We red team AI · We train AI · We deploy AI

Your AI gave you a number. Would you sign it?

We make AI work you can put your name under. We find where it is wrong, we build the tests that train it, and we install it in your own building, proven first in a copy of a business like yours. Every figure names the record behind it.

Request a pilotSee the operator pilot
How we draw every figure Confirmedtwo records agree Leadan estimate or a single signal Discrepancytwo records disagree

The operator pilot

an invented oil and gas company, a month of its records, fourteen planted defects · hover for night
A three dimensional oil field under a caprock escarpment with well pads, a central battery, a disposal well, a drilling rig and farmland to the horizon. A panel reads 2,517 barrels of oil today, 10 discrepancies, 4 open leads and 954,152 dollars flagged.

Everything in this scene is invented: the land, the company, the wells and every record. Sky and ground textures are CC0 from Poly Haven.

Open the operator pilotOwner-side pilot

This is our test bench

A synthetic company on real ground: public filings, grid and weather feeds, aerial imagery, terrain. We plant defects on purpose. Any AI we build, test or train has to find them here before it goes anywhere near your records.

It keeps moving

Slide the date through the month and watch fourteen planted defects surface: a run ticket that never got paid, a haul ticket billed twice, injection pressure over the permit. Then run the replay and watch a local agent work the queue.

We build one for your trade

Oil and gas is the first. The same method builds a hauler, a fabrication shop, a quarry, a contractor or a clinic. If a public record exists for your industry, a test bench can stand on it.

Three things we do

one method under all three: state the rule, derive it twice, name the record
You already use AI

We red team AI

Find out where your AI is wrong before a lender, a partner or a regulator does. A fixed-price review in ten working days, tests of agents and vendor products, and reviews of the graders that score models.

See red teaming → You build or train models

We train AI

Living environments, tasks a strong model fails and a working professional solves, and graders that cannot be gamed. Built new from public data, in trades most vendors have never worked in.

See training → You want AI doing real work

We deploy AI

Agents that run in your building, on your hardware or one small box. Your data stays put. They call Claude or ChatGPT on your own account only when they need to, under limits you set. Updated on a schedule, tested every time.

See deployment →

The risk you are carrying

Fluent is not the same as right.

A model will give you a confident number with a clean explanation and no way to tell whether it read the right table, applied the right rule, or invented the threshold. The report looks finished. Someone signs it. The error surfaces at the closing, the board meeting or the deposition.

Our work on frontier-model training programmes is finding exactly those failures on purpose, hundreds of times, against a written standard, and proving each one. We turn that discipline on the AI you run, the AI you are about to buy, and the AI we install for you.

The record so far

on frontier-model training programmes since 2025
100s

of frontier-model evaluation tasks authored by our team, built to hold strong models below the difficulty bar

100+

task packages reviewed as a calibrated gate on frontier-model programmes, each on a written record

2

vendor programmes where our people were promoted to team lead and quality review roles

Dozens

of validators and test fixtures in our own toolchain, each written after a check missed something

What you get in your hands

every finding names the rule, the record and the check
Defect 07Material Confidence high
OutputMonthly variance report, section 3, headline figure
RulePopulation must be the closed-period records only
FoundThe model included 41 open-period rows in a closed-period total. Headline overstated by 6.2 percent.
RecordLedger extract, period status column, 2026-08 closeledger extract > period status > closed-period population
CheckA population count against the period status column before any sum. Would have caught it in under a second.
OwnerFinance, reporting pipeline step 2

Illustrative entry. Figures shown are an example of the format, not a client result.

How to start

small steps, each one useful on its own
Step 1 · free

Send us one output

One number, report or answer your AI produced, with the records it came from. We check it and show you the evidence.

Step 2 · free

Request a pilot

Tell us what you do, what software and hardware you run, and which job hurts most. A named person replies within one working day.

Step 3 · fixed price

Proof before commitment

The ten-day review of AI you already run, or your workflow proven in a test company like yours, with a scorecard. The fee credits against any build.

Step 4

Build, install, keep current

A monthly plan, a larger contract with services and updates included, or a build we hand over and step away from. Your choice.

Where we work

if your trade leaves a public record, we can build its test bench
Oil and gasoperators, non-operators, mineral owners Midstream and watergathering, disposal, hauling Oilfield services and fabricationservice companies, code shops Mining and aggregatesfrac sand, quarries, mines Construction and design-buildcontractors, structural and civil firms Powerlarge loads, small producers, co-ops Manufacturingshops with controlled drawings Agriculturefarm managers, elevators, lenders AI labs and vendorsenvironments, tasks, graders
Insurance and catastrophe claimsSmall medical and chiropractic practicesLocal government and appraisal districtsCollegesSmall legal and compliance firmsAccounting and land firms

The same jobs come up in almost every one of them: matching tickets to invoices, keeping the compliance calendar, preparing the regulator's form from the ledger, assembling a bid or a data room, explaining a variance, drafting the daily report. We build each once, with a checker, and fit it to your trade.

Questions we get

Does our data leave the building?

For routine work, no. The agent runs on your hardware. When a job is too hard for the local model, it can go to Claude or ChatGPT on your own account, only if you allow it, under a monthly cap, with a log of every call and an off switch.

What if it is wrong?

That is the question the whole firm is built around. Every output passes a check written in plain code before a person sees it, and every figure names its record. What fails the check goes to a person, not into your books.

We already trust our AI vendor. Why have it reviewed?

Your vendor checks that the model runs. Nobody checks that the answers are right against your records and your rules. If there is no gap, the report says so, and you hold that in writing.

How much of our time does it take?

About three hours across a ten-day review: a scoping call, an hour to arrange read access and pick the sample, and the readout.

Do we need new computers?

Often not. We start by asking what you run now. Where an office computer is too slow, one small quiet box does the work. We can supply it or tell you exactly what to buy.

What happens when the models change?

We refresh the local model on a schedule. No update reaches you until it passes a test set built from your own work. If it fails, you stay on the version that works.

Do you build on a particular model?

No. We use whichever model the work needs and your accounts allow. The checking discipline is the product, and it does not depend on a vendor.

How small a company is too small?

If a wrong number would cost you money, a client or a license, you are not too small. Several of our offers are built for an office of one to ten people.

The team

Muskegon, Michigan
Who does the workRagnarok Solutions

Experienced in AI training and evaluation, reviewing, tasking, building models and benchmark testing, along with serving as COO of a multimillion-dollar development company.

Real depth in trades where being wrong is expensive, a team behind the work, and the habit of treating every AI output as a claim that needs proof.

Not sure where to start? Send us one output.

One report, one answer, one number your AI produced, with the records it came from. We will check it, show you the evidence, and say what would have caught it. No charge for the first one, and no obligation after it.

hello@ragnaroksolutions.aiRequest a pilot