Capsa is faster, smarter, and more accurate than alternatives.

Improved Accuracy

Capsa enables risk mitigation by completely automating accurate uncertainty quantification for any machine learning model.

Reduced Time and Cost

Capsa reduces the compute cost required to train and de-risk AI models by 89%, a major source of savings.

Trustworthy Output

Broadens the range of enterprise products that can be offered without risking harm or loss of trust with clients.

Capsa is faster, smarter and more accurate than alternatives.

Improved Accuracy

Capsa enables risk mitigation by completely automating accurate uncertainty quantification for any machine learning model.

Reduced Time and Cost

Prioritizing training on highly uncertain data allows for greater training efficiency, accuracy and performance.

Trustworthy Output

Broadens the range of enterprise products that can be offered without risking harm or loss of trust with clients.

Convert any model, from any framework, at any stage of development.

1. Load your model

Capsa is data- and model-agnostic, compatible with vision, language, graph, and generative AI models of any size or architecture.

# Load your model
_model = Model()

2. Convert your model

Capsa integration is simple: a single one-line call that converts an existing model into an uncertainty-aware variant, leaving the rest of the code unchanged.

# Convert your model
model = capsa_torch.wrapper(_model)

3. Get augmented results

Get uncertainty estimates with every output so your model "knows what it doesn't know" without having to manually alter each layer in your model.

# Get results
pred, risk = model(input, return_risk=True)

, σ

1. Load your model

Capsa is data- and model-agnostic, compatible with vision, language, graph, and generative AI models of any size or architecture.

# Load your model
_model = Model()

2. Convert your model

Capsa integration is simple: a single one-line call that converts an existing model into an uncertainty-aware variant, leaving the rest of the code unchanged.

# Convert your model
model = capsa_torch.wrapper(_model)

3. Get augmented results

Get uncertainty estimates with every output so your model “knows what it doesn’t know” without having to manually alter each layer in your model.

# Get results
pred, risk = model(input, return_risk=True)

Node

Weight

Layer

, σ
, σ

Easily estimate ambiguity and uncertainty.

Find errors

Automatically find errors in training data and easily identify inputs with high ambiguity.

Report uncertainty during inference

Report uncertainty with every output at run time and automate quality assurance by filtering low certainty output or requesting human-intervention.

Detect Failures

Automatically detect failures before they take place.

Find errors
Report uncertainty
Detect failures

Automatically find errors in training data and easily identify inputs with high ambiguity.

Report uncertainty with every output at runtime and automate quality assurance by filtering low certainty output or requesting human intervention.

Capsa automatically detects prediction risk. In this example, Capsa correctly highlights the passing bus, which sits outside the training set.

Note: Bright colors represent areas of high uncertainty flagged by Capsa that were missed by the initial model.

A single line of code to make your model safe and reliable.

PyTorch
TensorFlow

import torch
import capsa_torch

_model = Model()

# Wrap your model
model = capsa_torch.wrapper(_model)

# Your model is now uncertainty-aware
pred, risk = model(input, return_risk=True)

Copy

import tensorflow as tf
import capsa_tf

# Add a decorator
@capsa_tf.Wrapper()
@tf.function
def model(...):
   ...

# Your model is now uncertainty-aware
pred, risk = model(input, return_risk=True)

Quantify multiple types of uncertainty.

Aleatoric Uncertainty

What the model can’t understand from data due to ambiguity and irreducible noise like errors.

Epistemic Uncertainty

What the model doesn’t know due to limited knowledge or data.

Vacuitic Uncertainty

What the model can’t understand due to latent feature imbalance.

Solutions for every stage of the ML workflow.

  • Data cleaning

    Optimize data labeling costs by finding incorrect or noisy labels.

  • Data curation

    Identify missing data to intelligently curate datasets.

  • Model conversion

    Convert any model to be safe and reliable.

  • Risk-aware training

    Optimize models to overcome their deficiencies.

  • Quality control

    Targeted validation on high-risk scenarios.

  • Anomaly detection

    Quickly detect out-of-distribution events.

Solutions for every stage of the ML workflow.

  • Data Cleaning

    Optimize data labeling costs by finding incorrect or noisy labels.

  • Data curation

    Identify data missingness to intelligently curate datasets.

  • Model conversion

    Convert any model to be safe and reliable.

  • Risk-Aware training

    Optimize models to overcome their deficiencies.

  • Quality control

    Targeted validation on high-risk scenarios.

  • Anomaly detection

    Quickly detect out-of-distribution events.