Evaluation harness
A repeatable test built from real examples of your task, with a clear definition of what a correct result looks like.
AI and automation
New AI capability, tested against your workflow before it earns a place in production test that before you do
New models and techniques show up every few months. We run a structured evaluation against your actual workflow so adoption decisions are based on results, not release notes.
Every few months, a new model, technique, or capability shows up with claims attached to it, and a business is left deciding whether it is worth the disruption of adopting it. Most of the time the honest answer is no, not yet, or not for this task, but that answer only holds up if someone actually tested the claim against a real workload instead of a demo.
Applied AI innovation is the discipline of running that test before committing. It means building a small, cheap evaluation harness around a specific task you already do, trying the new capability against that harness, and looking at the actual numbers rather than the marketing. Only what clears that bar moves toward production.
This is different from research for its own sake. The point is not to stay current for the sake of appearing current. The point is that a narrow set of new capabilities will genuinely change what is possible in your operations, and finding those few requires filtering out a much larger set that will not, without wasting a production rebuild to find out which is which.
This page covers how we run that filter: the evaluation harnesses we build, the path from a working prototype to something running in production, how model selection factors into the decision, the guardrails that come with anything new, and how we measure whether it actually helped once it ships.
What it is
What an applied innovation engagement actually covers
Evaluate first, prototype second, ship only what earns it.
An evaluation harness is a small, repeatable test built around a task you already do, with real examples and a clear definition of a correct answer. Before we try a new capability on anything live, we run it against this harness so the comparison is against your actual data, not a benchmark someone else published. A capability that looks impressive on a general benchmark and mediocre on your harness gets set aside, and the reverse also happens more often than people expect.
The path from prototype to production is deliberately staged. A prototype that performs well in the harness gets built into a narrow, low-stakes version of the real workflow, run alongside the existing process rather than replacing it, and watched for a defined period before it takes over anything by itself. Nothing skips from a promising test straight into a system people depend on.
Model selection is part of this evaluation, not a separate decision made afterward. A new capability is often really a new model, or a new way of using an existing one, and the same harness that tests the capability also tells us whether a smaller or cheaper model gets the same result. Adopting a new technique should not mean quietly overpaying for a model that a lighter option handles just as well.
Guardrails travel with anything new by default, not as an afterthought once something goes wrong. That means scoped access to only the systems the new capability needs, a defined fallback to the previous process if it underperforms, and a human checkpoint until the track record justifies removing it. New capability earns loosened supervision, it does not start with it.
Measurement closes the loop. We define what success looks like before a prototype goes live, not after, and we check the real numbers against that definition on a set schedule rather than relying on impressions of whether something feels like it is working. A capability that does not show up in the numbers gets rolled back, regardless of how it looked in testing.
Fit
Who this is for, and who it is not for
We would rather say no early than sell a program that cannot work.
Right fit
- You have a specific workflow where a new AI capability might help, and want a real answer before committing engineering time to a rebuild.
- You already run one or more AI systems in production and want a standing process for evaluating what should replace or extend them.
- You want adoption decisions backed by a test against your own data, not a vendor's benchmark or a competitor's announcement.
Not the right fit
- You want to adopt a specific new model or tool regardless of what an evaluation shows. That defeats the purpose of testing it first.
- You have no workflow yet to test against. Start with a specific process, then bring in new capability once there is something real to measure it on.
- You are looking for a one-time trend report rather than a tested recommendation tied to a workflow you actually run.
Deliverables
What an innovation engagement includes
A harness, a tested prototype, and a clear go or no-go decision.
Capability scoring
The new capability run against the harness and scored on accuracy, cost, and speed against your current process.
Shadow-mode prototype
A narrow prototype run alongside the existing workflow without replacing it, so real performance is visible before anything depends on it.
Guardrails and fallback path
Scoped access, a defined rollback to the previous process, and human checkpoints sized to the track record so far.
Success metrics defined upfront
A clear, written definition of what success looks like, set before the prototype goes live rather than argued about afterward.
Go or no-go recommendation
A direct recommendation on whether the capability earns a place in production, backed by the harness results and shadow-mode data.
How we run it
How we run it
A staged path from test to production, with a rollback available at every stage.
Step 1: Build the harness
We pull real examples from your workflow and define what a correct result looks like, so the test is grounded in your task, not a generic benchmark.
Step 2: Score the new capability
We run the candidate capability against the harness and compare it to your current process on accuracy, cost, and speed.
Step 3: Prototype in shadow mode
Anything that clears the bar runs alongside the live process without replacing it, so we can watch real performance before anyone depends on it.
Step 4: Ship, adjust, or roll back
Based on the shadow-mode results against the metrics defined upfront, the capability either moves into production with guardrails, gets adjusted and retested, or is set aside.
What is applied AI innovation?
The practice of testing new AI capability against a real workflow, using an evaluation harness built from your own data, before deciding whether it is worth adopting into production.
How do you decide if a new AI capability is worth adopting?
We score it against a harness built from your actual task, run it in shadow mode alongside the current process, and check the results against metrics defined before the test started.
Where this connects
Where this fits with the rest of our AI work
Innovation work feeds directly into the systems we already build and maintain.
A capability that clears evaluation for judgment-heavy, multi-step tasks usually becomes part of agentic workflows once it moves out of shadow mode.
Model comparisons run during evaluation are the same work covered under frontier model services so the two are usually scoped together when a new model is the thing being tested.
When the winning approach turns out to be a fixed, known rule set rather than something that needs ongoing judgment, it belongs in automation workflows which is simpler to run long term.
For capability that needs to run with less human involvement per step once it has a track record, that shifts into autonomous agents with its own guardrail design.
The dashboards operators use to watch a shadow-mode prototype and review the evaluation results are usually built as custom application frontends since raw logs are not something a business team can act on directly.
Questions
Applied AI innovation questions we get asked
Test new AI capability before you bet a rebuild on it
We will build the harness, run the test, and give you a straight go or no-go.