Insurance
Claims that settle themselves
Triage, fraud signals, and underwriting drafts trained on your loss history — the one dataset no frontier lab has ever seen.
Sovereign AI · Regulated enterprise
Your alpha shouldn't train someone else's model
Erdmes Labs turns your claims history, routing data, and contract archives into frontier-grade models you own. Trained on open weights, measured against your evals, deployed in your jurisdiction.
Built for the boring and the regulated
Three archives. Three models. One owner.
Logistics
A dispatcher that knows your network
Exception handling, ETA prediction, and routing decisions tuned to your lanes, your carriers, your customers.
Legal ops
Review that follows your playbook
Contract review and clause extraction trained on your negotiated positions — not the internet's average contract.
Frontier APIs are trained on everyone. A model trained only for you — on decisions only you have made — is an asset on your balance sheet, not a line item on someone else's.
The pipeline
Measure it. Train it. Own it.
A productized path from your archives to a deployed model, in weeks, not quarters.
01 · Evaluate
Your workflow becomes a benchmark.
Before we train anything, we build the yardstick. Your historical decisions become machine-checkable evals that show precisely where general-purpose models fall short on your work.
Ground truth from your archive.Settled claims, completed routes, negotiated contracts — outcomes you already know are correct.
Frontier baseline, measured honestly.We score the best closed models on your eval first. If they already win, we tell you, and you've lost nothing.
02 · Train
Open weights, tuned against your reward.
Reinforcement learning and preference optimization push an open model past the frontier baseline on the only benchmark that matters: yours.
Reward functions from your KPIs.Loss ratio, on-time percentage, clause accuracy — the model is optimized toward the numbers your business already reports.
Every run inspected.Rollout-level observability catches reward hacking and regressions before they reach production.
03 · Own
Deployed in your jurisdiction.
Checkpoints promote to production inside your VPC, your datacenter, or your national cloud. The weights are contracted to you as IP.
Zero train-to-serve mismatch.The model ships in the same harness it was trained in, with replicas that spin up in seconds.
Yours even if we disappear.No API dependency, no vendor lock-in. The model runs wherever you do, for as long as you want it to.
A run, end to end
What week one looks like.
One workflow. One eval. One honest number before anyone commits to training.
The specialization layer
Six things a frontier API will never do for you.
#1 Measure
Eval harness construction
We turn your historical decisions into machine-checkable benchmarks before any training begins. You see exactly where frontier models fail on your work.
#2 Train
Frontier training stack
Reinforcement learning and preference optimization on the best open-weights models, from 1B to 1T+ parameters, scored against your reward functions.
#3 Deploy
Deploy in any jurisdiction
Your VPC, your datacenter, or your national sovereign cloud. Training runs where the data lives — nothing crosses a border you didn't approve.
#4 Compound
Continuous improvement
Production traces become training signal. Your model gets better every quarter your teams use it, instead of waiting for someone else's release cycle.
#5 Upgrade
Model-flexible by design
When a stronger open base model ships, we re-run your pipeline on it. Your evals, data, and harness carry over unchanged.
#6 Comply
Compliance-first
Built for GDPR, the EU AI Act, and local data-residency law from day one, because our customers are the companies that can't ignore them.
The sovereignty layer
Nothing leaves. Everything compounds.
01
Your data never crosses a border.
Training and inference run inside your infrastructure or your chosen sovereign cloud. We never take a copy. We bring the pipeline to the data.
02
Your weights are yours.
Every engagement contracts the trained model to you as intellectual property. If we part ways, the model stays, and keeps working.
03
Your edge compounds.
Every decision your teams make becomes signal for the next training run. Competitors calling the same public API get the same public model. You don't.