Picture this: You’ve spent weeks fine-tuning your model. Added some new layers, swapped out the optimizer, maybe even bumped the learning rate. The logs look promising—until they don’t. Your performance plateaus. Context understanding is weak. Captions are bland. You tweak

We recently delivered a workflow for an urban-planning platform building real‐time city simulations. Our expert-in-the-loop pipeline converts aerial images into simulation‐ready geospatial layers with centimeter‐accurate roof, road, and vegetation masks. Our workflow also

Heading to Ai4 - Artificial Intelligence Conferences next month? Our CEO Ahmed Rashad Rashad is attending and speaking about how to build robust computer vision data pipelines. His session will discuss the challenges of curating large-scale datasets, how to structure and maintain a strong data

What happens when you teach AI to see the world like a human does? We built the DataSeeds.AI Sample Dataset (DSD) in collaboration with Dataseeds.ai and Brickroad to answer exactly that. ➕7,772 hand-annotated images ➕Peer-ranked for aesthetic quality

“We found a huge gap between what the human experts mentioned in an image, and what the machine learning model could recognize,” Sajjad Abdoli says. Read the full article on IEEE Spectrum: spectrum-ieee-org.cdn.ampproject.org/c/s/spectrum.i… (2/2)

We recently closed a $9 million seed round to further our mission to improve how AI systems are trained and evaluated.

We recently helped a medical AI client create a high accuracy medical scribe. The client is working to deploy autonomous medical agents for medical note generations, which Perle supported in a few ways: ▶️Optimizing RAG-based multi-agent architecture ▶️Completing

Yesterday, our CEO Ahmed Rashad spoke on a panel at Ai4 - Artificial Intelligence Conferences titled “Building Robust Computer Vision Data Pipelines: Data Labeling and Management Strategies” with Arpita Vats, Saurabh Kumar, Vijayraj Gohil, and Teresea O’Neill. The conversation emphasized the importance

Earlier this year, we won ACTAI Global's AI competition that recognizes startups developing practical solutions to some of the world’s most critical problems for our solutions that deliver scalable, efficient results to help businesses "unlock the full potential of their data"

A client recently came to us for help advancing their AI model capabilities with audio data. Our platform provided a workflow to collect and annotate that audio data. We delivered high volume, enriched audio data, ahead of schedule — 14,550 audio samples in total. Each sample

How do you match your AI model to your goal? First, you have to understand what each GPT model is good at. Our founding AI scientist Sajjad Abdoli saw these trends when evaluating these GPT models: • GPT-4o-mini — the calm, calibrated evaluator. It applies steady standards,