Messy data in.
Signed training data out.
Refine your data or synthesize new examples — one flat price, quoted up front and capped, with a certificate you can re-verify.
Fine-tuning is blocked by data. Either it's messy — or there isn't enough of it. And the fix is usually a blank check with no receipt.
Messy in, garbage out
Inconsistent formats, missing values, duplicates, PII, raw noise. Cleaning it by hand is slow, error-prone, and never quite finished.
Or not enough data at all
Sometimes the problem isn't noise — it's volume. You need more high-quality examples for the cases your model keeps failing, and hand-writing them doesn't scale.
A blank check, no receipt
Hand the job to an autonomous agent and the meter just runs — no quote, no ceiling. And you still get no signed proof of what was cleaned, what it cost, or that PII is actually gone.
Refine what you have, or synthesize what you're missing.
Whichever you pick, you get one flat price, quoted before any work and capped. No metering, no surprise bills — the cap is the guarantee.
The cap is the guarantee. The agent runs autonomously inside it and can't bill you past it. You're only pinged if finishing a job would exceed the cap you approved.
Refine
Give us your messy data in any format. We return a clean, PII-masked, deduped, schema-valid ShareGPT / ChatML set — ready to fine-tune on.
Synthesize / Augment
Need more data? We generate new training examples — or expand your set — and keep only the ones a strong model solves that a weak one fails. That's the AutoData Δ-filter: every kept row earns its place.
Autonomous within a budgeted cap. You're only pinged on an overrun.
Accept the flat capped quote and that cap pre-authorizes the agent to run on its own within budget. The only time it stops to ask is when finishing would cost more than the cap you approved. Here, the last 1,240 scanned PDFs need a paid OCR pass that would run $4.00 over the cap — approve a top-up, or keep your cap and take what fit. Try it:
The agent has run the whole job inside the cap. The last 1,240 records are locked in scanned PDFs that need a paid OCR pass — finishing them would push spend $4.00 past the cap you approved. This is the only time it stops to ask.
The agent won't spend past the cap you approved. Approve a top-up, or keep your cap.
A clean dataset, and a receipt you can re-verify.
You get production-ready JSONL — plus an Ed25519-signed certificate that re-runs the checks (PII, dedup, schema) and records what you were quoted, what it actually spent, and the margin. Checkable by anyone, owned by no vendor.
It spends real money to do the work — and signs a receipt you can re-verify.
A real autonomous agent, governed by a budget. It runs within the cap you approved, books the margin, and would rather hand you nothing than fake a row.
Governed by a budget, not a vibe
The agent quotes a flat cap, runs autonomously inside it on NVIDIA DGX Spark, meters its own real spend, and books the margin. The cap is a hard ceiling — it can't bill you past it without asking.
A certificate you can re-verify
Every job ships an Ed25519-signed certificate that re-runs the checks — PII, dedup, schema — and records what was quoted, what was spent, and the margin. Checkable by anyone, owned by no vendor.
One flat price, quoted up front and capped.
No metering, no surprise bills. You see the number before any work starts — get a real one in seconds.
Your messy data, cleaned and certified. Priced by size, complexity & data type.
example quote — your number depends on the job
Get my quoteNew training examples, generated and filtered. Priced by target rows.
capped before any work — no surprise bills
Get my quote