AI Transformation Services That Reach Production
Governed AI transformation across the whole software lifecycle, run by senior engineers on your existing stack. From assessment to production, with human approval at every gate and no vendor lock-in.
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The pilot was never the hard part
Most AI programs produce a working demo and stop there. What decides the outcome is whether change reaches production under governance, and whether system knowledge survives the people who built it.
- Time to first demo
- Fast
- Reaches production
- Sometimes
- Review trail
- Ad hoc
- System context
- In people's heads
- Cost model
- Retained capacity
- Time to first demo
- Fast
- Reaches production
- By design
- Review trail
- Every gate, logged
- System context
- AppGraph, queryable
- Cost model
- Per approved change
From assessment to production, governed end to end
End-to-end AI transformation services across strategy, data, models, and applications. Every change is scoped, supervised, and traceable back to the business requirement that asked for it.
AI Strategy & Roadmap
Use cases prioritized by value and risk, with scope, timeline, and cost projected before work starts.
System Assessment
AppGraph maps your codebase, APIs, and infrastructure in days, scored across six readiness dimensions.
Agentic Workflows
Multi-step AI workflows that run real business and engineering processes under human approval.
AI Features in Existing Products
New AI capability added inside software you already ship to customers, with no rebuild.
Data & ML Foundations
AI-ready pipelines, feature stores, and retrieval systems grounded in your operational data.
Model Governance & Evaluation
Evaluation harnesses and approval gates so model behaviour is measured, logged, and signed off.
What Stops AI Transformation From Landing
The eight blockers we are called in to clear. Most teams recognise three or four at once.
Pilots never reach production
Promising proofs of concept stall because nobody owns the path from demo to deployed system.
No plan that survives the board
Leadership needs an AI roadmap that holds up to board, security, and audit scrutiny, not a slide deck.
Data isn't AI-ready
Fragmented pipelines and undocumented schemas make useful models hard to build and harder to trust.
Brownfield systems resist AI
New capability has to work inside real production stacks with real constraints, not greenfield demos.
Governance and audit gaps
AI-generated changes lack review trails, named accountability, and enterprise risk controls.
Unpredictable cost and scope
Open-ended transformation programs and per-seat economics make AI spend hard to forecast.
Senior AI talent is scarce
Experienced AI and ML engineers are hard to hire and retain, capping how fast the roadmap can move.
System knowledge walks out
Understanding lives in a few engineers' heads, so transformation stalls when they move on.
How a transformation actually runs
A governed lifecycle from business intent to production: AI executes, humans govern, AppGraph grounds the work.
Assess & Map
We scan your repositories and infrastructure to build AppGraph, a queryable model of your system, scored across architecture, dependencies, technical debt, and AI readiness.
Prioritize Use Cases
We rank candidate AI use cases by business value and delivery risk, then turn the top ones into scoped requirements with timeline and cost projections.
Prototype & Validate
Working prototypes land fast so you can confirm direction against real data and real users before committing to a full build.
Build with Supervised AI
Multi-model AI executes the high-volume work under senior-engineer supervision, grounded in AppGraph context at every step.
Harden & Deploy
Evaluation harnesses, security hardening, and human review at every governance gate, then production-ready code merges into your own repositories and cloud.
Operate & Expand
Monitoring feeds reliability and optimization work back through the same governed lifecycle, and the roadmap expands to the next prioritized use case.
AppGraph
A queryable model of your codebase, APIs, and infrastructure, built in days and scored across the six dimensions that decide how fast and safely your system can change.
AppGraph
Industries We Serve
We build for mid-market technology companies running real production systems across regulated and high-velocity industries.
FinTech & Insurance
Complex, regulated brownfield systems where governed AI must survive risk review.
Explore industryHealth & Life Sciences
Compliance-heavy environments, proven in HIPAA-regulated production at Nanox.
Explore industrySaaS & Software
Product teams adding AI features and copilots where lifecycle cost hits margin.
Explore industryE-commerce & Marketplace
High-complexity platforms needing continuous AI-driven velocity at scale.
Explore industryAdTech & Media
Multi-product AI delivery, like the 5× velocity gain delivered for Digital Remedy.
Explore industryManufacturing & Supply Chain
Operational automation and intelligent workflows across distributed systems.
Explore industryWhy teams pick us
A strategic transformation partner, not a tool vendor or a staff-augmentation shop, built for enterprise risk frameworks.
Supervised AI execution, not autonomous agents
AI does the high-volume work while named humans approve at every gate. A tool can propose a change; it cannot be accountable for one, and accountability is what production software runs on.
AppGraph system intelligence
A queryable model of your codebase, APIs, infrastructure, and undocumented decisions. System knowledge becomes a durable asset instead of leaving when an engineer does.
Your stack, zero lock-in
We operate on your repositories, CI/CD, cloud, and security controls with no proprietary runtime to migrate to. If you ever stop, everything you built stays in your environment.
You pay for approved changes
Not retained capacity or per-seat licences. Spend tracks delivered outcomes, and every change carries a full record from business requirement to release.
Outcomes you can measure
Structural benchmarks from real engagements, scoped and estimated before work begins, not guaranteed.
Eventric, built on LangBuilder and deployed on AWS.
Down from 2–4 week sprints at Nanox.
For equivalent lifecycle scope, measured structurally.
AI Transformation Services FAQs
Common questions about our AI transformation services and how we work.
What are AI transformation services?
AI transformation services take an organization from AI strategy through to AI capability running in production, rather than stopping at a pilot. CloudGeometry delivers them as a governed lifecycle: assessment, prioritized roadmap, supervised build, and operation on your existing stack. Senior engineers approve every change at a named gate, and each change traces back to the business requirement behind it. You keep your repositories, cloud, and IP with no vendor lock-in.
Why do most AI transformation programs stall?
Most programs stall because the pilot proves feasibility while nobody owns the path from demo to governed production. The blockers are rarely technical: unclear accountability, missing audit trails, data that isn't ready, and system knowledge held in a few engineers' heads. Adding AI tooling on top of an unchanged delivery process amplifies whatever was already there. Closing the gap means governing how change reaches production, not writing code faster.
How quickly can you get started?
We can start in days with a fixed-scope System Intelligence Assessment that maps your systems and prioritizes use cases by value and risk. AppGraph builds a queryable model of your codebase and infrastructure in days rather than months. Prototyping of priority use cases runs in parallel, so teams typically validate working value within the first few weeks. There's no long discovery phase or multi-year transformation program.
Do you work on our existing systems or build new ones?
We work on your existing systems. CloudGeometry is built for brownfield environments, live production software with real constraints, not greenfield demos. Work runs on your repositories, CI/CD, cloud, and security controls, with no proprietary runtime to migrate onto. If you ever stop, everything built stays in your environment.
How is AI-generated work governed?
Three named approval gates govern progression rather than a single review at the end. A Product Owner approves business intent, an Architect approves architectural direction, and an AI Lifecycle Manager approves release readiness. Every AI action is logged, and a human signs off before anything reaches production. Each engagement has a named Technical Manager accountable for lifecycle execution, so governance is a staffed role rather than a process diagram.
Do you work with companies our size?
We work with mid-market technology companies of 200–2,000 employees and $50M–$2B revenue running existing production software. Teams are typically 15+ engineers with an annual development budget above $500K. CloudGeometry is built for brownfield systems, not greenfield demos. We assess fit explicitly in the first conversation.
Is our code and data safe?
Your code and data stay in your environment, your repositories, your cloud, and your security controls. CloudGeometry operates on your stack with no proprietary platform to migrate to, and your code is never used to train any underlying model. Every AI action is logged and every change requires human sign-off before production. Per-client isolation means no cross-client data sharing, and deployment can run in your own VPC, on-prem, or air-gapped for sensitive workloads.
How do you price AI transformation work?
You pay for approved changes rather than retained headcount, so spend tracks results instead of capacity. Each change ships with a scoped timeline and cost projection agreed before work begins. Structurally, that runs at roughly one-third of traditional consulting cost for equivalent lifecycle scope. Engagements begin with a fixed-price, time-boxed System Intelligence Assessment.
Can't we just buy AI coding tools instead?
You can, but individual AI coding tools speed up developers without changing organizational throughput, governance, or accountability. To run a governed transformation internally you'd need system-wide context, an approval framework, a multi-model orchestration layer, and a dedicated engineering management function, none of which is your product. Coding assistants also leave system knowledge in individual engineers' heads, so progress stalls when they leave. A governed lifecycle preserves that knowledge as a durable, queryable asset.
Tell us what you want to build
You'll get a scoped plan with timeline and cost before any work starts. No staff augmentation, no lock-in, all IP stays with you.
- Fixed-scope assessment in days, not months
- Senior-engineer supervision at every gate
- Production-ready code, merged to your repos
Prefer to talk first? Book a call.
Thanks, we've got it.
An AI expert will come back to you with a scoped plan, timeline, and cost. Usually within one business day.