The numbers are public, and so is the corpus that produced them.
Chandra hits 85.9% on olmOCR-bench, and your team can verify it against the published corpus instead of taking our word for it.
USE CASE · MODEL TRAINING DATA
Clean training data, at the throughput your pipeline needs.
When you build a training corpus, the parse is where signal gets lost — tables flatten, equations break, scripts mangle. Datalab turns crawls, archives, and papers into clean, layout-preserving markdown at scale.
TRUSTED BY
Five million pages of scientific literature, parsed in days.
CASE · 04
Radical AI
Read study →"We were quite blown away by the quality of extraction, especially on the type of complex academic papers that we were passing in."
- 5M+ PAGES INDEXED, IN DAYS
- 2–3/s PAGES PER SECOND, SUSTAINED
- 16 wks TO MATERIALS PERFORMANCE GOALS
From crawl to corpus, on a pinned pipeline.
from datalab_sdk import DatalabClient
client = DatalabClient()
PIPELINE_ID = "p_training_corpus_v3" # published, immutable version
for doc in crawl("s3://crawls/2026-q1/"):
execution = client.run_pipeline(PIPELINE_ID, file_path=doc)
execution = client.get_pipeline_execution(execution.execution_id, max_polls=300)
result = client.get_step_result(execution.execution_id, step_index=0)
if result.parse_quality_score >= 3.0:
write("s3://training/2026-q1/", result.markdown)
# > 100M+ pages parsed at sustained throughput
# > Parse Quality Score filter applied (>= 3.0)
# > pipeline version pinned - bit-identical across re-runsWhy teams prefer Datalab for model training.
When a document type breaks the parse, the next release fixes it.
If documents in your corpus parse poorly, our research team takes them back to Chandra and ships a fix in the next release.
We tune to the documents you actually care about.
Scientific notation, multilingual scripts, microfilm scans, equation-heavy PDFs — the parts where parsing usually drops are what we improve.
Run it wherever your training data has to live.
- Managed cloud
Start with an API key — nothing to host or operate.
Sign up → - EU data residency
Run in-region, with no egress to US infrastructure.
Sign up → - Your VPC
Runs inside your own AWS, GCP, or Azure account.
Talk to sales → - On-prem & air-gapped
Fully offline, the same model weights, dedicated support.
Talk to sales →
SOC 2 Type II · BAA available View our trust center →
SCALE FOR TRAINING
- 100M+pages a day, sustained
- 89.9%olmOCR-bench tables · open model
- 67.7k+open-source stars · Marker + Surya + Chandra
START
Stop training on dirty markdown.
Free tier with up to $20 in credits per month — no card required.