Data · Environments · Talent

The training and data infrastructure
layer for 

Institutional data licensed at the source, datasets written by experts in the top 1% of their field, and training environments built from real software.

lh2 · data pipeline
Institutional data in · PII stripped · Trainable data out
CODE repos · reviews · commits COMPANY OPS workflows · documents MEDICAL clinical · imaging · notes AUDIO speech · calls · ambient LH2 AI LABS frontier data core ANONYMIZED · VERIFIED · TRAINABLE AI TRAINING DATA text · code · multimodal EXPERT DATA reasoning · gold sets RL ENVIRONMENTS train & evaluate agents TASKS & BENCHMARKS expert-graded evals
0Tokens of proprietary data
0Top-1% experts across verticals
0Institutional partners
0Data verticals
ORIGIN
Coverage

Data, talent and environments across five verticals

Code
Software Engineering
Repos · Reviews · Commits
Enterprise codebases with the review history attached
Company Ops
Operational Data
Workflows · Documents
How a company runs, from intake to close
Medical
Clinical Records
Clinical · Imaging · Notes
De-identified patient records, clinical history intact
Audio
Speech & Sound
Speech · Calls · Ambient
Multi-speaker audio recorded in the field
Finance & Legal
Filings & Contracts
Filings · Contracts
Filings and contracts, checked, with the audit trail
// Trusted by top frontier labs and enterprises
LOCKED
01 · The bottleneck

The best training data was never on the internet. Labs train on the same scraped web. The next gain sits behind closed doors: codebases, clinical records, finance floors, and the reasoning of people at the top of their field. LH2 licenses that access.

Distributional gaps

Models fail in medicine, code and enterprise operations. The data describing that work never left the buildings where people made it.

Reasoning ceilings

Scraped text records conclusions. An expert writing out a differential, a code review, an audit trail records the steps that got there.

Evaluation blind spots

Agents that top public benchmarks break in production. Run them against a real workflow first and you see where.

STACK
02 · What we build

Three layers of the data stack

Raw institutional data at one end, graded agent runs at the other. Follow a record through it.

lh2 · data pipeline live
01 · Datasets
Real sources
github.com/acme/payments4,812 commits · 38 contributors
Re: Q3 supplier contractops@acme.com · 214 threads
Patient MRI · oncologyDICOM · 1,204 studies
Line-7 sensor logsPLC telemetry · 90 days
Support calls · 9 langsWAV · 12,000 hrs
Identifiers stripped · normalized
name ████████
dob ██/██/████
ssn ███-██-████
diagnosis non-identifying
Machine-readable dataset
{ "id": "rec_8842",   "modality": "multimodal",   "tokens": 18344,   "identifiers": "stripped" }
JSONL · Parquet
02 · Environments
Real product environments
🛒Checkout & paymentsapp.acme.com · Stripe flow
📇CRM & sales deskSalesforce · 40 objects
🏥Hospital EHREpic · order entry
💻IDE & CI pipelineVS Code · GitHub Actions
Replica simulation
mirrored state
live DB snapshot
API + service mocks
seeded user accounts
Real tasks · evals
✓ Refund a duplicate charge
✓ Reconcile the ledger
Handle a failed webhook
running · 2/3 0.91
03 · Talent · across the whole pipeline
◐ Structure and ground raw data
◑ Verify & de-identify
◒ Write expert solutions
◓ Rank outputs (RLHF)
◔ Grade & score runs
PIPELINES
03 · How it works

From locked away to training-ready

Three pipelines, one standard at the end. They run in parallel, and you can take any one on its own.

Pipeline 01 · Datasets

License it, clean it, ship it.

Institutional data licensed at the source, then cleaned and checked until you can train on it.

  1. SourceWe partner with the institutions that own the data, on terms both sides can sign.
  2. Clean & de-identifyWe strip identifiers, normalise formats, and attach provenance to each record.
  3. Verify & deliverDomain experts check the corpus before we hand it over.
Pipeline 02 · Environments

Real software, real tasks.

We rebuild a working copy of your product, run the real task in it, and grade what the agent does.

  1. ReplicateWe rebuild the environment from production data.
  2. ActAgents run the task with the friction and the edge cases intact.
  3. Grade & publishExperts score each run against outcome rubrics, and we publish the benchmark.
Pipeline 03 · Talent

2% admitted, calibrated weekly.

Experts calibrated to your rubrics, rechecked every week.

  1. SourceWe admit 2% of applicants, across all five verticals.
  2. CalibrateExperts train on your rubrics and gold sets before touching real work.
  3. Deploy & monitorAnnotation, evals and red-teaming, checked weekly. Weak work gets pulled.
TRUST
Governance

Built for data that carries obligations

Medical records, financial documents, proprietary code. The data worth training on comes with the strictest rules attached, so we built the company around meeting them.

001

De-identification

We remove and audit PII and PHI before data leaves the source institution. We check each field against the schema, redact it, and log what we did.

002

Provenance

Every record traceable to a consented, licensed origin. No scraped grey-zone data, no ambiguous rights: each dataset carries a clear chain of custody from the institution that owns it.

003

Controlled access

Data owners set the terms: usage scope, model types, exclusivity. We scope access to the contract, and we revoke it when the contract says to.

004

Security

Encrypted pipelines, access logs, and audit rights written into the contract. We record each touch of the data so you can review it.

For AI teams

Building at the frontier?

Tell us which capability you are pushing on. If we can move it, we will show you the data.

Start a conversation
For data owners

Sitting on valuable data?

Your code, records and recordings can train models. License them on your terms, and we handle the compliance.

Contact Us
Top 1% of your field? Get paid to write the data, models train on
Global operations

One network, every timezone.