A use case and evaluation baseline
A defined task, representative examples, and criteria for useful output. We consider accuracy, review effort, latency, and cost together.
Applied AI
Give AI a useful job. And a clear standard.
Evaluate AI against a real task, with a quality baseline, data boundaries, and human oversight.
Explore an AI use caseStart with one bottleneck. We’ll arrange a conversation by email.
Clarity in the thinking.Discipline in the build.Conceptual
system study
A useful starting point when
There is a real task behind the interest in AI.
Your team repeatedly reads long documents to extract the same types of information.
Useful internal knowledge exists, but finding the right source takes too much effort.
People already use AI informally, without agreed data boundaries or a way to evaluate its output.
The work, made tangible
A concrete scope.
A useful result.
Applied AI starts with a specific piece of work: finding information, extracting details from documents, or preparing a draft for someone to review. We assess whether AI can do that job usefully, design it into the workflow, and make its limits visible before expanding its role.
A bounded working system
An AI-assisted workflow or tool connected to appropriate sources. Data access, provider choices, human review, and fallback behavior are part of the design.
A decision about what comes next
An evaluation against the agreed criteria, a record of failure modes, and a recommendation to expand, revise, or stop. A demonstration alone is not evidence that a system is ready for everyday use.
The exact deliverables and acceptance criteria are agreed in the scope.
An illustrative workflow
Preparing an intake brief from documents
An example of the kind of problem we can examine. The scope and outcome depend on your business.
Where the friction starts
A team member reads each incoming document, extracts the key facts, and writes a summary before the next person can begin.
What a better workflow could look like
An AI-assisted step proposes a structured brief with links back to its sources. A person checks the details and handles uncertain or missing information before the brief enters the next workflow.
What to measure
Time including review and corrections; accuracy on representative examples; frequency and severity of missing or unsupported details.
A deliberate sequence
From the first question to the next useful step.
The same team that diagnoses the business does the build. Each stage should make the next decision clearer.
Talk through the first stepChoose the task and boundaries
Define what useful output looks like, which information can be used, and who is responsible for reviewing it.
Evaluate on representative work
Test the approach against realistic examples and awkward cases. Compare the result with the current workflow, including the effort of checking it.
Integrate with oversight
Connect the useful parts to the operating process. Define fallback behavior, ownership, and the checks needed as models and inputs change.
A few practical questions
Before
we talk.
A clear understanding of the work makes for a better conversation.
How do we know if AI is the right approach?
Start with the task and compare possible approaches. If fixed rules or an existing tool solve it reliably, that may be the better choice. AI deserves a role only when its useful output and review cost support the case.
Will our business data be sent to an AI provider?
That depends on the proposed system. Data sources, permissions, provider terms, and deployment choices must be reviewed and agreed before business information is connected. There is no blanket assumption that all data is appropriate to share.
Can AI work with our existing knowledge?
It can be designed to retrieve from approved sources and show supporting material. That still requires attention to document quality, access permissions, missing information, and whether an answer is supported by its source.
What happens if the evaluation is not good enough?
We make the shortcomings visible and decide whether to change the scope, improve the inputs, use a different approach, or stop. A failed evaluation is useful if it prevents an unreliable workflow from becoming operational.
A conversation, to begin
Start with the part that needs to work better.
Tell us what happens today, who it affects, and what keeps getting in the way. You don’t need a finished brief.
Explore an AI use caseSend a note. We’ll reply to arrange a time.