Prompt engineering
Ongoing refinement of system prompts, tool instructions and response formats based on real usage and failure cases.
AI support
AI support
Tune the system after launch.
Design and engineering, under one roof
The order
What this service is for.
AI automation systems need active maintenance after launch. We support RAG workflows, assistants, prompts, model choices and evaluations so the system keeps improving as usage, data and model behaviour change.
AI products are not static. Models change, costs move, prompts degrade, new edge cases appear and users quickly reveal where the workflow needs sharper context or better guardrails.
Our AI automation retainers give businesses a technical partner for ongoing tuning, prompt engineering, retrieval quality, monitoring, model updates and practical improvements that keep AI systems useful in production.
What we can make happen.
Practical capability, scoped around the outcome rather than a fixed package.
RAG tuning
Chunking, retrieval, ranking, metadata, source grounding and answer quality improvements for knowledge systems.
Model updates
Evaluation and migration support as newer, faster or more cost-effective models become appropriate.
Quality evaluation
Test sets, review workflows and scoring criteria to measure whether the AI is actually improving.
Cost monitoring
Usage, latency and model spend reviewed so automation remains commercially sensible as volume grows.
Workflow iteration
Practical changes to automations, handoffs and fallback paths as teams discover how they want to work with AI.
Included in the order
What you get
- Prompt and workflow tuning
- RAG quality improvements
- Model evaluation and migration support
- Monitoring of cost and latency
- Failure-case review
- Improvement roadmap
From brief to open sign
How the work moves.
Baseline
We review the current AI workflow, data sources, prompts, model choices, user journeys and known failure cases.
Measure
We define the quality signals that matter: answer accuracy, source grounding, completion rate, latency, cost and escalation rate.
Tune
We improve prompts, retrieval, context, tools and model settings in controlled iterations.
Evolve
We keep the system aligned with new models, new business data and the way users actually behave.
Good questions
Before we get started.
Why does an AI automation need maintenance?
Because prompts, retrieval quality, model behaviour, user needs and costs all change. Production AI needs monitoring and tuning like any other business-critical system.
Can you support an AI system you did not build?
Often, yes. We start by reviewing the architecture, data sources, prompts and evaluation approach before taking responsibility for improvements.
Do you work with RAG systems?
Yes. Retrieval quality, source grounding and answer evaluation are central parts of the support we provide.
Can you reduce AI running costs?
In many cases, yes. We review model selection, prompt length, retrieval context, caching and workflow design to reduce unnecessary spend.
Ready when you are
Ready to talk ai automation support & maintenance retainer?
Bring us the rough brief, the awkward problem or the half-formed idea. We will help make the next move clear.