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

LatticeFlow AI Audio

Fighting AI Blind Spots Deep Fakes Lack of Trust with Deep Insights

Ensure the safety and performance of audio models with LatticeFlow AI Audio by uncovering critical blind spots and operationalizing data quality. Join leading AI teams by analyzing both custom and off-the-shelf AI models for Automatic Speech Recognition, preventive machine maintenance, biometrics and identity verification, deepfake detection and more.


Proud to work with leading organizations

TE connectivity
US army
athena AI

Use Cases

Powerful analysis across audio use cases

DeepFake Detection-transparent
DeepFake Detection-transparent

Analyze DeepFake
Detection Models

Stay ahead of the curve by analyzing deep fake detection AI models to ensure their robustness.

Automated Speech Recognition-transparent
Automated Speech Recognition-transparent

Analyze Automatic
Speech Recognition Models

State-of-the-art multilingual models to convert raw audio signal into spoken language.


Analyze Preventive
Maintenance Models

Audio analytics models for preventive maintenance to detect machine malfunctions and anomalies ahead of time.

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 noise, speaker or frequency? 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.

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.

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 audio starts 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.

Use Cases

Powerful analysis across speech use cases

Computer Vision

The first product that can automatically diagnose model blind spots in audio AI.


The first product that can automatically diagnose model blind spots in audio AI.


The first product that can automatically diagnose model blind spots in audio AI.

Streamlined Experience

First Class Support for your Audio Data

Building AI Models is already hard, do not let lack of suitable ML Ops tools get in your way and block your progress. We have built LatticeFlow AI Audio to provide native support to your audio data such that you do not have to.

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Spectrogram View

Visualize audio files natively using a controllable spectrogram view.

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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.

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Audio Similarity

Specialized algorithms to efficiently compute similarity of audio files with different lengths and transformations.

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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.

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 an on-premise as well as private cloud deployments, which means that the customer has the total control over the data access and security settings.

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

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.

Let's talk!​