AI-Native Engineering · for CTOs and CIOs
AI speed. Your standards.
We install a governed delivery pipeline in your Jira or Linear, GitHub and CI/CD, run by a Claude Certified Architect. You see every change that breaks your rules, and who decided.
45 minutes · a written plan, whether or not we work together
Example leadership view with illustrative data: this week 142 changes shipped, 61% merged automatically, 9 held for a person and 4 deviations from your architecture rules logged, each with its risk and the decision taken.
ISO 27001
ISO 9001
- Clutch 5.0★ (opens in a new tab)
- Claude Certified Architects
What gets installed
Nine stages, spec to production. All in your tools.
Checks are automated where your rules are clear and handed to a person where judgment matters. Choose a stage to see what happens in it.
Spec from a template
Every change, including bug fixes, starts as a written spec in Jira or Linear: scope, acceptance criteria, the services it touches and any data changes.
Architecture check
The spec is checked against the architecture rules you have agreed: your decision records, database schema, API conventions and design system. Conflicts show up before any code exists.
Plan and architect sign-off
AI drafts an implementation plan covering files, interfaces, data migrations and tests. An architect approves the plan before any build starts.
Build and test in a sandbox
Code is written and run in an isolated sandbox. AI writes tests from the acceptance criteria, and they must pass before a pull request is opened.
Pull-request checks
Each pull request is checked automatically: a clear description and linked ticket, your architecture rules, security scanning and minimum test coverage.
Risk-scored merge
Every change gets a risk score based on how far it departs from your rules. Low-risk changes merge on their own; anything over your limit goes to a named approver.
Staging and scenario tests
The change runs in staging against a fixed set of known-good tasks and edge cases, not just unit tests.
Feature-flagged release
The change goes to a small share of users behind a feature flag and is watched against your normal error rates, speed and cost before it is rolled out further or rolled back.
Feedback loop
Tickets, decisions and incidents are linked automatically, so the next spec starts with the full history of what came before.
Autonomy is earned, not assumed: merge limits start strict and loosen only as the pipeline proves itself on your own work.
Your options
AI tools write code. Nothing governs it.
Many of the tools are ones you already run. What we add is the installed process from day one, and the people who keep it working. A pilot measures it against your current process before you commit.
AI-native engineering vs AI coding toolsBuild it in-house, or bring in Upcore?What is AI-native engineering?
Who does the work
Three experts, inside your team.
Every Upcore architect is Claude certified. To scale, we add pods rather than enlarging one, so each team gets the pipeline, the rules and the judgment.
Full-stack developer
Runs the pipeline day to day: drafts specs with your team, supervises what AI builds and keeps the checks green.
Claude Certified Architect
Maintains your architecture rules with your team, signs off plans, tunes the merge limits you set and reviews every high-risk deviation.
Analyst & client lead
Turns business requests into specs, runs acceptance with your stakeholders and is your single point of contact.
How we engage
Start with a pilot. Scale on evidence.
No price list. Every engagement is scoped after the discovery call, and you get a written proposal with a fixed scope and price before any build starts.
- Step 01
Pilot on one team
One team, one service, a real backlog. Prove it on real work before you commit further.
- Step 02
One-time implementation
A fixed fee scaled to the teams and repositories in scope: pipeline, checks, templates, dashboard and training.
- Step 03
Monthly retainer
An embedded Claude Certified Architect maintains your rules, tunes checks and limits, and reviews high-risk deviations as your system evolves.
The pilot, specified
- Your pod
- A full-stack developer, a Claude Certified Architect and an analyst who is your single point of contact.
- Measured against
- Success measures agreed up front against your current delivery process.
- You get
- The pipeline installed end to end, then a results review with you before any wider rollout.
- Duration & commercials
- Agreed on the discovery call, based on your scope and requirements.
Results
Already live in production.
- EngineeringIn productiondelivery agents inside a product teamA field-service SaaS company from a ServiceNow Elite partner group · South Africa
- Engineering4.9★App Store rating, 89 ratingsA booking and payments app for barbers, built with AI-first engineering · United Kingdom
- EngineeringRehiredto add test and DevOps agents to their release pipelineA dental implant network across nine states · United States
I have worked with Upcore many times on projects big and small. Their expertise, network, and professionalism is second to none.
Questions
Before you book a call.
How is this different from giving developers Copilot or Cursor?
Those tools generate code. AI-native engineering is the delivery process around them: the spec template, the architecture checks, the plan sign-off, the automated pull-request checks, the merge rules, the scenario tests, the controlled release and the decision record. Without that process, AI-written code still depends on manual review to be trusted.
Couldn’t our platform team build this ourselves?
You could, and many of the tools are ones you already run. What we add is the installed process from day one: spec templates, architecture checks, the plan sign-off, merge limits and the deviation log, plus an embedded Claude Certified Architect who maintains your architecture rules and tunes the checks as your system evolves. The pilot measures it against your current process before you commit.
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.
Who decides when AI-written code can merge?
You do. You set the merge limits. Only low-risk changes that follow your architecture rules merge on their own; everything above the limit goes to a named approver, and every decision is recorded on the dashboard.
What happens to our engineers’ roles?
They stop hand-writing routine code and prompts and move up a level: writing and reviewing specs, approving plans, owning architecture rules and handling the changes that genuinely need human judgment.
How do you handle security and intellectual property?
Code stays in your repositories and builds run in your environments. Security scanning is a mandatory check and builds run in sandboxes. Our security and quality management are certified to ISO 27001 and ISO 9001. Security details
Where is your team, and how do you work across 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, then a one-time implementation fee scaled to the teams and repositories in scope, then a monthly retainer for your embedded Claude Certified Architect. Duration and commercials are agreed on the discovery call, and you get a written proposal with a fixed scope and price before any build starts.
Try it on one team. Keep what works.
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.
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