Your model is only as specialized as the people behind its data.
If you're building a vertical model or an agent product, your advantage is the data no general-purpose lab has: expert judgments in your domain, evaluations your customers would recognize, and environments that look like the real work. We build those for startups and SaaS teams, with domain experts we pay directly and no ties to any lab or model vendor.
What vertical AI teams usually need from us
General models are good at the average task in every field. Your customers pay for the hard tasks in one field: the prior-auth appeal that cites the right policy clause, the claim note that separates wind from water, the maintenance diagnosis that names the right part. Getting a model there takes data written and judged by people who do that work for a living.
That is what we supply. Practitioners from our expert network write supervised examples, score outputs against rubrics, compare pairs, and record how they work through multi-step tasks. Our engineers turn that into versioned datasets, evaluation suites your sales team can show a skeptical buyer, and sandboxed environments for agent training.
We also handle the unglamorous part of building on customer data: getting design partners' documents parsed, de-identified and documented well enough that your contracts, and your customers' compliance teams, allow you to train on them.
What we build for AI product teams
Expert SFT and reasoning data
Practitioner-written examples with worked reasoning, built to a task specification, reviewed by a second expert, and delivered with a held-out set.
Preference and rubric data
Pairwise comparisons and rubric scores from credentialed reviewers, with agreement measured per field and hidden gold questions in every batch.
Customer-grade evaluation suites
Golden sets that look like your customers' work, a judge model calibrated against your experts, and a release gate in your CI pipeline.
Agent environments and trajectories
Vertical workflows rebuilt as sandboxes with tools, seeded data and automatic verifiers, plus reviewed expert trajectories to train and test against.
Design-partner data you can actually use
Parse, de-identify and document customer data inside the customer's tenant or yours, with measured redaction recall and a record of the permission behind each source.
Training-data disclosures
Datasheets, Croissant metadata and a lineage manifest per dataset, plus a draft public summary, so the disclosure question has an answer before a buyer asks it.
Neutral vendor or lab-aligned vendor
When a data vendor takes investment from a model developer, its other customers have to decide how comfortable they are. In June 2025, Scale AI announced a significant investment from Meta that valued Scale at over $29 billion, with Meta holding a minority stake; Scale says it remains independent. Whatever you conclude about any particular vendor, it's worth asking every vendor who owns it, who else it works for, and what happens to your task designs.
Questions to ask any data vendor
- Does any model developer hold equity, a board seat or a commercial agreement with you?
- Are our rubrics, task designs or prompts reused for other customers?
- Who exactly sees our data, where are they, and are they employees?
- Can we audit where our data went and confirm it was deleted?
Our answers
- No AI lab or model vendor holds equity in, partners with, or resells through Benner House
- Your specifications, rubrics and data are yours and are not reused for anyone else
- Named US-person employees and vetted domain experts, working in your environment where you need it
- Every delivery ships with a provenance manifest, and every project ends with a written deletion record
What you have, and what it becomes
| What you have | What it becomes | Where the work is |
|---|---|---|
| Design partners' documents | De-identified, parsed training and evaluation data with a permission record per source | Protect and Source: customer terms differ by partner |
| Production traces and tool calls | Labeled failure categories and replayable tasks for an agent sandbox | Clean: removing customer identifiers from logs |
| Thumbs-up and thumbs-down feedback | Expert-adjudicated preference pairs; raw clicks are a weak label on their own | Label: experts check what users actually meant |
| A founder-written eval set | A golden set of a few hundred cases with rubrics and a calibrated judge | Prove: coverage of the hard cases customers hit |
| Support tickets about wrong answers | Red-team cases and regression tests for every release | Label: grouping tickets by root cause |
| Public and licensed corpora | Deduplicated, decontaminated pretraining or retrieval data with license records | Clean: removing overlap with your test sets |
Training-data disclosure is now part of the product
California's AB 2013 requires developers of generative AI systems released on or after January 1, 2022 and offered to Californians to post documentation about their training data, on or before January 1, 2026 and again with each substantial modification. The required items include a high-level summary of datasets: their sources, the number of data points, and whether they contain personal information, copyrighted material or synthetic data. Enterprise buyers often ask the same questions during procurement.
Our provenance manifest records those facts as the data is built, so a disclosure becomes an export rather than an archaeology project. Benner House is not a law firm and does not provide legal advice; your counsel decides what your disclosure says.
Questions from AI product teams
Can you move at startup speed?
Pilots are scoped to run in two to four weeks with weekly deliveries, so you see the first reviewed batch and its agreement numbers early. Starting prices for every pilot are on the pricing page.
Who owns the data and the task designs?
You do. Specifications, rubrics, examples, environments and evaluation sets are delivered to you and not reused for any other customer. We keep no copy after the project closes.
Can you find experts in a narrow specialty?
Usually. Our network covers clinical, insurance, legal, financial, maintenance and energy practitioners, and we recruit for narrower specialties when a project needs them. Recruiting time is part of the pilot plan, not a surprise.
Will you work in our labeling tool and our cloud?
Yes. We're tool-neutral and recommend no platform by default. We can work in your annotation tool, set up an open-source one in your cloud, or deliver in the formats your training stack expects.
Do you have relationships with model providers?
No. We have no investors, partnerships or resale agreements with any AI lab or model vendor, which is why we can evaluate your model against theirs without an interest in the outcome.
Related services and 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.