is held for an architect. Both decisions are logged.
ISO 27001
ISO 9001
- Clutch 5.0★ (opens in a new tab)
- DesignRush 5.0★ (opens in a new tab)
- Claude Certified Architects
The problem
AI writes more of your code. Review can’t keep up.
- 01
Review becomes the bottleneck
Someone senior reads every AI-written line, so the speed you gained disappears in review.
- 02
Architecture drifts quietly
Models don’t know your conventions. Small deviations pile up until they are expensive to unwind.
- 03
Leadership can’t see the risk
Nobody can say what AI changed, why it changed, or who approved it.
Put a number on it
What does reviewing AI code cost you?
Set the numbers for your team. Routine, low-risk changes that pass every automated check don’t need a senior engineer to read them line by line.
67hours of senior review every week
27of those hours go on routine changes your checks could clear
0.7full-time senior engineers, every week
An estimate from your own inputs, not a promise. A pilot measures the real figure against your baseline.
How it works
Four checkpoints. Every change.
Installed inside the tools your teams already use. You set the rules; the pipeline enforces them and a Claude Certified Architect keeps them current.
Results
Already running in production.
- Engineering4.9★App Store rating, 89 ratingsA booking and payments app for barbers, built with AI-first engineering · United Kingdom
- Automation60%+fewer delivery-support ticketsA national retailer: a WhatsApp order-status agent · South Africa
- Automation≈$210Ka year of licensed tooling replacedAn automotive compliance firm: a compliance-check agent · India
I have worked with Upcore many times on projects big and small. Their expertise, network, and professionalism is second to none.
See all resultsResults as reported from our engagements. Ask us for a reference call.
Start small
Prove it on one team first.
No price list and no long contract up front. Duration and commercials are agreed on the discovery call, based on your scope.
- Scope
- One team, one service, a real backlog.
- Your pod
- A Claude Certified Architect, a full-stack developer and an analyst who is your single point of contact.
- You get
- The pipeline installed end to end, measured against your current process, then a results review before any wider rollout.
From first call to results
- Step 01
Discovery call
45 minutes on your delivery process and where AI is already writing code.
- Step 02
Written plan
Scope, success measures and commercials for a pilot, in writing, whether or not we work together.
- Step 03
Pilot on one team
The pipeline installed end to end on a real backlog, inside your own tools.
- Step 04
Results review
The numbers against your baseline, before you decide anything wider.
What a pilot is measured on, against your own baseline
- Time from spec to release
- Senior hours spent on review
- Changes that fail in production
- Share of changes merged automatically
- Your code stays yours
It stays in your repositories, and builds run in your own environments.
- Not used to train models
Model providers are configured so your data is never used for training.
- Scoped, revocable access
Only the permissions a workflow needs, every action logged, revocable at any time.
- Certified processes
ISO 27001 and ISO 9001 certified, with delivery run to CMMI Level 3.
Not leading an engineering team?
The same governed approach runs AI across the rest of the business.
Who we help
Questions
Questions CTOs ask first.
How is this different from giving developers Copilot or Cursor?
Those tools generate code. We install the delivery process around them: spec templates, architecture guardrails, plan sign-off, automated pull-request gates, risk-scored merges, controlled releases and a decision record. Without that process, AI-written code still depends on manual review to be trusted.
Do we have to replace our tools?
No. We install the process inside what you already run: Jira or Linear, GitHub, your existing CI/CD and the AI assistants you have approved, such as Claude, GitHub Copilot or Cursor.
Where is your team, and how do you handle time zones?
Our delivery team is in India, with clients in the USA, UK, South Africa, Australia and Mauritius. Your pod’s analyst is your single point of contact, and working hours are agreed in the pilot plan.
What does it cost?
We don’t publish a price list. You start with a pilot on one team; duration and commercials are agreed on the discovery call. After the call you get a written proposal with a fixed scope and price before any build starts.
Let AI write the code. Keep your standards.
Book a 45-minute discovery call. We’ll review your delivery process and send a written plan for a pilot on one team, whether or not we work together.
Not ready for a call? Get your AI Governance Score in 2 minutes