LiteSeed
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Model-driven data generation loop

Architect the data your model needs where it fails.

Generate data, train your model, feed back the results — and let LiteSeed create what’s missing. Repeat.

See how your model improves →Start generating →
Synthetic data generation·dataset augmentation·model-driven data creation
Model-driven synthetic data generation
Run #17 → LiteSeed → Run #18
Live loop
01 · Model feedback from last training run

Model feedback from last training run

False negatives
428
Top missed patterns (extracted from evaluation results, analyzed by LiteSeed)
›transactions < $20
›new device after login
›checkout after midnight
The problem

Your model fails.
You don't have the data to fix it.

Every failed training run tells you something is missing — but not what to generate next.

You can't generate what you can't see

Model failures don't come with a dataset prescription. You see the metric drop — not the missing scenario.

Real data doesn't cover edge cases

Production failures happen in the long tail. Real datasets rarely include enough of these scenarios to train on.

Iteration cycles are too slow

Collecting, labeling and versioning new training data takes weeks. Your model needs it now.

Every failed run is a generation opportunity

Each training failure contains a signal. LiteSeed turns that signal into new training scenarios — automatically.

The mechanism

Generate training data — and improve it with every iteration

LiteSeed is built to generate large-scale training data.

Instead of guessing what to generate next, it learns from your model’s performance and improves every new dataset automatically.

Closed loop
Generate
Compile (LLM once)
Train
Evaluate
Generate better data
Generate ↺
Generate

LiteSeed generates large-scale structured training data — deterministically, reproducibly, and at any scale.

Train

Train or fine-tune your model on the generated dataset in the format your stack expects.

Evaluate

Evaluate real model performance. LiteSeed uses the results to understand what the next dataset should improve.

Generate better data

Generate an improved dataset based on real performance outcomes. Each iteration is versioned and reproducible.

Key insight
Most teams spend weeks guessing what data to generate next. LiteSeed improves every dataset automatically — based on real performance.
Real scenarios

Design complete training datasets — and fill the gaps your model exposes

LiteSeed generates both full training data and the specific scenarios your model is missing.

scenario_patterns.log
# production-like failure regions detected
Infrastructure

Built for production pipelines

No mock data. No toy generators. Real infrastructure.

Stream datasets at scale

Pipe generated data directly into your training infrastructure without intermediate storage.

Deterministic generation

Same schema, same seed, same row count — same dataset. Reproducible across every run.

No memory bottlenecks

<512 MB footprint. Runs alongside your existing pipeline without resource conflicts.

Works with your training stack

Outputs in OpenAI Chat, JSONL, CSV, Parquet — no adapter layer required.

Minimal API example

Generate → Train → Evaluate → Generate better data

ML engineers need to see that the system is deterministic and API-controllable. Same blueprint, same seed, same row count — same dataset.

Generation-first

LiteSeed starts by generating large-scale training data — then improves it using real model performance.

Compile once

LLM-assisted decisions are frozen before generation starts.

Improve from outcomes

Model metrics feed the next dataset iteration instead of forcing manual guesswork.

Generate → Train → Evaluate → Generate better data
Deterministic · API-controllable
# 1. Generate initial datasetdataset = liteseed.generate(schema, scale=10_000_000) # 2. Train your modeltrain(dataset) # 3. Evaluate performancemetrics = evaluate(model) # 4. Generate improved datasetimproved = liteseed.generate(    based_on=metrics)
Possible recommendation

Increase refund denial edge cases and add contrast examples for partial refunds.

Possible recommendation

Shift scenario weights toward ambiguous refund requests with short user inputs.

Pricing

Start generating training data

Join the waitlist to lock in early access pricing.

Starter
Prototyping
$99/mo

Generate up to 100k rows per month. Includes scenario generation and iterative improvement.

100k High-Fidelity Rows / month
Basic Blueprint Engine
CSV, JSONL, OpenAI Chat export
Basic metrics feedback
Email Support
Best Value
Professional
Best Value
$299/mo

Generate up to 500k rows per month. Supports iterative improvement with full versioning.

500k High-Fidelity Rows / month
Edge-Case Injection
All export formats
Full metrics feedback + versioning
Priority Support
Business
Closed-Loop Automation
$999/mo

Generate up to 2.5M rows per month. Full iterative improvement automation at scale.

2.5M High-Fidelity Rows / month
Full Closed-Loop API
Custom export pipelines
Advanced data intelligence
Dedicated ML Engineer
Enterprise
Custom Deployment
Custom

Custom deployment, unlimited scale, white-glove support.

Unlimited rows
Custom Deployment
Custom export pipelines
Advanced data intelligence
24/7 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

Final close

Every iteration without feedback is wasted training time.
Start improving your data now.

Your model performance is limited by how fast your data evolves.

Review the API sketch