Find the weakness.
Understand where your model fails and which gaps matter to the task. Start with evidence from development data.
Model-aware data optimization
Build the next dataset around what your model needs.
Turn model weaknesses into better data decisions. Generate or import candidates, review their validity, prioritize a batch, and measure its learning value within your budget.
Early-stage product · Initial focus: driving perceptionTHE ITERASTRA LOOP01 — 06
- 01 / DIAGNOSEModel weakness
- 02 / BUILDCandidate data
- 03 / VERIFYValidity & review
- 04 / PRIORITIZEBatch selection
- 05 / MEASURELearning test
- 06 / DECIDENext investment
A decision process, grounded in your task and budget.
More learning from every data dollar.Model feedback · Reviewed candidates · Independent evaluation
The decision behind the datasetMore data brings
More data brings
more decisions.
Generation, annotation, review, and training all consume resources. The next useful batch depends on your current model, existing data, target conditions, and evaluation task.
Check the candidates.
A convincing image is not enough. Review the scenario, verify the labels, and record what is approved or unknown.
Test the learning value.
Compare selection approaches under a controlled protocol. Let independent real evaluation guide the next investment.
How it worksA clearer path to
A clearer path to
your next batch.
Iterastra is building a workflow that connects model feedback, candidate review, selection, and controlled learning experiments.
- 01
Define success.
Specify the task, target conditions, evaluation metrics, acceptable regressions, and budget.
- 02
Find the gaps.
Review data coverage and model failures on development examples, keeping final evaluation independent.
- 03
Build and review.
Generate or import candidates, check labels, and document technical validity and human review evidence.
- 04
Choose the next batch.
Compare random, quality-based, and model-targeted selection within agreed batch and cost limits.
- 05
Measure and decide.
Test on independent real data. Keep the approach supported by the result—even when that is a simpler method.
The first applicationStarting with
Starting with
driving perception.
We are preparing a synthetic-data workflow for pedestrian and car detection under challenging visibility conditions.
The first pilots will connect scenario review, verified labels, model failure analysis, and controlled training comparisons.
Cosmos is the first planned video-generation integration. Actual clip generation and real learning benchmarks are pending.
Pilot scopeOne task.
One task.
Four scenario groups.
NightLOW VISIBILITY
FogREDUCED CONTRAST
RainCHALLENGING CONDITIONS
Clean referenceREGRESSION CHECK
Initial labels: pedestrian/person and car. Scenarios and visible boxes require review.
Evidence before expansionEvery recommendation
Every recommendation
needs evidence.
Candidate scores help prioritize an experiment. Learning claims require a controlled test. We record the data, model, settings, outcomes, and costs behind each comparison.
An estimate of priority.
Model errors, reviewed quality, target weaknesses, and exact-duplicate signals help select a batch worth investigating.
A score is a hypothesis, not proof that a sample will help.
Measured learning value.
Independent real evaluation measures conditional batch effects, regressions, uncertainty, and recorded costs.
Inconclusive and negative outcomes are valid results.
Test the next batchKeep simpler selectionReview more data
Built around the taskOne decision loop.
One decision loop.
Task-specific evaluation.
The core workflow is designed to be reused across models and data types. Adapters define the labels, correctness checks, and outcome metrics for each task.
Driving perception is our starting point. Robotics and document workflows are future applications; each needs its own data, evidence, and definition of success.
A shared methodology, adapted to your taskScoped technical pilotsHave a data decision
Have a data decision
coming up?
Tell us what your model struggles with, what data you have, and what you plan to generate or curate next. We will assess whether a scoped pilot can produce a useful decision.
Start with your decision.
- Your task and target conditions
- Available data, labels, and model predictions
- The next batch you are considering
- Your evaluation evidence and compute limits
Opens your email app. Contact asimonyan@iterastra.ai.
Share a description first; no dataset uploads needed.
Questions, answered
Before we begin.
What does Iterastra do?
Iterastra connects data generation or import, quality review, model feedback, sample selection, and learning evaluation to help teams decide what data to invest in next.
Can you tell whether a sample will help before training?
We can estimate priority from the current model’s errors, target weaknesses, quality evidence, and redundancy. Confirming learning value requires a controlled training experiment and independent evaluation.
What is the first application?
Synthetic data for driving perception, initially focused on pedestrian and car detection in challenging visibility conditions.
Is Iterastra only for Cosmos?
Cosmos is the first planned video-generation integration. The shared methodology is designed around task and provider adapters, with different correctness checks and outcome metrics for each application.
What if targeted selection does not help?
The experiment should report that outcome. A simpler selection method, more review, or additional evaluation data may be the appropriate next action.
Is a hosted platform available?
The current implementation is a local pipeline. The launch offer is a scoped pilot; a hosted product is a future direction.
What does a pilot require?
A defined data decision, permitted examples, task and label conventions, and available evaluation evidence. Model predictions and training access determine how far the analysis can go.