Solution 03 / Applied AI & Automation

Move AI from a convincing demo to a system you can defend.

Retrieval, agents and workflow automation built with the evaluation, guardrails and monitoring that a demo never needed and production cannot go without.

Discuss Applied AI ↗

01The problem

The gap between a working prototype and a production AI system is almost never model quality. It is that the prototype has no definition of correct. Without an eval set, nobody can say whether a prompt change helped; without retrieval measurement, "it hallucinates" is untreatable; without monitoring, quality degrades silently as the underlying data drifts. Teams stall here for months, iterating on prompts and hoping.

Typical symptoms

A prototype that impressed leadership and has not moved in three months
Prompt changes shipped on vibes, because there is no way to measure a regression
"It hallucinates sometimes", with no measurement of how often, or on what
Retrieval quality never evaluated separately from generation quality
No PII, injection, or policy handling between user input and the model
Nobody can say what the system costs per resolved task

Decisions we help you make

Which parts of the workflow genuinely need a model, and which are rules pretending to need one
Where a human stays in the loop, and what specifically triggers escalation
Retrieval design: chunking, hybrid search, reranking, and how much context is actually useful
What "correct" means for this task, expressed as a gradeable eval
What ships as an agent with tool access versus a constrained, deterministic pipeline
Which failures are acceptable and which must be structurally impossible

02How we work on it

Methods we apply.

Golden eval-set construction from real traffic, with disagreement-resolved human labels

Retrieval evaluation measured on its own: recall@k, MRR, and grounding coverage

LLM-as-judge scoring, calibrated against human review rather than trusted blindly

Regression gates in CI: no prompt, model, or retrieval change ships without passing evals

Guardrails: PII detection and redaction, prompt-injection defence, policy and output validation

Grounded generation with citations, so claims are traceable to a source

Full request tracing, drift and quality monitoring, with alerting on measured degradation

Without this loop, prompt engineering is guesswork: every change is an unverifiable claim. The eval set is what converts opinion about quality into evidence.

03What moves

Metrics this work is measured on.

Task success rate on the eval setGroundedness / citation accuracyRetrieval recall@kEscalation and human-intervention rateCost per resolved taskEnd-to-end latency

04What we need to start

The existing prototype, and an honest account of where it breaks
Real examples of the task, including the hard and ambiguous cases
The knowledge sources it must ground against, and their update cadence
Any policy, privacy, or regulatory constraints on outputs
Who the users are and what they do today when the system is wrong

05Engagement path

How this becomes an engagement.

Readiness Review

1–2 weeks

Assess the prototype against production requirements and define what "good enough to ship" measurably means.

Productionisation

6–12 weeks

Build the eval harness, retrieval, guardrails and monitoring, then harden the system against them.

Operate & Improve

Ongoing

Ongoing eval expansion and quality review as real usage exposes cases the original set missed.

Want to see what this looks like against your own systems?

Most engagements start with a short, fixed-scope assessment, enough to quantify the opportunity before anyone commits to a build.

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