Discover the world's first product designed to find model errors in audio AI applications.

LatticeFlow AI Vision

Solving AI Blind Spots Data Leakage Data Quality with Automation

Operationalize data quality control of your data supply chain and analyze custom and off-the-shelf model architectures to elevate your model performance. From satellite imagery and smart city analytics, all the way to preventive maintenance, manufacturing, identity verification or even 3D medical imaging use cases.


Proud to work with leading organizations

TE connectivity
US army
athena AI

Launch with Ease

Powerful Analysis Made Simple

Whether you are a machine learning engineer, data analyst or a person responsible for quality control, with LatticeFlow finding and fixing issues in your data and models is just one step away.

Step 1
Select Data Storage

Integrate with existing products for cloud data storage or labeling to easily ingest your data, or simply upload from local storage.


Step 2 (Optional)
Plug-in your Models

Plug in your own model or choose from 100+ out-of-the-box model integrations.

Step 3
Take Action and Benefit

Review automated suggestions provided by LatticeFlow or integrate your custom domain-knowledge to improve your data and models.


Model Diagnostics

Model Blind Spots

Establish a systematic process to identify and fix critical model errors before they get into production. Avoid being overwhelmed by black-box AI models and the painstaking process of finding the root cause of model failures. Instead, take advantage of deep model integration to analyze your custom models and to generalize individual model failures into common root causes.


Custom Hypotheses

Is the model underperforming for a given background, occlusion, colors or lighting? Formalize your intuition as a statistical check in minutes.

Model Blind Spots

Analyze internal model structure to uncover dataset subsets where the model systematically underperforms and take actions to fix them.

Error Root Cause

Avoid expensive iteration cycles by identifying groups of samples that fail due to the same reason and take action.

Reliable Model Evaluation

Go beyond aggregate model performance metrics to uncover subgroup regressions and connect with business metrics.

Validate Failure Hypothesis

Do you have a couple of samples where you know your model underperforms?

Avoid expensive iteration cycles by checking custom hypotheses, by generalizing individual model failures and finding their root cause.

Understand Model Predictions

Use feature attribution to understand if your model is behaving as expected and relies on the right elements of the image to make accurate predictions.

Take model understanding to the next level by finding attribution patterns responsible for degraded model performance across the whole dataset, not just individual samples.

Auto-diagnose Model Blind Spots

Do you know whether your model learned unwanted patterns that can cause costly errors in production?

Analyze internal model structure to uncover dataset subsets where the model systematically underperforms and take actions to fix them.

Model-guided Data Collection

Be smart about how new data is collected to minimize data collection costs and ensure the highest impact on model performance. 

Create data collection campaigns with your preferred labeling solution that is guided by your model blind spots and hard samples.

Data Diagnostics

Operationalize Data Quality and Curation

Eliminate manual, repetitive, and time-consuming tasks and instead, focus on improving data quality at scale. Find anomalous samples, inconsistent labeling, poor quality samples, data leakage, unbalanced data distributions, and more. More importantly, build a quality gate that automates data quality checks, tailored to your specific application and requirements.


Data at Scale

Save time and avoid errors by understanding the distribution of your labelled and unlabelled data, avoid data duplication and ensure data representativeness.

Production Issues

Expand and generalize existing issues at scale – go from a couple of incorrect samples to finding similar problems in the whole dataset.

what to Label

Not all samples are created equal. Curate representative subsets and query unlabelled datasets for impactful samples to include in training or testing.

Data Quality Checks

Remove unwanted ambiguities, low quality samples and eliminate data leakage, all in an automated way and tailored to your needs.

Fix Low Quality Samples

Use our quality score to identify errors that you can quickly triage and fix. Find anomalous samples, inconsistent labeling, wrong cropping and more. Collaborate and share the findings with your team.

Gain Model Insights

Assess the quality of your data through model-guided data analysis. Find and generalize data issues, fix wrong or inconsistent labels, poor quality samples, or unbalanced data distributions.

Find Data Issues at Scale

Expand and generalize existing issues at scale – go from a couple of incorrect sample to finding similar problems in the whole dataset. Identify risks for data leakage and remove duplicates from your datasets.

Data Representativeness

Not all samples are created equal. Curate representative subsets, identify regions with degraded model performance and distribution shifts to improve the quality of your data. Query unlabelled datasets for relevant samples to include in your training or test dataset.

Model Robustness

Purpose-built to ensure performance, safety, and reliability

Build AI models with safety and reliability guardrails from the beginning, not as an afterthought. Just because your model excels in the validation environment, it does not guarantee its success when raw unlabelled samples are streaming in. Instead, start assessing your models beyond aggregate model performance and labelled datasets only, to build trust that they perform as expected.


Coming Soon

with Quality Standards

Adapt the latest ISO standards on data and model quality as part of your development process and CI/CD pipeline.

Systematic Model Failures

Gain a deep understanding of model failures in normal operational environments and across critical subgroups.

Model Risk

Establish standardized quality assessment reports and model stress testing that are easily understandable and shareable.

Deployment Decisions

Deciding which model to deploy requires reliable model evaluation that takes into account data quality, bias, representativeness and more.

Streamlined Experience

Benefits Everyone on your Team

All stakeholders in your organization including data analysts, domain experts, ML engineers can collaborate using a single source of truth in real-time to find and fix issues at scale.



Plug-in your data storage or labelling tools to easily import your existing data into LatticeFlow.

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Dynamic Embeddings

Not one or two fixed embeddings, but a dynamic amount tailored for the task at hand.

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Model Comparison

Compare models by focusing on critical subpopulations and model regressions.


Data Levels

Analyze your data at the right level of abstraction, whether its the full sample or individual objects. 


Data Leakage

Automatically detect and eliminated data leakage in your datasets.


Learn more



Define new attributes that matter for your domain in minutes and generalize them to unlabeled data.

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Built for everyone on your team to share the findings and collaborate on solutions.


Spurious Correlations

Discover spurious correlation using model attribution methods and attribution aware model representation.

What our customers say

Innovate responsibly without compromising safety!

Did we mention LatticeFlow is fully secure? Your data and model never leaves your own server. Our hosted solution is secure and optimized for performance. Try LatticeFlow on your custom models or choose from a wide range of out-of-the-box model integrations.

Asked Questions

LatticeFlow offers both private cloud and on-premise deployment options to ensure you have complete control over your data and its privacy.

Yes, you can by taking advantage of the data diagnostics capabilities of LatticeFlow.

Yes, LatticeFlow provides a flexible infrastructure that allows users to upload their own metadata. Both system and user-provided metadata is indexed and made available for search, filtering and analysis.

Yes, LatticeFlow includes both the no-code web UI as well as python SDK that supports running the analysis and exporting the results as part of CI/CD.

Yes, our enterprise plan includes the ability to deploy LatticeFlow in secure sandboxed environments. For more details, contact us to learn more.

Yes, we support native integration of your custom models. 

We support models trained using PyTorch, Tensorflow 2, Keras, MMLab, FastAI, as well as many off-the-shelf architectures such as Yolo, Detectron2, DeepLabV3 and many more.

LatticeFlow provides off-the-shelf integration with popular foundational models, simply by selecting the framework and the model type to integrate. Similarly, any custom features or metadata can be integrated to enhance the analysis.

Let's talk!​