Software engineering service
A long-term engineering partner for the AI systems you depend on.
AI systems need care after launch — models change, data drifts, processes evolve. We provide continuous engineering to monitor, improve, and extend your AI and software over time.
The problem
Launch is the beginning, not the end.
Model providers update their models, business rules change, new edge cases appear, and costs shift. Without ongoing attention, AI systems quietly degrade.
What we typically hear
- Quality drifts after a model or data change.
- No one owns the AI system after the original team moves on.
- Costs creep up without visibility.
- New requirements wait months for engineering time.
- Incidents are discovered by customers instead of monitoring.
What we build
What the partnership covers
Monitoring
Quality, latency, error, and cost tracking with alerts.
Evaluation
Scheduled evaluation runs to catch regressions early.
Model updates
Testing and migrating to new model versions when they are better or required.
Improvements
Prompt, retrieval, and rule changes based on observed failures.
New capabilities
Extending agents and applications as the business needs change.
Incident response
Agreed response process when something goes wrong.
Typical use cases
Where this work pays off
Post-launch care
01Keep a newly launched system healthy and improving.
Inherited systems
02Take over an AI system built by another team.
Scaling usage
03Optimise performance and cost as volume grows.
Capability expansion
04Add new AI employees, workflows, or features over time.
Architecture
Continuous improvement cycle
- 1
Monitor
Track quality, cost, and reliability.
- 2
Detect
Find failures and drift.
- 3
Improve
Change prompts, rules, or code.
- 4
Evaluate
Test against the regression suite.
- 5Human
Review
Agree changes with your owner.
Technology considerations
Decisions we make with you
Technology choices follow from the workflow, data, and constraints — not the other way around.
Observability
Tracing and dashboards for AI calls, tool use, and outcomes.
Regression suites
Growing test sets built from real production cases.
Cost optimisation
Caching, routing, and model selection reviewed regularly.
Documentation
Runbooks and architecture notes kept current.
Delivery
How the engagement runs
01
Onboarding
Review architecture, access, and current health.
02
Baseline
Set up monitoring and evaluation if missing.
03
Monthly cycle
Planned improvements and reporting.
04
Quarterly review
Roadmap and priorities with stakeholders.
05
Continuity
Documentation that keeps knowledge transferable.
Security & governance
Control is designed in, not added later.
Every system we deliver includes the controls below as a baseline. Specific compliance requirements are scoped with your team.
- Change management with review and rollback
- Access reviews for engineers and service accounts
- Incident logging and post-incident reviews
- Regular reporting on quality, cost, and reliability
- Data handling consistent with your policies
FAQ
Frequently asked questions
01Can you support systems you did not build?
Yes. We start with an onboarding review to understand the architecture and risks, then propose a plan for monitoring and improvement.
02How is ongoing work structured?
Typically as a monthly engagement with an agreed capacity, priorities set with you, and regular reporting. Details are agreed per client.
03What do you report on?
Quality measures, incidents, costs, changes made, and recommended next steps — in plain language for business owners.
Related reading
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Next step
Bring us the process your team is tired of doing manually.
A strategy call is a working conversation about one workflow: what it involves today, what could be automated, and what a first build would look like.
What happens next
- 1Tell us about one workflow
- 2We review it before replying
- 3A working session, then a first-build proposal