AI & software systems
AI & Software Systems
We build AI that earns its place in the process. That means a clear definition of the decision being improved, a retrieval and prompting strategy grounded in your own data, and an evaluation harness that tells you whether the system is actually working when a prompt changes under it.
What we are usually hired for
The problems behind the brief.
A demo that does not survive production
The prototype answered beautifully on curated examples. In production it meets ambiguous inputs, missing context, adversarial data, and a latency budget. We build the evaluation harness first, so quality is a measured property rather than an impression.
Model dependence treated as a feature
Behaviour changes when a provider ships an update. We design abstraction boundaries, prompt and retrieval versioning, and regression tests so upgrades are a controlled operation.
No trustworthy data foundation
Retrieval fails because the corpus is stale, unsegmented, and ungoverned. We fix ingestion, chunking, and freshness before tuning anything.
Automation nobody trusts
An agent that acts without a human checkpoint is a liability. We define the autonomy boundary explicitly: what the system decides, what it proposes, and what it must ask about.
Core capabilities
What we do, concretely.
How we approach it
Our method, applied here.
We ship the thinnest useful slice first and instrument it from day one. Every model, prompt, and retrieval change is measured against a fixed evaluation set before it reaches users.
Discover
constraints / users / success criteria
Design
architecture / plan / risk register
Build
integration / CI / review
Deploy
monitoring / runbooks / handover
Layered view of a ai & software systems engagement — interface, integration, infrastructure, operations.
Interface
- AI-powered applications and agents
- LLM integration and retrieval systems
- Backend and API development
Integration
- Retrieval architecture
- Model routing and fallback design
- Evaluation harness design
Infrastructure
- Prompt and context engineering
- Vector and structured retrieval
- Agent orchestration
Operations
- Monitoring
- Runbooks
- Handover
What you receive
Deliverables, not status updates.
A task definition and success criteria agreed before model selection
A production retrieval and orchestration pipeline over your own data
An evaluation suite with regression gates wired into CI
Tracing, cost accounting, and latency monitoring per request path
A documented fallback path with human-in-the-loop checkpoints
Infrastructure-as-code for reproducible environments
Disciplines
The skills we bring to the problem.
When this applies
Where this capability is the answer.
Automating high-volume operational review where errors are expensive
Giving internal teams trustworthy answers over an existing document corpus
Replacing brittle rules engines with a system that tolerates messy input
Hardening an AI feature that already shipped and is not performing
Other capabilities
The rest of the stack.
Currently viewing AI & software systems.
Ready to scope this properly?
Tell us what you are trying to build and where it is stuck. We will come back with a technical read on the problem and a realistic path through it.