Core capability
Put AI where it makes the product better.
We help teams identify valuable AI use cases, prepare the data and workflow foundation, integrate models into real products, and create the guardrails that make intelligent systems useful and trustworthy.
What this covers
- Opportunity and use-case selectionFind the applications of AI that survive contact with real users, real data and real consequences.
- Data and retrieval foundationsIntelligent features are only as trustworthy as the information behind them.
- Product and workflow designDesign the experience around the model, including what happens when it is wrong.
- Evaluation and governanceMake quality measurable, and keep it measurable after launch.
01The problem
What we solve
Many AI initiatives stop at the prototype. The demo works, the room is impressed, and then the work stalls — because the use case was never tied to a decision anyone makes, the data was not ready, the workflow around the model was never designed, or nobody agreed how quality would be measured.
The gap is rarely the model. It is the product design, the data foundation, the evaluation method and the accountability structure around it.
Inorbit connects AI to the product and to the people who use it, so an intelligent capability becomes something a team can depend on and improve.
02What we do
How the work is put together.
Opportunity and use-case selection
Find the applications of AI that survive contact with real users, real data and real consequences.
- AI opportunity mapping across workflows
- Use-case prioritisation against value and risk
- Feasibility and data-readiness assessment
- Success measures defined before the build
Data and retrieval foundations
Intelligent features are only as trustworthy as the information behind them.
- Retrieval-augmented generation architecture
- Document and knowledge-base pipelines
- Permission-aware retrieval and filtering
- Content quality, freshness and provenance
Product and workflow design
Design the experience around the model, including what happens when it is wrong.
- Copilots, assistants and intelligent search
- Agentic workflow design with defined boundaries
- Human review and escalation paths
- Uncertainty, citation and explanation patterns
Evaluation and governance
Make quality measurable, and keep it measurable after launch.
- Evaluation sets and regression testing
- Hallucination and failure-mode analysis
- Auditability, logging and traceability
- Post-launch monitoring and drift detection
03Outcomes
What changes when this works.
- AI capability tied to a decision or workflow that matters
- A data foundation that supports trustworthy answers
- Quality you can measure rather than assert
- Clear human oversight where consequences are high
- Permissions and auditability that hold up to scrutiny
- A path from prototype to production that does not stall
The Inorbit Product Loop
Progress without losing the plot.
Align
We clarify users, outcomes, constraints, risks, and the decisions that matter most — so the team is solving the same problem before anyone writes code.
What you get
- Problem and opportunity framing
- User and workflow map
- Success measures
- Risk and constraint register
Architect
We shape the product, platform, data, integration, security, and cloud foundations, and record the trade-offs behind each decision.
What you get
- Architecture position and rationale
- Tenancy, identity and data model
- Integration and API approach
- Delivery plan and first release scope
Build
We work in focused increments with design, engineering, quality, and product thinking together rather than in sequence.
What you get
- Working software in production
- Design system and accessible UI
- Automated test and release pipeline
- Observability from the first release
Accelerate
We improve adoption, performance, intelligence, release confidence, and operating leverage using what the product has actually taught us.
What you get
- Adoption and behaviour insight
- Performance and reliability improvement
- Prioritised next-phase roadmap
- Ongoing engineering capacity
Where we apply it
Domain context changes these decisions.
Questions
Straight answers.
If your question is not here, ask it directly — we would rather have the conversation than write a longer page.
We look for work that is high-volume, language- or document-heavy, currently slow, and where a knowledgeable person can quickly tell whether an output is good. Those four properties predict success better than enthusiasm does. We then check data readiness and the cost of being wrong before committing.
Yes, and for most useful product features it has to. The usual approach is retrieval — the model is given relevant, permission-filtered content at request time rather than being trained on your data. That keeps answers current, keeps access control intact, and makes it possible to cite sources.
We build an evaluation set from real examples with known-good answers, and score changes against it the way you would run a regression suite. That converts "it feels better" into evidence. For high-stakes outputs we add grounding requirements, citation checks and explicit abstention behaviour so the system can say it does not know.
By designing the workflow, not just the model. We decide which actions are suggested versus executed, what a reviewer sees in order to judge quickly, what gets logged, and what happens on rejection. In regulated or high-consequence work, the system proposes and a person decides.
Treat them as living systems. We instrument acceptance and override rates, latency and cost, retrieval quality, and the specific failure modes identified during evaluation — then review them on a regular cadence. Model providers change, source content drifts, and user behaviour shifts; monitoring is what turns those surprises into ordinary maintenance.
Keep exploring
Related thinking, and the capabilities next to this one
- AI in practicePerspective
AI integration is a product decision, not a feature request
Most AI initiatives do not fail on the model. They fail because nobody decided which decision the feature was supposed to improve.
7 min read
- AI in practiceChecklist
From prototype to production: an AI evaluation checklist
How to replace "it feels better" with evidence — evaluation sets, regression testing, failure-mode analysis and post-launch monitoring.
8 min read
- Product design and qualityFramework
Release confidence: a practical quality-engineering model
Teams rarely lack tests. They lack the ability to say, at any moment, whether the current build is safe to ship.
8 min read
Make the next release a step forward.
Tell us where ai integration & intelligent automation would make the biggest difference to your product. We will help you find the clearest next step.