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AI training data · Jackson, Mississippi

The data your business already has is the model you haven't trained yet.

Benner House turns claims notes, work orders, contracts, call recordings and decades of scanned paper into training-grade datasets, evaluation sets and pipelines. Our onshore team works inside your cloud, and every delivery is measured against a test your own experts trust.

Score your data readiness Talk to an engineer

Watch a record become training data

claims/2025-q3/batch-114/note-0388.pdf · scanned, 1 page

07/14 Spoke w/ insured (pol. ) re: roof at , Pass Christian. Wind-driven rain, shingles lifted on S slope, approx 14 sq. Ceiling stain bdrm 2. Policy HO-3, wind deductible 2%. 6 photos rec'd. Roof age 17 yrs, ACV vs RCV on shingles likely. Rec field inspection.

Fine-tuning pair · JSON

{
  "task": "claims_next_step",
  "input": "Wind-driven rain; shingles lifted, south slope,
            ~14 squares; interior stain, bedroom 2; HO-3;
            2% wind deductible; roof age 17 years.",
  "output": "Schedule field inspection. Flag ACV vs. RCV
             on roof shingles. Apply 2% wind deductible.",
  "labels": { "peril": "wind", "severity": "moderate" },
  "pii_removed": 3,
  "reviewer": "adjuster-07"
}
  • Parsed · OCR confidence 0.97
  • 3 identifiers redacted
  • Near-duplicates checked
  • Adjuster-reviewed
  • Lineage recorded

Three real-world shapes of business data, and what each becomes after it passes through the Six Gates. Every name, number and address is invented for the demonstration.

  • OnshoreEmployed US-person team. No anonymous crowd.
  • In your cloudRaw records stay in your tenant or on your premises.
  • Lab-neutralNo ties to any model vendor or AI lab.
  • Eval-firstEvery dataset ships with a measured before and after.
  • TraceableA provenance manifest with every delivery.
Aerial view of a braided river delta where loose floating papers are guided by wooden weirs into straight canals that feed one orange lock gate.

Raw archives in. Training-grade data out.

Most organizations are sitting on a delta of records: scanned, duplicated, sensitive, scattered across systems nobody owns. Our work is the weirs and the lock.

A robot on a library ladder holds the last book from empty public shelves while private archive stacks stay full behind a padlocked iron gate.

The public web has been read. Your archive hasn't.

The general-purpose models your teams use were trained on what anyone could download. What they have never seen is how your business actually works: the adjuster's reasoning on a disputed roof, the nurse's handoff note, the dispatcher's fix for a stuck barge. That knowledge sits in your systems, mostly unstructured, partly sensitive, and largely unlabeled.

This is why the next round of useful AI is smaller and more specific. Gartner predicts that by 2027 organizations will use small, task-specific models 3× more than general-purpose LLMs, and it names data preparation as the work that makes that possible. The bottleneck isn't the model. It's turning your records into something a model can learn from without leaking what it shouldn't.

How a readiness assessment finds the usable data

Why the data comes first

Figures from Gartner's research on AI-ready data and small models. Sources are linked on each card.

60%

Of AI projects unsupported by AI-ready data will be abandoned through 2026, Gartner predicts.

Gartner press release, February 26, 2025

63%

Of organizations either do not have or are unsure if they have the right data management practices for AI.

Gartner press release, February 26, 2025

3×

Gartner predicts that by 2027 organizations will use small, task-specific AI models at least three times more than general-purpose LLMs.

Gartner press release, April 9, 2025

Four disciplines, twelve services, one channel

Every engagement moves data through the same channel. Start wherever your bottleneck is.

Inflow Your records: documents, scans, tickets, recordings, tables

  1. Prepare

    1. Readiness Assessment
    2. Document AI & RAG
    3. Curation & Deduplication

  2. Protect

    1. De-identification
    2. Provenance & Compliance
    3. Synthetic Data

  3. Teach

    1. Fine-Tuning Datasets
    2. Expert Annotation
    3. Agents & RL Environments

  4. Prove & Run

    1. Evaluation & Red-Teaming
    2. Data Processing
    3. Managed Data Ops

Outflow Training-grade datasets, evaluation sets, and pipelines you own

Prepare

Find what you have, make it machine-readable, and remove what would teach the wrong lesson.

Protect

Take out what must never be learned, record where every record came from, and generate what you can't collect.

Teach

Turn your experts' judgment into the examples, preferences, and demonstrations that models learn from.

Prove & Run

Measure whether it worked, run the heavy processing, and keep the data improving after launch.

The Six Gates

Like a flight of river locks, each gate lifts the data one level and holds it there until it passes. Nothing skips a gate. Read the full method.

  1. Source

    Inventory every system, owner and right to use.

  2. Parse

    Make scans, tables, forms and audio machine-readable.

  3. Clean

    Remove duplicates, junk and anything leaked from your test sets.

  4. Protect

    Redact or pseudonymize what must never be learned.

  5. Label

    Capture your experts' judgment as examples, preferences and demonstrations.

  6. Prove

    Measure before and after, then write the provenance manifest.

A craftsperson measures a sand mold for a bell with calipers and a curved template while a crucible of glowing metal waits unpoured.

We shape the test before we pour the data.

Most data projects are judged by volume: how many records labeled, how many hours transcribed. We judge them by whether the model got better at the task your people care about.

So before we label anything, we build the evaluation: a golden set of real cases your experts agree on, a rubric that says what good looks like, and an automated judge we have calibrated against those experts. Every dataset we deliver comes with a before-and-after score on that test. If a batch doesn't move the number, we say so and change course.

Evaluation, benchmarks and red-teaming

Built around the records each industry already keeps

Our analysts and reviewers come from the work itself, so they read your data the way your people do.

See exactly what you'd receive

We publish a complete sample datasheet and three illustrative engagement blueprints instead of testimonials. They show the deliverables, the checks, and the numbers we report.

From Insights

Field notes on data readiness, evaluation, agents, and privacy. All articles.

Jackson · Gulf South · United States

On Capitol Street, a short drive from your data

Our team works from downtown Jackson and travels to client sites across Mississippi, Louisiana, Alabama and Tennessee when the data can't leave the building. We work remotely with clients anywhere in the United States, always in US time zones and always with US-person staff.

AI data services across the Gulf South

OfficeBenner House LLC188 East Capitol Street, Suite 1350
Jackson, MS 39201
(601) 954-9979
hello@bennerhouse.com

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.

Start a project Take the readiness scorecard