Model Swap
A documented model swap, with the eval, the cost review and the deployment the AI needs.
AI Consulting & Governance
AI Optimization & SupportThe AI That Gets Better Every Month.
AI optimization and support for the AI that is already in production — model swap, fine-tuning, prompt tuning, eval-driven improvements, the cost review, the drift monitoring, the monthly review and the support the AI needs to get better every month.
10+Years Experience
100+Projects Delivered
50+Expert Developers
20+Industries Served
Model Swap
Fine-Tuning & Adapters
Prompt & Context Tuning
Cost Optimisation
Drift Monitoring
Monthly Review
Overview
AI optimization is the monthly review, not the one-off project
AI in production gets better every month — model swap, fine-tuning, prompt tuning, eval-driven improvements, the cost review, the drift monitoring. The work is the monthly review, with the next iteration scoped from the data. The work compounds, because the AI gets smarter with each month.
We do AI optimization as a monthly retainer, with the model swap, the fine-tuning, the prompt tuning, the eval, the cost review, the drift monitoring and the support as part of the engagement. The output is an AI that gets better every month, not a one-off project that ships and is forgotten.
This is the wrong engagement if the AI is not yet in production. The right answer there is an AI development engagement, with the optimization as the next step after launch.
- Gets Better Monthly — An AI that gets better every month, with the eval, the cost and the drift as the input.
- Cost-Defensible — Token budgets, caching, batching, the cost review that keeps the bill honest.
- Drift Caught Early — Data drift, model drift, the monitoring the AI needs to stay in production.
- Eval-Driven — Every change evaluated against the held-out test set, with the regressions caught early.
What we deliver
Everything included in our ai optimization & support
Fine-Tuning & Adapters
Fine-tuning or LoRA adapters against the data, with the eval that proves it worked.
Prompt & Context Tuning
Prompt tuning, context engineering, the eval-driven improvements the AI needs.
Cost Optimisation
Token budgets, caching, batching, model routing, the cost review the AI needs.
Drift Monitoring
Data drift, model drift, the monitoring the AI needs to stay in production.
Monthly Review
A monthly review, the eval, the cost, the next iteration, the cadence the AI needs.
Our process
A proven process for successful delivery
01
Discover
We audit the AI, the eval, the cost, the drift and the team feedback.
02
Plan & Design
We design the next iteration, the model swap, the fine-tune, the cost review.
03
Develop
We build the change behind a flag, with the eval and the rollback path tested.
04
Deploy
We cut over the change, watch the metrics and roll back if it drifts.
05
Optimize & Grow
We read the eval, the cost and the team feedback, and ship the next iteration.
Technology
Built with a stack that stays maintainable
Models
- OpenAI
- Anthropic
- Google Gemini
- Open source
Eval
- LangSmith
- Helicone
- Custom eval
Drift
- Evidently
- Whylabs
- Arize
Cost
- Token budgets
- Caching
- Batching
- Model routing
What you can expect
- Improvement Cadence
- MonthlyImprovement Cadence
- Driven Changes
- EvalDriven Changes
- Caught Early
- DriftCaught Early
- Defensible
- CostDefensible
Industries we serve
Delivered for teams in your sector
A monthly retainer, with a written scope — the model swap, the fine-tune, the prompt tuning, the cost review, the drift monitoring, the support. The scope is renewed quarterly, with the priorities updated as the AI landscape changes. The output is the monthly review and the next iteration.
The model swap is part of the architecture. When the next model is meaningfully better, we run the swap, with the eval as the gate. The swap is a configuration change, not a rewrite. The cost, the latency and the quality are part of the eval, and the result is in the deploy log.
Against the eval suite and the business metric. The eval suite is the technical metric the team can act on; the business metric is the metric the business cares about. The two are tied together in the monthly review, and the next iteration is scoped against the data.
The support is part of the retainer. The on-call rotation, the incident response, the runbook and the post-mortem are part of the engagement, with the SLA agreed in the scope. The support compounds, because the post-mortem feeds the next iteration.
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Ready to start your ai optimization & support project?Let’s scope it together.
Tell us the outcome you need. We’ll come back with an approach, a timeline and a written estimate.