A use case with evidence behind it
A defined user, workflow, value hypothesis, risk boundary, and representative examples that show whether AI improves the job enough to justify production.
Design · Evaluate · Integrate
Applied AI built around a real workflow.
Choose a workflow where intelligence matters, establish what good looks like, then build the product, evaluation, and controls required to run it responsibly.
Best for
Product teams with a specific high-friction workflow and access to representative examples, domain reviewers, and the systems the new capability must work with.
A convincing demo is not evidence that an AI feature will survive production. Real inputs are uneven, source material changes, permissions matter, and a plausible answer can still be wrong in an expensive way. We begin with the decision or task being improved, the acceptable error, and the evidence needed to compare the new workflow with the current one.
The model is then treated as one component inside a product system. Context, retrieval, tools, permissions, user experience, evaluation, human review, monitoring, and fallback behaviour are designed together. This makes quality observable and gives the team somewhere concrete to intervene when usage or model behaviour changes.
Quality the team can measure
A versioned evaluation set, clear scoring criteria, human review guidance, and baseline results across realistic inputs and failure cases.
A complete production workflow
AI connected to the right data, tools, permissions, interfaces, and operating systems—not isolated in a chat box or prototype.
Control when the model is uncertain
Fallbacks, review queues, audit evidence, monitoring, cost limits, and release controls that match the consequence of an error.
What we cover
The work behind a result your team can own.
- 01
Workflow and risk design
Task decomposition, value and error analysis, automation boundaries, human responsibility, fallback paths, and adoption constraints.
- 02
Model and approach evaluation
Representative datasets, vendor and model comparison, structured scoring, regression tests, adversarial cases, latency, and cost analysis.
- 03
Context and retrieval
Document ingestion, chunking, metadata, hybrid search, reranking, citations, freshness, permissions, and retrieval-quality measurement.
- 04
Tool-using workflows
Structured outputs, function calls, multi-step orchestration, state, retries, approval boundaries, and safe interaction with existing systems.
- 05
Product integration
Interfaces for guidance and review, feedback capture, identity, access, audit trails, APIs, queues, and integration with operational software.
- 06
Production operation
Tracing, prompt and model versioning, quality monitoring, exception review, budgets, rate controls, incident paths, and controlled rollout.
Frame
We define the user, current workflow, decision or output, economic value, consequence of error, human responsibility, and the conditions under which AI should not act.
Evaluate
We assemble representative examples, agree scoring with domain reviewers, and compare the simplest viable approaches before committing to a model or architecture.
Integrate
We connect data, retrieval, tools, permissions, UI, feedback, and review paths, then test the complete workflow rather than model responses in isolation.
Operate
We release behind controls, trace real usage, monitor quality, latency and cost, review exceptions, and rerun evaluations whenever the system changes.
How we make decisions
Practical rules for keeping the work honest.
Evaluations come before optimisation
Without representative examples and agreed scoring, prompt changes and model swaps are opinions rather than product decisions.
The workflow matters more than the model
Context, tools, permissions, UX, and review often determine usefulness more than a marginal benchmark improvement.
Uncertainty must be visible
The product needs an honest way to abstain, ask for review, show evidence, or fall back when the consequence of guessing is too high.
The engagement
Enough structure to move quickly. Close enough to make decisions.
Typical shape
2–4 week validation followed by an 8–16 week production build
Core team
Product/AI lead · 2–3 engineers · Product designer as needed
Working rhythm
Weekly evaluation review using representative cases
From your side
A domain reviewer, real examples, and access to the systems and data involved
Not a fit for
An AI demo without a specific workflow or owner, automation that hides an accountable human decision, or a project where representative data and domain review will not be available.
Need help with applied ai built around a real workflow.
Share the context, constraints, and where you are stuck. We’ll reply with useful questions and a clear next step.
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