The training & eval data
your AI is missing.
Tell Datastrap what good data looks like — a few examples or a description — and it generates as much as you need, checks every row for quality, sends only the unclear ones to a human, and writes the approved data into your own database. No labeling vendor. No shipping your data out. Ready in a day.
AI gets smart by studying examples.
Most teams don't have enough.
Think of it like a made-to-order library for the example data your AI learns from. You hand us a few samples; we produce thousands more — checked for quality — and put them on your own shelf.
You don't have enough good examples. Your AI needs a lot more.
Collecting or buying more takes weeks and real money — and usually means handing your private data to an outside labeling vendor.
Turns your few examples into thousands of good ones.
We generate realistic new examples, automatically check each for quality, ask a person only about the unclear ones, and write the finished set into your own database.
A ready-to-use dataset in a day, not a quarter.
For a fraction of the cost of a labeling contract — and your data never leaves your control, so privacy and compliance stay intact.
Who it's for: teams building or improving an AI feature — a support assistant, a document reader, an in-app copilot — who are stuck waiting on training data. If you've ever said “the model's fine, we just don't have enough data,” that's us.
From a tiny seed to a clean set —
end to end, you never touch the plumbing.
An API with a thin dashboard on top. Connect once; every approved row is written back to a staging table you name.
Connect
Scoped read/write to your Postgres/Supabase — or a CSV. Credentials live in a vault, never logged.
Add your examples
Point at a table or paste 10–50 examples. We read the structure automatically — fields, types, the lot.
Generate
AI turns your examples into thousands of realistic new ones that match your structure — including the rare and tricky cases.
Judge + review
A reasoning model scores every row. You only see the ambiguous ones — with the judge's reasoning attached.
Write-back
Approved rows land in your_db.staging, or export .jsonl. Nothing of yours persists with us.
One pipeline, many kinds of data.
We stay horizontal and let your results choose the wedge. Same loop, whatever your rows look like.
A few ideal replies → a full fine-tune set.
Show Datastrap a few well-written support tickets and ideal responses; it generates as many on-brand, schema-matched pairs as you need — judged for faithfulness and diversity, deduped, and written straight back to your warehouse.
Synthesize regulated data — without it leaving the VPC.
Classification and extraction over financial documents, where compliance forbids shipping data to a labeling cloud. Datastrap reads a small seed in place, generates and judges, and writes approved rows back — the Enterprise tier runs the worker inside your own VPC.
Generate the edge cases your agent keeps failing.
Seed a few hard examples; Datastrap parameterizes the long tail — adversarial prompts, rare states, synthetic negatives — and the judge keeps the set diverse and clean. Pull it via API straight into your eval harness.
A few hand-written rows and a deadline become as many judged, schema-matched rows as you need — a usable .jsonl in an afternoon, not a six-week SOW.
We read a small seed, generate and judge in memory, and write only approved rows back to a table you name. Nothing customer-identifiable persists in our cloud.
An LLM judge scores every row on faithfulness, diversity, and format. You only ever see the genuinely ambiguous rows — with the judge's reasoning shown.
Metered on approved rows written back — the rows you actually use. Rejected rows are on us. Our incentive is wired to match yours.
Built for the bottleneck nobody else solves.
Scale, Snorkel, Tonic and Mostly are capable tools — for teams that already have a dataset, a budget, and a demo on the calendar. Datastrap is built for the moment before that.
Labeling vendors Scale · Surge |
Synthetic platforms Tonic · Mostly · Gretel |
Datastrap the cold-start wedge |
|
|---|---|---|---|
| Starting point | Need volume to label | Need an existing dataset to synthesize from | ✓ A few examples is enough to start |
| Where your data goes | Exported to their cloud | Replicated into their platform | ✓ Never leaves — written back to your warehouse |
| Quality guarantee | Human QA — slow | "Private," not "good to train on" | ✓ LLM-judge audited · precision reported |
| To get started | Sales call + SOW | Book a demo | ✓ Self-serve, minutes — no demo |
Reflects each category's typical positioning, not a feature-by-feature audit. These are good tools for a different stage.
Your data never leaves
We read a tiny seed and write finished rows back to your own database. No durable copy is kept.
No demo to sit through
Self-serve from day one. No sales call, no SOW, no six-week procurement.
Pay only for what you keep
Billed on approved rows you actually use. Rejected rows cost nothing.
No card, no commitment
Join the early-access list with just your email. Nothing to pay, nothing to cancel — we'll reach out before we open seats.
Metered on approved rows.
Solo & small AI teams escaping the cold start.
- → CSV + Postgres/Supabase connector
- → LLM judge, standard rubric
- → Legible review UI + audit panel
- → Download (.jsonl / .csv) or pull via API
Funded teams refreshing their training & test data on a cadence.
- → Everything in Bootstrap
- → Multi-reviewer queues
- → Custom English judge rubrics
- → Continuous precision audit + dedup reporting
Regulated teams — data legally can't leave the VPC.
- → Everything in Team
- → In-VPC worker · compute beside your data
- → SSO · audit logs · SOC2 evidence
- → Snowflake / BigQuery connectors
Launch pricing — provisional, may change before general availability.
Trust is the product — so we won't fake the numbers.
We're pre-launch. Below is the standard we hold ourselves to before any row reaches your training set — and the figures we'll publish here from real design-partner audits.
Figures shown are targets. Live design-partner metrics replace them here as we validate. Onboarding founding partners now.
No hand-waving.
Is synthetic data actually trustworthy?+
Every row is judged on faithfulness, diversity (measured by embedding dedup), and format. We launch with conservative auto-approve thresholds and run a continuous sampled audit — a random slice of auto-approved rows is re-checked by a human or a second independent judge, and we report that precision to you. We'd rather send you more rows to review than poison your model.
Does my data leave my environment?+
No durable copy is ever persisted by us. We read a small seed and hold in-flight rows transiently (TTL of minutes) only long enough to judge them, then write approved rows back and wipe the rest. If your compliance can't accept any egress, the Enterprise tier runs the worker inside your own VPC.
What if the judge is wrong?+
Two guards. Anything the judge isn't confident about never auto-clears — it goes to your review queue. And the sampled audit catches drift early: if precision slips, thresholds auto-tighten and more rows route to humans until calibration recovers.
How do you keep my credentials secure?+
Connection strings live in a dedicated secrets vault with per-tenant keys, never in our app database and never logged. We default to writing into a new staging table you name — never an in-place mutation of a production table.
How does pricing work, really?+
Billed only on approved rows written back — the rows you keep. Rejected rows cost nothing. A small monthly floor keeps the math honest on both sides. No per-seat surprises.
Stop waiting on a
labeling vendor.
Show us what good data looks like. Leave with a clean, ready-to-use dataset — inside your own systems, by end of day.
- → Onboarding a small group of founding design partners
- → Funded AI teams go to the front of the line
- → Founding-partner pricing locks for 12 months
Get on the early-access list
Just your email — no card, no commitment. We'll reach out before we open seats.
The orchestration layer for the data flywheel — before the warehouses bolt on a worse one.
Generation + LLM-judge finally cost cents per 1k rows; open table formats make "connect, don't copy" real. The market shifted from collecting data to curating it.
Funded AI teams already paying for labeling/evals. Postgres connector, text data, the sharpest cold-start pain.
Zero-copy posture + a judge quality-loop that compounds with usage + model- and warehouse-agnostic. We sit above any single vendor.
Pre-seed. A staged proof — judge precision >90%, gross margin >70%, priced intent — each gate before the next dollar.
Pre-product. Figures above are targets we're actively validating, not results.