AI Solutions & Engineering
Build AI products around a problem worth solving.
At a glance
- Best for
- Teams with a clear business problem and an idea for using AI to solve it
- Typical engagement
- Fixed-scope or ongoing, agreed to fit the work.
- Typical duration
- Varies by scope; agreed at the start of the engagement.
- Team
- Sized to the scope, drawn from our delivery team.
The problem
Where this costs you time.
Ideas without evidence
AI features get proposed without checking whether the data supports them.
Results nobody can trust
Outputs look plausible but are never measured against what is correct.
Prototypes that stall
Demos impress in a meeting but never reach daily use.
What we do
Capabilities
Problem and data assessment
Checks that the problem is real and the data is good enough to use.
Evaluation design
Agreed measures for accuracy and usefulness, set before the build.
Product engineering
Interfaces, APIs and integrations that put the capability in front of users.
Access and data controls
Permissions and data handling designed in, not added at the end.
Monitoring and retraining
Tracking of output quality in use, with a plan for when it drifts.
Human review design
Clear points where a person checks, approves or overrides the result.
Benefits
What changes when this works.
Outcomes we design for. Measured results appear only after a client confirms them.
- AI features that solve a stated problem
- Measured quality, not impressions
- A route from prototype to production
- Human oversight built into the design
Process
How ai product development engagements run.
Stage 1
Discover and Diagnose
Business, workforce and compliance analysis.
Tech deliverablesProcess map, data-flow map, risk registerStage 2
Design and Align
Solution architecture, operating models, integration planning.
Tech deliverablesArchitecture, prototype, test planStage 3
Implement and Enable
Deployment, onboarding, system validation, change management.
Tech deliverablesBuild, integrate, test, trainStage 4
Govern and Optimize
Continuous compliance monitoring, reporting, automation and enhancement.
Tech deliverablesMonitor, support, improve
Technologies
Tools we would use.
Models
- Large language models
- Classification
- Forecasting
- Computer vision
- Recommendation models
Engineering
- Python
- TypeScript
- REST and event APIs
- Containerised services
- Vector databases
Operations
- Evaluation harnesses
- Model monitoring
- Audit logging
- Feature stores
- Drift detection
Industries
Where this matters most.
Oil and gas
Models for equipment and site data, tested on real operating records before use.
Pharmaceuticals
AI support for regulated records, with every output traceable and reviewed.
Chemicals and fertilizers
Process and quality predictions checked against plant measurements.
Related
Where this connects.
- Generative AI ApplicationsAssistants and tools that draft, summarise and answer from your own content.
- Grounding on your documents and data
- Drafting and summarisation tools
- Guardrails and human approval
- Agentic AI SystemsSystems that carry out multi-step tasks, with clear limits and a person in charge.
- Task design with defined limits
- Tool and system integration
- Oversight and escalation paths
- Our platformStellar Core connects people, payroll and compliance.Explore Stellar Core
FAQ
Questions we hear.
Answers are drafted for review. Anything that needs a fact from Stellar is marked.
Have a business challenge worth solving?
Tell us what you're trying to fix. An engineer will reply, not a form robot.