Product owners with a live system and an agent or model proposal
Services
Data & AI Consulting and Advisory
Advisory for teams that already have a live system and need a written view on whether the data, control boundary, and inference economics can support an agent or model in production.
Delivery Flow
AI-native structure with human oversight
Live
Flow Map
Structured stages with human sign-off at each gate
Architecture
01
Diagnose
Structured problem framing and success metrics.
02
Architect
Solution design with human sign-off before build.
03
Build
AI-orchestrated implementation with continuous validation gates.
04
Validate
Outcome testing against business metrics, not only technical acceptance.
05
Transfer
Clean IP handover, knowledge transfer, and exit documentation.
Delivery Mesh
Human review at every critical gate
Synchronized
AI orchestration
Coded steps move through each gate
Human oversight
Architecture, security, and quality
Validation gates
Business outcomes stay in view
Hero
A production-readiness opinion on the data, control, and cost under a proposed AI system.
Best for a product owner who has a pilot or a board question, and needs a decision on one live system before a larger build.
Includes: production-readiness score, context and control boundary, go / no-go with an inference cost ceiling.
StartPremium deliveryClear ownership
What it unlocks
Advisory for teams that already have a live system and need a written view on whether the data, control boundary, and inference economics can support an agent or model in production.
Engagement tone
Calm, precise, and structured for international teams.
Who it is for
The kinds of teams and situations this service is built for.
Leadership teams asked to fund AI before the data is production-ready
Operators who need a written control boundary before rollout
What we deliver
The practical outcomes and deliverables that define the engagement.
Production-readiness score
Score the data under the proposed agent or model for trust, lineage, and fitness for production use.
Context and control boundary
State what the system may see, what is retained, and where human review stays in the loop. TheiaOne does not train foundation models on client data.
Go / no-go and cost ceiling
Close with a written decision and an inference cost ceiling, handed to a Diagnose Sprint or Client Partner retainer.
How we work
A simple delivery path that keeps the work moving without losing clarity.
01
Diagnose
Frame the decision, the live system, and the success measure before any opinion is written.
02
Inspect
Review the data path, access boundary, and current operating controls on that system.
03
Decide
Issue the score, the control note, and the go or no-go.
04
Hand off
Route the next step to a Diagnose Sprint or Fractional CTO / Client Partner oversight. No open-ended advisory retainer.
Engagement model
How the work is typically structured with clients.
One system
The opinion is on one live system and one decision, not a transformation programme.
Written output
The engagement ends with artefacts the product owner can take to a build or a stop decision.
No model training
Client data is not used to train foundation models.
Related capability
A connected service path when the engagement needs a broader or more focused modernisation scope.
FAQ
A few common questions about the service and how it is delivered.
Is this a strategy roadmap?
No. It is a production-readiness opinion on one live system, with a go or no-go and a cost ceiling.
Do you train models on our data?
No. TheiaOne does not train foundation models on client data.
What happens after the opinion?
The next step is a Diagnose Sprint or Client Partner oversight. A build is sequenced only after that paid relationship.
Final CTA
Ready to shape the next phase of your product or platform?
The next step is a Diagnose Sprint on one live system. A build follows only after that decision.