Keep useful AI working well
AI reliability & managed support
Trace errors, review effort and spending to the work being completed, then test improvements against a consistent quality standard.
When AI is live, but the work still needs help
For technology, product and finance owners responsible for AI systems already used by staff or customers.
True North AI evaluates AI systems already in use and tests changes to retrieval, instructions, model selection and workflow logic. We compare quality, latency and full task cost against a representative evaluation set. Monitoring, incident ownership and recovery steps help the team keep improvements working after release.
- Answers vary in quality, but the team has no shared test for acceptable performance.
- Retries and manual corrections make a low-cost AI call expensive to finish.
- A prompt, model or source change improves one task and quietly breaks another.
A scope you can inspect
Agree what gets delivered, what good work looks like, and who takes ownership. Open each part to explore the detail.
A failure and cost baseline
See which issues consume time and money.
A failure and cost baseline
A review connects observed issues to affected tasks, system behaviour and human work, making the main causes easier to prioritize.
A question we resolve together
Which failures affect the business, and what do they cost to resolve?
A repeatable evaluation set
Compare changes against the same standard.
A repeatable evaluation set
Representative examples and clear scoring criteria allow the team to compare changes, including difficult cases and known failure patterns.
A question we resolve together
Do the test examples represent ordinary work and known difficult cases?
Tested improvements
Improve the whole result, including rework.
Tested improvements
Scoped changes are compared against the baseline for quality, response time and accepted-outcome cost before a decision to release them.
A question we resolve together
Does the change improve accepted work after retries and review are included?
An operating review process
Know who acts when performance changes.
An operating review process
Monitoring requirements, issue ownership and recovery guidance help staff identify degradation and decide when to investigate, revert or escalate.
A question we resolve together
Who investigates an issue, and when should the team revert or escalate?
Improve the result Account for the full cost
Model spend is only part of the picture. We test changes against the same quality standard and include retries, manual corrections and recovery effort in the comparison.
Our approach to measurementEvidence for the decision
- Acceptance rate on representative tasks
- Apply consistent criteria to ordinary tasks and known failure cases.
- Cost per accepted outcome including rework
- Include retries, manual corrections and review alongside model and infrastructure costs.
- Response time and retry frequency
- Measure the complete task, including repeated attempts and waiting.
- Incidents and time to restore the workflow
- Record disruption, ownership and the effort needed to return the workflow to service.
Agree the starting point, review period and accountable owner before comparing results.
From the first conversation to useful work
Bring the workflow, its owner and examples of the issues you see. Available records, access and business priorities will determine the review and support scope.
Inspect the live workflow
Review the architecture and available records with the owner. Identify missing visibility and handle diagnostic information within agreed data boundaries.
Define acceptable performance
Turn business requirements into evaluation criteria. Record which errors need human intervention and which changes require additional review.
Compare targeted changes
Test one set of changes against the same examples. Include retries, review and fallback work when comparing cost and response time.
Release with a way back
Agree rollout checks and recovery steps, then review live outcomes for unexpected failures or shifts in the work being requested.
Before we begin
Related serviceAI strategy and ROIWill a cheaper model reduce our total cost?
It might, but the comparison must include quality, retries and human corrections. We test whether the candidate completes the required work at a lower overall cost.
Can you evaluate a system another team built?
Yes, subject to access and scope. We first establish what documentation, examples and system records are available and what changes your team is authorized to make.
Can reliability be guaranteed?
No evaluation removes every possible failure. We define measurable acceptance criteria, test known risks and agree how the team will handle failures that appear in use.
Get more from the AI you already use
Discuss the quality, reliability or cost issues holding the workflow back. We’ll help you identify what to investigate and improve first.
Let’s talk about your business