Real-time adaptability for your models

Orca helps machine learning teams update models instantly as business needs and data change, ensuring peak performance.

Build AIs as dynamic as the real world

Instantly expand to new use cases or environments

Problem
Expanding to new use cases can be difficult and time-consuming due to the lack of a robust pre-existing data set.  
Solution
Orca enables you to quickly build a new production-ready model using a small "golden" dataset. Then, as new data comes in, you can seamlessly optimize your model to improve performance without retraining.

Keep up with rapidly-evolving data

Problem
Large volumes of data often lead to data drift, which degrades AI/ML model performance, especially when data arrives faster than it can be processed
Solution
Orca uses a proprietary retrieval augmentation architecture that splits the model's knowledge from its reasoning capabilities. As a result, you can continually update your model's knowledge base to keep it performant even over long periods of time.

Customize to different preferences

Problem
AI/ML models often struggle to quickly adapt to diverse user requirements and changing definitions of "correct" outcomes due to sparse data availability and time-consuming retraining cycles.
Solution
Orca's unique architecture enables users to build performant models with sparse data and then continually update them as new data comes in. Its closed-loop system ensures that models remain accurate and responsive as definitions of "correct" outcomes change.

Adapt to changing business objectives

Problem
Businesses often change their strategic goals and priorities, which leads to their AI/ML models becoming outdated. Retraining or transfer learning can be inefficient and time-consuming.
Solution
Orca solves this by using a memory-augmented architecture that allows models to update in real-time with external data, enabling instant adaptation to new priorities.

How does Orca's augmentation methodology work?

Orca is an end-to-end system that integrates data management, models, and automated tuning for classification and other predictive systems. Orca teaches these models to leverage external data during fine-tuning, transforming them into "retrieval augmented" systems that adapt instantly to change. By separating reasoning from knowledge, models can be updated by modifying external data rather than retraining. Orca’s closed-loop nature also links model behavior to individual data points, enabling real-time data debugging and refinement, so you can build and update models with even just a few data points.

from datasets import load_dataset
from orcalib.rac import LabeledMemoryset, RACModel

memoryset = LabeledMemoryset("datalicious") #cloud storage backed dynamic model memory dataset

source_dataset = load_dataset("datalicious/datalicious")
memoryset.insert(source_dataset) #works with all standard dataset formats

my_model = RACModel(num_classes=10)
with my_model.use(memoryset):
    my_model.finetune(source_dataset)


my_model.attach(memoryset)
my_model.predict(new_input)
memoryset.analyze() #tell me what could be better about my data

with my_model.use(new_memoryset):
    my_model.predict(new_input)

Blog

Learn more about what Orca is doing and where we’re going.

How Orca Simplifies AI Debugging
2 min read

How Orca Simplifies AI Debugging

Debugging AI systems is far more complex than traditional software. With Orca, companies can transform this time-intensive process into a precise, data-driven workflow that identifies root causes, enables targeted fixes, and ensures continuous improvement without retraining.
How Orca Helps AI Teams Ship Faster
3 min read

How Orca Helps AI Teams Ship Faster

Building and maintaining AI systems is often slow due to messy data and complex processes. Orca simplifies AI development, helping teams work faster and smarter with tools for transparency, immediate updates, and continuous improvement.
How Orca Helps You Customize to Different Use Cases
3 min read

How Orca Helps You Customize to Different Use Cases

When evaluating an ML model's performance, the definition of "correct" can vary greatly across individuals and customers, posing a challenge in managing diverse preferences.
Stop Contorting Your AI App into an LLM
4 minutes

Stop Contorting Your AI App into an LLM

Why converting your discriminative model into an LLM for RAG isn't always worth it.
Building Adaptable AI Systems for a Dynamic World
4 min read

Building Adaptable AI Systems for a Dynamic World

Orca's vision for the future of AI is one where models adapt instantly to changing data and objectives—unlocking real-time agility without the burden of retraining.
Keep Up With Rapidly-Evolving Data Using Orca
1 min read

Keep Up With Rapidly-Evolving Data Using Orca

Orca can help models adapt to rapid data drift without the need for costly retraining using memory augmentation techniques.
Tackling Toxicity: How Orca’s Retrieval Augmented Classifiers Simplify Content Moderation
10 min read

Tackling Toxicity: How Orca’s Retrieval Augmented Classifiers Simplify Content Moderation

Detecting toxicity is challenging due to data imbalances and the trade-off between false positives and false negatives. Retrieval-Augmented Classifiers provide a robust solution for this complex problem.
How Orca Helps Your AI Adapt to Changing Business Objectives
2 min read

How Orca Helps Your AI Adapt to Changing Business Objectives

ML models must be adaptable to remain effective as business problems shift like targeting new customers, products, or goals. Learn how Orca can help.
How Orca Helps You Instantly Expand to New Use Cases
2 min read

How Orca Helps You Instantly Expand to New Use Cases

ML models in production often face unexpected use cases, and adapting to these can provide significant business value, but the challenge is figuring out how to achieve this flexibility.
Orca's Retrieval-Augmented Image Classifier Shows Perfect Robustness Against Data Drift
5 min read

Orca's Retrieval-Augmented Image Classifier Shows Perfect Robustness Against Data Drift

Memory-based updates enable an image classifier to maintain near-perfect accuracy even as data distributions shifted—without the need for costly retraining.
Retrieval-Augmented Text Classifiers Adapt to Changing Conditions in Real-Time
6 min read

Retrieval-Augmented Text Classifiers Adapt to Changing Conditions in Real-Time

Orca’s RAC text classifiers adapt in real-time to changing data, maintaining high accuracy comparable to retraining on a sentiment analysis of airline-related tweets.
Survey: Data Quality and Consistency Are Top Issues for ML Engineers
4 min read

Survey: Data Quality and Consistency Are Top Issues for ML Engineers

Orca's survey of 205 engineers revealed that data challenges remain at the forefront of machine learning model development.

Find out if Orca is right for you

Speak to our research scientists to see if we can help you build more adaptable models.