Every failed training run costs time, compute and money — without telling you what to fix.
Real data is scarce, expensive, sensitive or legally blocked. You can't see the gaps.
Weak metrics don't tell you which dataset property caused them. You see the symptom, not the cause.
Data fixes are manual, inconsistent and slow down every training cycle.
Every failed training run costs time, compute and money — without telling you what to fix.
Define your dataset as a blueprint, generate it deterministically, then improve it with actual model feedback.
Repeat. Not guesswork. Measured iteration.
Blueprint defines entities, fields, distributions, scenarios and edge cases. LiteSeed generates a versioned dataset deterministically.
Train or fine-tune your model outside LiteSeed using the generated dataset in the format your stack expects.
Feed experiment metrics back in. LiteSeed correlates dataset properties with model outcomes and identifies likely weak spots.
Apply a structured blueprint patch and regenerate. Each iteration is versioned, reproducible and easier to compare.
ML engineers need to see that the system is deterministic and API-controllable. Same blueprint, same seed, same row count — same dataset.
Dataset structure is explicit, versioned and patchable.
LLM-assisted decisions are frozen before generation starts.
Model metrics feed the next dataset iteration instead of forcing manual guesswork.
Increase refund denial edge cases and add contrast examples for partial refunds.
Shift scenario weights toward ambiguous refund requests with short user inputs.
Select a domain and walk through every step — from blueprint to trained model to patch.
Join the waitlist to lock in early access pricing.
For individual ML engineers exploring synthetic data.
For ML teams running regular fine-tuning cycles.
Full Closed-Loop Automation for teams that train at scale.
Custom deployment, unlimited scale, white-glove support.
Need a single batch? ($2.00 per 1k rows). Overage rates for subscribers: Starter: $1.50/1k · Professional: $1.00/1k · Business: $0.75/1k
LiteSeed focuses on speed, control and dataset iteration. It does not currently focus on manual curation workflows.
No accept, reject or edit workflow for individual rows after generation.
Detected gaps are not filled automatically without a patch and re-generation cycle.
Since curation is not part of the current workflow, filtered export is not available either.