Formats
- The same formats your training and evaluation pipelines already read
- Scorecard as a dashboard or PDF
What your model gets wrong this month is next month's training data.
Launch is when the most useful data starts arriving. A standing Benner House team samples what your model sees in production, labels where it failed, keeps your evaluation sets current, and ships a reviewed retraining set every month, so the system improves on the work it is actually doing.
Models stand still. The work around them doesn't.
A model is frozen the day it ships, and the world it serves keeps moving. Policy forms get revised. A payer changes its criteria. A new product line launches. After a hurricane, claim volume and claim types change overnight. Users phrase questions differently than the pilot group did. Each change opens a gap between what the model learned and what it now sees.
Gartner's guidance on AI-ready data makes the same point from the data side: it is not “one and done”, but a practice of testing and monitoring data after models are trained. Gartner predicts that through 2026 organizations will abandon 60% of AI projects unsupported by AI-ready data.
Most teams know they should review production failures. In practice the job falls to whoever has spare time, which is nobody. Managed data operations makes it someone's job, on a schedule, with the same quality controls as the original build.
One turn of the flywheel
Sample production, weekly
A stratified sample of inputs and outputs, oversampling low-confidence answers, user-flagged cases, escalations, and new categories. Identifiers are removed inside your environment before review where your policy requires it.
Review and tag failures
Reviewers score sampled outputs against your rubric and tag each failure by type: retrieval miss, wrong reasoning, policy violation, format error, or out of scope.
Find the pattern
We cluster failures and report the top failure modes with examples and an estimate of how often each occurs in production, so fixes go where the volume is.
Refresh the evaluation sets
New failure cases join a rotating slice of the evaluation set and stale ones retire. A frozen core stays fixed, so month-over-month trends remain comparable.
Ship the retraining set, monthly
Corrected outputs, preference pairs, and new demonstrations, decontaminated against the evaluation set and versioned with a manifest, ready for your training pipeline.
What arrives every month
| Deliverable | Contents | Who uses it |
|---|---|---|
| Failure report | Top failure modes with counts, examples, and suspected causes | Product owner and engineering |
| Labeled sample | Reviewed production cases with rubric scores and failure tags | ML team and model risk |
| Evaluation refresh | New cases added and stale ones retired; frozen core unchanged | The release gate in CI |
| Retraining set | Corrected outputs, preference pairs, and demonstrations, with a manifest | Your training pipeline |
| Scorecard | Trends on the frozen core and the rotating slice, with before-and-after for the latest retrain | Leadership and risk committees |
Ad hoc upkeep versus a managed flywheel
Ad hoc
- Failures collected from complaints and anecdotes
- The evaluation set from launch day, never updated
- Retraining when someone finds the time
- No record of what changed between model versions
Managed
- A stratified weekly sample, including failures nobody reported
- Evaluation refreshed monthly, with a frozen core for trend lines
- A reviewed retraining set on a fixed schedule
- Every dataset version tied to a model version in the manifest
The retainer at a glance
You provide
- A sampled feed or log access covering production inputs, outputs, and outcomes
- Your existing evaluation set and rubric, or a first month to build them
- A 45-minute monthly review with your product owner
We deliver
- Weekly reviewed samples with failure tags
- A monthly evaluation refresh and scorecard
- A monthly retraining set with its provenance manifest
- A quarterly review of the rubric, sampling plan, and priorities
Quality gates
- Samples stratified, with uncertain and flagged cases oversampled
- Reviewer agreement and gold-question accuracy tracked monthly
- Retraining sets decontaminated against the evaluation set
- Data handled under the same retention and deletion terms as project work
Turnaround
First reviewed sample within two weeks of kickoff, then a monthly cadence
Team
A named data operations lead, reviewers from your domain, and a part-time ML engineer
Starts at
$8,900per month
A standing team that samples production traffic, labels failures, refreshes evaluation sets, and ships retraining data every month.
A monthly retainer
Managed Data Operations
Starts at
$8,900per month
A standing team that samples production traffic, labels failures, refreshes evaluation sets, and ships retraining data every month.
Request a scoped quoteThe starting retainer covers one model or agent at a set monthly review volume. More models, higher volumes, or specialist reviewers such as physicians or attorneys are priced into the retainer. After an initial three-month term, it runs month to month.
Questions about managed data operations
Do we need an evaluation set before we start?
It helps, but it isn't required. If you don't have one, the first month builds it, using the same approach as our evaluation suite, and the flywheel starts from there.
Who actually retrains the model?
Usually your team or your model vendor, using the datasets we ship. If you'd like, we run the fine-tune in your environment as well and report the before-and-after on the frozen evaluation core.
How do you review production data we can't share outside the company?
We don't take it outside. Reviewers work inside your environment through a virtual desktop or your labeling tool, identifiers are removed where policy requires, and everyone on the team is an employed US person under NDA, with a BAA for health data.
How do we know it's working?
The scorecard. It tracks the frozen core month over month and shows the before-and-after for each retrain. If a retraining set doesn't move the numbers, we say so and change the approach rather than keep shipping data.
Can the team scale up for a launch or a bad month?
Yes. Surges are planned into the retainer: a product launch, an open-enrollment period, or the weeks after a Gulf storm when claim volume and claim types shift. We agree the surge capacity and its rate up front.
Keep reading
Bring us one messy data source and one question.
Tell us what the data is and what you want a model to do with it. A data engineer, not a salesperson, will reply within one business day with questions and a proposed first step.