Create a useful internal service catalog
Put approved application templates, service owners and operating guidance in one place. Teams choose a supported starting point for a knowledge assistant, an AI API or an internal workflow.
AI + Platform Engineering
Give every AI team a better starting point.
A self-service developer platform that turns approved AI building blocks into a repeatable path to production.
Discuss this approach
More time for product work
The business challenge
Every AI team rebuilds the same foundations: environments, model access, knowledge connections and release controls. Platform engineers become a queue for routine requests, while ownership and running costs become harder to track.
A practical delivery approach
Provide a controlled self-service path
Provision environments, identity, model access and spending limits through reviewed templates. Automate common setup steps while keeping approval for restricted data and higher-cost capacity.
Make quality part of every release
Package checks for answer accuracy, response time and access boundaries alongside release and rollback workflows. Give teams an understandable readiness view before production.
Business value
Built to make
a business difference.
Connect delivery decisions to growth, operating efficiency and customer trust.
Faster product progress
Spend more engineering time on customer problems and less on repeated setup.
Consistent operating controls
Carry ownership, access rules and budgets into every new AI service.
A platform teams choose
Measure adoption and remove the friction engineers actually experience.
Evidence over assumptions
Define how progress
will be measured.
Track progress against a shared baseline.
Time to first environment
Track the elapsed time from an approved request to a usable workspace.
Platform adoption
Track active teams and the share of new services using supported templates.
Production readiness
Review release lead time, failed changes and ownership coverage.
A controlled path to scale
Find friction
Baseline setup delays and select one common AI workload.
Prove the path
Pilot an approved template with a product team and its operators.
Expand adoption
Improve the experience before adding more workloads and teams.
The supporting technology
Selected around the workload, existing systems and agreed operating responsibilities.
Background reading:Backstage software templates
Move forward with QuantAimLabs
Let’s put this approach to work.
Tell us about your AI ambitions, platform challenges or infrastructure priorities. We’ll connect the next step to a clear business outcome.