APPLIED AI · IN PRODUCTION
AI that survives contact with production.
A demo that works on five examples is not a system. We build the parts that are missing between the two: evaluation, guardrails, fallbacks, observability, and the integration into the workflow where the value actually lands.
- Evaluated
- Against a held-out set you can inspect
- Observable
- Every call traced, logged and costed
- Bounded
- Explicit fallbacks when the model is wrong
THE SHORT VERSION
The gap between demo and system
Most AI projects stall in the same place. The prototype is convincing, then the questions start: how do we know it is right, what happens when it is not, what does it cost per request, who is accountable for the output, and how do we change it without breaking what already worked.
Those questions are engineering problems with known answers. We treat the model as one component inside a system that has tests, monitoring and a defined failure mode — the same as any other dependency.
The difference is where the effort goes.
The stalled pilot
- "It works" measured by vibes on a handful of examples
- No visibility into cost, latency or failure rate
- Prompt changes ship blind and regress silently
- No answer for what happens when the model is confidently wrong
A production system
- A labelled evaluation set and a score you can argue with
- Per-request tracing, cost attribution and latency budgets
- Changes gated by evaluation runs before they reach users
- Explicit confidence thresholds, human review and graceful degradation
WHAT IT INCLUDES
Scope, in plain terms.
Every engagement is shaped to the problem, but this is the ground it typically covers.
Document intelligence
Extraction, classification and validation over messy real-world documents.
Decision support
Systems that surface evidence and rank options for a human to decide.
Evaluation harnesses
The test suite for probabilistic systems: datasets, metrics and regression gates.
Model integration
Wiring a model into real workflows, queues, permissions and audit trails.
Cost & latency engineering
Routing, caching and model selection to make unit economics work.
AI readiness review
An honest assessment of whether AI is the right tool for the problem at hand.
What you get
- Problem framing with a measurable success criterion
- Evaluation dataset, metrics and baseline results
- Production service with tracing, cost tracking and rate limits
- Guardrails, fallback behaviour and human-in-the-loop paths
- Documentation of known failure modes and their handling
QUESTIONS
The things people ask first.
How do you decide whether AI is even appropriate?
By asking what the system does when it is wrong. If a wrong answer is cheap to catch and correct, AI is usually a good fit. If it is expensive and invisible, we look at deterministic approaches first.
Which models do you use?
Whichever fits the constraint. We build provider-agnostic interfaces so the model is a swappable dependency, not an architectural commitment.
What about our data?
Data handling is designed before the model is chosen — what leaves your environment, what is retained, and what the contractual position is with each provider.
Often paired with
- 04Generative AI & RAGLLM applications, retrieval systems and intelligent knowledge experiences.
- 06Machine LearningPredictive models, recommendation systems and intelligent optimization.
- 05AI Agents & AutomationAI agents and workflow automation that execute tasks, connect systems and reduce repetitive work.
Need ai development?
Tell us the problem and the constraint. We will tell you what it takes — and whether this is even the right service for it.