Simplified, rapid model development
Velocity of AI development and ROI is top of mind for both technical and business leaders. Among the factors limiting velocity are iterative manual debugging & correction, dependence on experts, and cumbersome data management & training. Orca’s new system architecture streamlines, instruments, and automates workflows to address each of these challenges.
Less iterative manual debugging & correction
Conventional models often involve cycles of trial and error “best guesswork” in troubleshooting and/or tuning. Not only are these cycles expensive and high opportunity cost for experts, but also they slow the pace of reaction often to days or weeks. Orca exposes the model’s reasoning in a transparent, deterministic, and auditable workflow which enables confident cause and effect steering. System recommendations with optional cascading & bulk data label changes and automated update options can collapse time to correct down to minutes from hours and days. Instead of frustrating searches for a “needle in a stack of needles,” you can pinpoint the error and resolve it based on actions suggested by Orca.
Less dependence on experts
While Orca’s automation and online learning free experts to focus on higher layer design and tuning, the system also expands access to the broader product community to develop, deploy, and adapt predictive AI. Orca requires smaller datasets than conventional deep learning, is built with pre-trained models, leverages out of the box automated data preparation, and has UI options to supplement programmatic workflows.
These attributes, combined with transparency to see the data used for any inference, enable data literate team members - software engineers, data analysts, product managers - to fine-tune a model for a specific use case simply by tweaking labels, adding new examples or removing unnecessary datapoints from the memoryset. This simplification allows more teams to self-service on their model development needs and alleviates critical bottlenecks for projects.
Less cumbersome data management & training
New models often have a slow start with tedious data labeling and correction; and initial model training can take many iterations with extended test cycles for each. Orca’s Data Co-pilot can help get a system up and running with only a small set of labeled data per class, and automated analytics identify and correct duplication, mislabeling, and distribution density issues. More fundamentally, Orca does not require initial training - the system is pre-trained to mimic the distribution of its attached memory. Just create the memory dataset from your labeled data using the Data Co-Pilot and you are good to go to staging and production.
Orca’s innovative memory-controlled system unlocks solutions to these challenging problems limiting AI velocity - manual processes, scarce expertise, and complex data management & training. The resulting simplification & accelerated cycle times expand capacity and enable the speed for AI the business demands.
Find out if Orca is right for you
Speak to our engineering team to learn how we can help you unlock high performance agentic AI / LLM evaluation, real-time adaptive ML, and accelerated AI operations.