One method, six kinds of paperwork.
A discharge summary, a claims note and a bill of lading fail in different ways. The gates we run are the same; the reviewers, the redaction rules, the regulators and the hard cases are not. Pick your sector to see what we expect to find in your systems and what it can become.
Sector knowledge matters more than model choice
The same base model can read a radiology report, a homeowners claim file and a switching order. What changes from one sector to the next is everything around the model: what counts as a correct answer, who is qualified to judge it, which identifiers must come out, and which regulator will ask how the system was built.
That is why our projects are staffed by sector. An annotation lead who has run claims projects knows that notes written during a hurricane surge read differently from calm-season notes. A reviewer who has worked a hospital floor catches the omitted anticoagulant a generalist would miss. A privacy reviewer who knows utilities knows that a substation diagram can be more sensitive than a customer list.
The method underneath stays constant: the Six Gates, an evaluation set built first, and a provenance manifest with every delivery.
Six sectors we know well
Each page lists the data we usually find, the use cases worth testing first, and the constraints that shape the work.
Healthcare & Life Sciences
Clinical notes, prior-auth letters and faxed referrals de-identified in place and turned into training data and clinician-graded evaluations for health AI.
Insurance & Financial Services
Claims notes, photos, underwriting files and recordings turned into training and evaluation data for carriers and lenders, with documentation for AI governance.
Legal & Professional Services
Contracts, redlines, briefs and matter files turned into retrieval indexes, training sets and lawyer-graded evaluations, with confidentiality and ethical walls intact.
Logistics & Manufacturing
Bills of lading, work orders, maintenance logs and inspection photos turned into extraction pipelines, agent environments and evaluations for operations AI.
Energy & Utilities
Outage tickets, operator notes, inspection photos and maintenance records prepared for AI by background-checked US staff, inside the utility's own environment.
AI Product Companies
Expert-written training data, evaluations and agent environments for startups and SaaS teams building vertical models, from a vendor with no lab or model-vendor ties.
What each sector already has
A quick map from the records you probably keep to the first use case we'd test with them.
| Sector | Records we usually find | First use case we'd test | Main constraint |
|---|---|---|---|
| Healthcare | Discharge summaries, progress notes, prior-auth letters, faxed referrals, portal messages | Drafting discharge summaries for physician review | HIPAA de-identification; 42 CFR Part 2 records |
| Insurance & financial services | Claims notes, adjuster photos, underwriting submissions, call recordings, loan files | Claims-file summaries with citations for desk adjusters | State insurance AI bulletins; GLBA safeguards |
| Legal | Contracts and redlines, briefs, research memos, discovery productions | Contract review against the firm's own playbook | Confidentiality, privilege and ethical walls |
| Logistics & manufacturing | Bills of lading, work orders, maintenance logs, inspection photos, OEM manuals | A work-order agent tested in a sandbox | Customer contracts; export controls for defense suppliers |
| Energy & utilities | Outage tickets, SCADA operator notes, inspection photos, switching orders | Outage cause classification from crew remarks | NERC CIP access rules; CEII |
| AI product companies | Design-partner documents, product traces, user feedback, a founder-written eval set | An expert-written evaluation suite and release gate | Customer data terms; training-data disclosure laws |
Built in the Gulf South
Our team works from downtown Jackson, Mississippi, and the region shapes what we know best: health systems serving rural counties, property insurers that live through hurricane seasons, river and port logistics, energy, agriculture, and the law firms that serve all of them. We work with clients across the United States, and for those within driving distance we can work on-site. See AI data services in the Gulf South.
Questions about sector work
We don't see our industry here. Can you still help?
Probably. The method doesn't depend on the sector; the reviewers and the rules do. We also work with agriculture, public-sector and education clients. A readiness assessment is the usual starting point when a sector is new to us, because it surfaces the rules and the hard cases early.
Do your reviewers actually come from these industries?
Yes. Domain judgment comes from practitioners in our expert network: nurses and physicians, licensed adjusters and underwriters, attorneys and paralegals, mechanics and marine engineers, lineworkers and inspectors. They work under the same access controls and confidentiality terms as our employees.
Can you work with regulated data?
That's most of our work. We default to working inside your environment, sign business associate agreements for health data, and follow your personnel screening process where your rules require it. Benner House is not a law firm and does not provide legal advice; we document the evidence your counsel and compliance team need to decide.
Our business spans two sectors. Which page applies?
Both, usually. A health insurer has clinical records and claims files; a utility's legal department has contracts as well as outage tickets. We staff each workstream with the reviewers that fit its records.
Where to go next
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.