For CTOs and CIOs of 50–500-engineer companies
More AI code. Less risk.
Your engineers already use Copilot, Cursor and Claude. We add what’s missing: a governed delivery process with automated gates, visibility of AI spend and risk, and delivery agents inside your tools.
45 minutes · a written plan, whether or not we work together
- Spec from template · Jira / Linear
- Guardrails checked · ADRs & schema
- Plan approved · architect (a person decides)
- Built & tested · sandbox
- Gates passed · SAST & coverage
- Released safely · feature flag
ISO 27001
ISO 9001
- Clutch 5.0★ (opens in a new tab)
- Claude Certified Architects
What we do
Three ways we help engineering teams.
Each stands alone. Together they make AI-assisted delivery governed and visible to leadership. See the full AI-Native Engineering pipeline
- 01
AI governance and spend visibility
Dashboards for AI tool and token spend by team and user, where your tools expose usage data; controls that keep personal data out of AI tools; and review of the quality of AI-written code.
Know what AI costs and what it changes - 02
AI-Native Engineering pipeline
A governed spec-to-production process inside Jira or Linear, GitHub and your CI/CD: architecture guardrail checks, architect plan sign-off, AI-written tests, automated pull-request gates, risk-scored merge rules and feature-flagged releases.
Governed delivery without more review load - 03
Delivery agents and engineering pods
Agents for requirements, user stories, developer onboarding, testing and DevOps, plus lean pods (developer, Claude Certified Architect, analyst) embedded in your delivery.
More shipped per engineer
Logos show integration compatibility, not partnership.
Before and after
What changes for your engineering leaders.
- Review
Today: Every AI-written pull request needs a senior engineer’s full attention.
With Upcore: Automated architecture, security and coverage gates catch issues before a person reviews; people focus on what is genuinely risky.
- Visibility
Today: Nobody can say what AI tools cost per team, or what they changed.
With Upcore: Spend, gate results and deviations are on one dashboard you can take to the board.
- Releases
Today: AI-generated changes ship with the same risk as hand-written ones, or more.
With Upcore: Changes go out behind feature flags, monitored for errors, latency and cost, then promoted or rolled back.
- Knowledge
Today: Decisions live in pull-request comments and are forgotten.
With Upcore: Tickets, decisions and incidents are linked automatically for the next spec.
Results
Already live in production.
Led by a Claude Certified Architect, with a Claude-certified engineering team.
- EngineeringIn productionThe client belongs to a ServiceNow Elite partner group. Our delivery agents for requirements, user stories and developer onboarding are built into the team’s product workflow.Field-service SaaS company · South Africa
- Engineering4.9★Built with AI-assisted engineering practices and live on both stores: 4.9★ on the App Store (89 ratings), 4.5★ on Google Play (5,000+ downloads).Booking & payments app for barbers · United Kingdom
- EngineeringRehiredHired us twice: first for ten automated operational workflows and a clinician voice tool, then to add test and DevOps agents to their software release pipeline.Dental implant network · 9 states · United States
I have worked with Upcore many times on projects big and small. Their expertise, network, and professionalism is second to none.
How engagements work
Start with a pilot. Expand on results.
No price list: the model below is scoped to your organization after the discovery call.
- Step 01
Pilot on one team
One team, one service, a real backlog. We install the pipeline end to end and measure it against your current process. Duration and commercials are agreed on the discovery call.
- Step 02
One-time implementation
A fixed fee, scaled to the teams and repositories in scope: pipeline, gates, templates, dashboard and training.
- Step 03
Monthly retainer
An embedded Claude Certified Architect who maintains your architecture rules, tunes gates and reviews high-risk deviations.
Questions
Questions CTOs ask.
Can you show us what our AI tools cost today?
Yes, where your AI tools expose usage and billing data. The governance dashboard breaks down AI tool and token spend by team and user, so you can see where spend produces output and where it doesn’t.
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, merge rules, scenario testing, controlled releases and a decision record.
Do we have to replace our tools?
No. Everything runs inside Jira or Linear, GitHub, your existing CI/CD and the AI assistants you have already approved.
Who decides when AI-written code can merge?
You do. You set the thresholds; only low-risk changes that conform to your architecture rules auto-merge, and everything else goes to a named approver.
How do you handle security and IP?
Code stays in your repositories and builds run in your environments. Security scanning is a mandatory gate. See the enterprise controls above for certifications and data handling.
What does it cost?
We don’t publish a price list for this work. It depends on the workflows, volumes and systems involved. Pilot duration and commercials are agreed on the discovery call, based on your scope and requirements, and you get a written proposal with a fixed scope and price before any build starts.
Trust every AI-written change you ship.
Book a 45-minute discovery call. We’ll review your current delivery process and outline what a pilot on one team would look like.
Not ready for a call? Get your AI Governance Score in 2 minutes