Introducing Perle: an exceptional, modular AI training data management solution. It’s time to save your AI from itself. Perle can help. If AI could do one thing perfectly for you, what would it be? We might just make it happen. #HelloPerle

Super excited to announce our Pre-Seed round with a lot of great investors. The team has made a lot of progress over the last few months. More updates coming soon!!!

Yesterday, our CEO @AhmedZRashad spoke on a panel at @Ai4Conferences 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 of data labeling, management, and strategy and discussed the challenges of curating large-scale datasets, ensuring high-quality labeled data, and implementing efficient data workflows to support model training and optimization. Thanks to everyone who attended!

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

Earlier this year, we won @ACTAIglobal'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" and streamline workflows. Our CEO, @AhmedZRashad, said: “We are incredibly honoured to be recognised by ACTAI Global. This event is more than just a competition; it's a convergence of brilliant minds coming together to create meaningful change. Being part of this network is a game-changer for us, and we're eager to leverage AI to drive real-world impact on sustainability and economic growth. Congratulations to the whole Perle team! Read more: abs-cbn.com/news/technology/…

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 human-in-the-loop benchmarking ▶️Assisting with multilingual review and QA The results? 
 🔶Improved the generative stack by tightening the quality of context and coordination across agents 🔶Co-developed a benchmark framework grounded in real-world edge cases leveraging the pool of medical experts 🔶Supported QA and annotation in Arabic, Spanish, and other languages Learn more about Perle’s AI solutions: perle.ai/solutions

We’ve said it before: your AI model is only as good as its data. And accurate, high-quality data requires human expertise. In fact, in our recent whitepaper, we saw a 24% benchmark accuracy lift achieved under human QA. Our AI data services have human QA — Perle provides high-quality, multi-modal data collection tailored to your AI model’s needs. See how our data collection solution works across text, image, audio, and video.

Our founding AI scientist @sajjad_abdoli was quoted in @IEEESpectrum's article on data labeling and the importance of human experts-in-the-loop. He discussed Perle’s recent task: Working with a customer on a model to label images. (1/2)

Your data needs context. To effectively use your data in an AI model, it must be annotated and enriched, preferably with human oversight. Perle’s precise, expert-led annotation solutions effectively label text, images, video, and audio, so your team can ship better models faster.

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, checks every box, and keeps judgment consistent across criteria. • GPT-4o — the sharp quality controller. It’s especially good at spotting what’s off while still keeping a balanced view of the whole. • GPT-5 — the ultra-cautious gatekeeper. It’s very tough on anything that smells like a hallucination, but its standards can swing from case to case. TL;DR: Model choice should match your goal, whether it’s consistency, error-hunting, or maximum caution.

The next generation of AI won’t be trained on the internet. It will be trained in our kitchens, living rooms, and workplaces. For years, annotated data powered the breakthroughs that gave us GPT, BERT, and autonomous driving. But static annotations have reached their limit. The next frontier is observational data for embodied AI—teaching robots not just to see, but to act like us. Think smart homes where annotators record daily routines, or wearable tech capturing real-world tasks. These datasets will fuel the rise of domestic robots that can fold laundry, load dishwashers, and anticipate human needs. The opportunity is massive. But the challenge for data labeling companies lies in positioning themselves as infrastructure enablers for the robotic economy. Read our latest blog by Moe Abdelfattah to learn more: perle.ai/resources/the-evolu…

“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 @IEEESpectrum: spectrum-ieee-org.cdn.amppro… (2/2)

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 — all 14,550 of them — was enriched with additional metadata: ✔️Two levels of category annotation ✔️Distance from the source ✔️Recording environment Want to create your own AI workflow? Learn more about Perle’s AI solutions: perle.ai/solutions

A week after publication, the DataSeeds.AI Sample Dataset (DSD) we created in partnership with @imagedatasets and @TryBrickroad is the 4th most trending dataset on Hugging Face — out of 424,480 total datasets. It’s been downloaded 1,260 times! We’re excited to see how quickly it’s sparked interest and been downloaded and used. In case you missed it: Last week we launched the DataSeeds.AI Sample Dataset (DSD), an open source and optimized dataset for fine-tuning and evaluating multimodal vision-language models, especially in scene description and stylistic comprehension tasks. Learn more and download it for yourself here: huggingface.co/datasets/Data…

If your model sees a bee and a flower as one blob in a rectangle, it’s not ready for the real world. The DataSeeds.AI Sample Dataset (DSD), created with our partners DataSeeds.AI, and Brickroad, uses full semantic segmentation, not just bounding boxes to capture detail, context, and occlusion like a human would. That’s what we mean by “expert-in-the-loop.” Learn how DSD sets a new benchmark: perle.ai/white-paper

With the launch of GPT-5, our founding AI scientist @sajjad_abdoli put together his thoughts on the 3 GPTs, what they’re best for, and how to best evaluate AI models. (1/2)

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 @imagedatasets and @TryBrickroad to answer exactly that. ➕7,772 hand-annotated images ➕Peer-ranked for aesthetic quality ➕Layered with semantic, stylistic, and technical metadata It’s a dataset born from human judgment. Why does this matter? Because AI is evolving. We’re moving beyond object detection and bounding boxes. The frontier now is: 🔸 Multimodal learning 🔸 Scene interpretation 🔸 Generative modeling 🔸 Compositional reasoning DSD introduces a new standard. It’s what we call expert-in-the-loop annotation at scale—a process that combines scalable machine assistance with structured, human-generated insight. Our takeaway? This is about rethinking AI from the lens of human perception. When you train machines to interpret the world the way we do—through aesthetics, emotion, light, and structure—they don’t just get more accurate. They get more capable. That’s what Perle is here to enable. 🔗 Download the white paper to learn more: perle.ai/white-paper

Hello Folks - been hearing about a kiva token that launched and they replicated our website and claiming to be us. This is a SCAM!!! We did not launch a token yet our website is KivaAi.com. We will make public announcement here prior to our token launch

We’re entering a new era of data labeling—one where models don’t just see, they observe and behave. At Perle, we believe the next leap in AI will come from observational, human-centric data: multi-modal recordings of how people act, make decisions, and move in real environments. Think real homes, first-person cameras, rich sensor data. It’s the kind of data that lets embodied AI actually imitate human behavior, not just recognize it. Why this matters: ➕Robots in healthcare, biotech, and the smart industry can’t just be accurate; they must understand context and nuance. ➕Traditional annotated datasets are hitting diminishing returns for embodied AI. ➕Capturing rich, real-world behavior enables models that generalize better, adapt faster, and are safer. Read our latest blog by Moe Abdelfattah, Head of Product Operations, for more: perle.ai/resources/the-evolu…

While AI/ML models continue to evolve, there is a general lack of data—let alone quality data—for effective model training. Why? 1️⃣The absence of robust licensing models and technologies around data sharing. 2️⃣The difficulty of creating metadata—high-quality data annotation is a challenging task. When creating a vision language model recently, we addressed this problem by switching from bounding boxes to masks and adding rich contextual metadata. For each image, we included a title (if one was missing), a description of the image and its objects, and a scene description—covering angles, mood, and any additional contextual details. This approach improved object definition and enabled us to segment the dataset more effectively and train models that delivered better results. And yes, we used human annotators. Read more: perle.ai/resources/why-quali…

Heading to @Ai4Conferences next month? Our CEO @AhmedZRashad 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 pipeline, and best practices for handling edge cases and imbalanced datasets. Going to be there? Let's connect! Get in touch: hello@perle.ai

We recently worked with a healthcare AI company developing autonomous medical agents for note generation. Their goal was to reduce clinician documentation workload and improve record accuracy. Perle helped strengthen the model architecture, built a benchmark framework with medical experts to capture real-world edge cases, and supported multilingual quality assurance in Arabic, Spanish, and other languages. The outcome was a more reliable generative stack, clinically relevant evaluation, and broader accessibility for diverse patient populations bringing their medical AI closer to safe, scalable deployment. Learn more in our full case study: perle.ai/resources/case-stud…

Great conversation with a group of the best builders around linkedin.com/posts/coinfund_… @jbrukh @coinfund @gensynai @Pluralis__ @PrimeIntellect @BagelOpenAI

Can GenAI understand photos? If you have the right model. We built a vision language model that we tested on the Dataseeds.ai Sample Dataset. We labeled 20,000 images with rich annotations, including semantic object segmentation (via detailed masks) and comprehensive textual scene descriptions. The results? A fine-tuned VLM model that understands scenes and better captures photographic elements. And our data helps vision language models perform 70% better than Amazon Rekognition. Learn more about our solutions: perle.ai/solutions

Exceptional AI is (almost) here.

🚀 KIVA AI wins big! 🏆 Honored to be a winner of the ACTAI AI TECH Competitions 2025 Finals! Thanks to @ACTAIglobal for the recognition & to our team for their dedication. AI is evolving fast, and we’re just getting started! #AI #ACTAI #Innovation #KivaAI

Want better AI performance? Try fewer labels and more humans. At Perle, we purposely put humans in the loop. Because when you understand how people perceive scenes, you can train models that actually comprehend. Learn how we structured DSD with our partners DataSeeds.AI and Brickroad: perle.ai/white-paper

AI is shifting towards a more data-centric approach. But, the widespread adoption of data-centric AI model training is blocked by extraneous circumstances. That means most AI ecosystems continue to rely on data that suffers from crowd-sourced labels, low annotation fidelity, and limited task-specific utility. This isn’t data that is suitable for multimodal reasoning, fine-grained visual analysis, and generative AI. Perle, @imagedatasets, and @TryBrickroad partnered to address that issue and produce a new foundational dataset: the DataSeeds.AI Sample Dataset (DSD) The DSD introduces: ➕Structured human judgement ➕Dense annotation ➕High-aesthetic quality Which paves the way for precision AI. Download the white paper: perle.ai/white-paper

Too many AI initiatives start with bold ambitions then derail in weeks as requirements shift, edge cases pile up, and teams scramble to catch up. In annotation-heavy projects, scope creep is especially insidious. The real costs of loose scoping: ❌ Vague business objectives that leave teams guessing what “success” means ❌ Cross-functional misalignment where data, engineering, and product diverge on priorities ❌ Endless loops: more labels, new categories, rework over rework ❌ Undefined technical specs forcing teams down expensive trial-and-error paths At Perle, we believe clarity from day one is non-negotiable. We’ve built tools and processes to: ✅ Translate big-picture goals into precise, executable data specs ✅ Align data scientists, engineers, and product owners on the same scope ✅ Minimize rework by locking down requirements early If your AI project feels like it’s chasing its tail instead of advancing, scope creep is probably the culprit. Learn more: perle.ai/resources/the-hidde…

Smarter AI starts with smarter evaluation. Perle’s fast, expert-driven insights improve and validate your AI models. We evaluate models based on what really matters: ✅ Fast, expert-in-the-loop assessments ✅ Side-by-side model testing and A/B experiments ✅ Flexible workflows to track accuracy, recall, and key metrics ✅ Expert reviews for bias, safety, and compliance

Everyone’s racing to build better AI models. But here’s the real unlock ➡️ better data. Our CEO @AhmedZRashad sat down with NYSE Wired x @SiliconANGLE & @theCUBE to talk about why human-in-the-loop, scalable data is the key to building AI that’s not just powerful, but safe and trustworthy. Think healthcare. Law. Fields where mistakes aren't an option. From leading Scale AI’s $14B data operations to winning ACTAI Global’s APAC AI Startup Competition, Ahmed and the Perle team are building the foundation for the next era of AI. linkedin.com/posts/actai-ven… #AI #HumanInTheLoop #DataInfrastructure #FutureOfAI #PerleAI

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 included: ▶️ Sub‐pixel polygon segmentation of roofs, roads, and vegetation across 10,000+ drone images ▶️ Automatic spatial‐layout analytics generate density, zoning, and setback metrics in GeoJSON / CityGML ▶️Fine‐tuned model on edge detection, object boundaries, and class granularity The result? Outputs that outperformed benchmarks and power real‐time traffic, utility, and green‐space simulations. Learn more about Perle’s AI solutions: perle.ai/solutions

Most AI models fail because of their data. When annotation is handled by generalists, you end up with: ❌ Higher iteration costs ❌ Missed edge cases ❌ Slower model performance gains The fix? AI scientists. They bring the domain knowledge, technical context, and precision that transform annotations from “just labels” into the foundation of high-performing, trustworthy models. At Perle, we don’t just label data. We use experts annotators to ensure high-quality data. Learn more: perle.ai/resources/revolutio…

It's our 1st birthday! 🎂 Here’s to another year of great AI, new innovations, and more Perles of wisdom.

We developed a multi-tier human-in-the-loop annotation pipeline for the DSD, a foundational data set created with our partners @imagedatasets, and @TryBrickroad . We integrated structured human input with scalable machine assistance for a dataset that balances annotation quality and efficiency. Each DSD image is annotated with: ➕Pixel-level segmentation masks ➕Three tiers of human-generated text with: ◾A concise title ◾A 15+ word image description ◾A 20–30 word technical scene analysis This approach delivers compositional insight, photographic context, and aesthetic interpretation providing critical data for training models on human-like reasoning tasks. Learn more about DSD in our white paper: perle.ai/white-paper

This week in AI, we’re seeing a theme: AI is facing complex challenges that it needs to overcome in order to advance and widely integrate across industries. Here at the highlights: ➡️The NIST published comprehensive guidelines on securing AI systems, providing a critical framework for protecting against emerging technological vulnerabilities. ➡️Over 20 new AI certification programs launched to try to address the growing skills gap. ➡️ The U.S. is developing a multi-faceted AI Action Plan focusing on export controls, infrastructure, regulatory frameworks, and ethical development. At Perle, we're watching this evolution — and actively shaping it. Our solutions are designed to align with regulatory standards, empower AI development, and create resilient data sets. Learn more about Perle’s solutions and get more AI news on our blog: perle.ai/resources/this-week…

So we can deliver pearls of wisdom.

Generic human oversight doesn’t cut it in AI. In high-stakes domains like healthcare, law, and finance, you need experts-in-the-loop. Here’s why expert guidance matters: 👉 Well-annotated data separates a great model from a mediocre one 👉 Experts catch edge cases that generalists miss 👉 Accurate labels reduce iteration cycles and compliance risk At Perle, we make sure your data pipelines are guided by domain specialists. Read more: perle.ai/resources/experts-i…

Perle and AI Circle co-sponsored an insightful panel discussion on the intersection of human intelligence and AI, featuring Kaushik (Former Head of ML Data Operations at Google), Danielle (Cognitive Scientist at Amazon AGI), and @gregschoeninger (CEO of Oxen.AI). Moderated by @AhmedZRashad , CEO of Perle, the evening sparked engaging discussions on the future of AI. This is just the beginning of many more Perle and AI Circle events to come—stay tuned for future discussions! Thanks to everyone who joined us and our incredible panelists for a truly enlightening conversation!

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 again, with mid-tier results. The issue? It’s not your model. It’s your data. The next real leap in AI performance is high-quality, human-aligned data. We partnered with @imagedatasets .AI and @TryBrickroad to create the DataSeeds.AI Sample Dataset (DSD). DSD is a curated, expert-annotated, and peer-ranked dataset built for today’s multimodal AI systems. And our findings prove that the exceptional visual fidelity of the DSD is essential for models seeking to become capable of professional-level scene analysis. Download the white paper: perle.ai/white-paper

We can’t predict the future, but our founding AI scientist Sajjad Abdoli believes LLM-assisted coding will evolve to focus on: ✔️Improved security guarantees: Developing formal verification methods for LLM-generated code ✔️Specialized Domain Adaptation: Creating LLMs optimized for specific programming languages or development contexts ✔️Personalized Learning Curves: Adapting to individual developers' skills and preferences over time ✔️Transparent Reasoning: Providing clear explanations for code design decisions and security considerations Read more about how to safely integrate LLMs into your code and why experts-in-the-loop are so important: perle.ai/resources/expert-in…

Will we see you at HumanX in Las Vegas next week? 👋 Our very own @Nate_Castro will be in attendance. Come find us! To schedule time to meet, please email hello@perle.ai or request a demo on our website ➡️ perle.ai

What’s the #1 reason AI projects fail?

Not all GPT judges think alike. In our vision-language evals, each model showed a distinct “personality”: • GPT-4o-mini — the calm, calibrated evaluator. It applies steady standards, checks every box, and keeps judgment consistent across criteria. Great when you need reliable, systematic coverage. • GPT-4o — the sharp quality controller. It’s especially good at spotting what’s off while still keeping a balanced view of the whole. Reach for this when error-hunting matters most. • GPT-5 — the ultra-cautious gatekeeper. It’s very tough on anything that smells like a hallucination, but its standards can swing from case to case. Use when maximum caution beats breadth. Across the board, these judges lean more toward catching mistakes than celebrating correct details—so model choice should match your goal: consistency, error-hunting, or maximum caution. (2/2)

Learn more about our data collection solutions: perle.ai/solutions

We partnered with DataSeeds.AI (@imagedatasets) and @TryBrickroad to publish an open-source dataset and research paper, highlighting how expert-in-the-loop human annotation and high-aesthetic quality can pave the way for precision AI and bridge the gap between perception and machine understanding. The dataset, called the DataSeeds Sample Dataset (DSD), demonstrates how peer-ranked, human-annotated data outperforms commonly used datasets and traditional tagging APIs in precision tasks, especially for technical scene understanding and aesthetic modeling. While developing the DSD — and to validate and benchmark its effectiveness — we published our findings in a comprehensive research paper, as well as the weights and code. Read the full research paper: arxiv.org/abs/2506.05673 #dataset #AI

Where do you see the biggest ROI from investing in data quality?

Couldn't make it to HumanX? @Nate_Castro, on our Growth team, recently went to the event and recapped his key takeaways. In short: AI’s future isn’t about bigger models, but better data. Read more: perle.ai/resources/reflectio…

The DataSeeds.AI Sample Dataset (DSD) is here. We worked with @imagedatasets and @trybrickroad on this high-fidelity, human-curated computer vision-ready dataset comprised of:

 ▶️7,772 peer-ranked, fully annotated photographic images ▶️350,000+ words of descriptive text, and comprehensive metadata Each image includes multi-tier human annotations and semantic segmentation masks. The DSD is open source and optimized for fine-tuning and evaluating multimodal vision-language models, especially in scene description and stylistic comprehension tasks. Get the DSD on Huggingface: huggingface.co/datasets/Data…

Do AI agents work well in domains like finance, healthcare, and law? 

 In short, yes. And we’ve seen a variety of examples in the market: ▪️Finance & Banking: AI analyzes loan applicant data, enabling lenders to make more accurate decisions. ▪️Legal: AI processes vast amounts of legal data and provides relevant insights to assist lawyers. ▪️Medical: Expert-annotated AI models support tasks like tumor identification, surgical phase detection, and more — helping surgeons and healthcare professionals deliver better outcomes. What other examples come to mind? Drop them in the comments below 👇 Learn more about AI agents in critical applications from our founding AI scientist Sajjad Abdoli: perle.ai/resources/the-unsun…

🔎How clear are your AI project requirements? They could be the cause of delays in your AI model development. Perle’s AI-powered assistant (coming soon!) solves this challenge. It translates your business and research goals into precise model-training specifications. Don’t waste time on efforts not in scope. Learn more about Perle’s AI solutions: perle.ai/solutions

Going to be at Ai4 in Las Vegas next month? So will we! Our CEO Ahmed Rashad will also be speaking about how to build robust computer vision data pipelines. We’d love to connect at the conference! Get in touch: hello@perle.ai

LLMs are rapidly transforming software development workflows — anyone can produce functional code from natural language descriptions, suggest optimizations for existing implementations, and identify potential bugs or performance issues. In theory, this sounds great. It reduces the barrier to entry for coding and frees up developers to focus on other tasks. But, there’s a downside. LLMs can introduce significant errors and vulnerabilities into its code, leaving companies open to injection attacks, memory leaks, authorization bypasses, and more. What’s the solution? LLMs that work in tandem with humans. This expert-in-the-loop ensures the safe and effective integration of LLMs in code development, producing high-quality code while minimizing the risk of errors and vulnerabilities. Learn more about this approach from our founding AI scientist Sajjad Abdoli: perle.ai/resources/expert-in…

Don’t get stuck in a rigid workflow. Our flexible platform adapts to any AI training data so you can: ✔️ Build a comprehensive dataset from scratch for a new model ✔️ Collect and annotate data for new domains or scenarios ✔️ Enhance your model's performance through better data quality and evaluation Learn more: perle.ai/solutions

We're at #HumanX in Las Vegas this week! Want to chat about great AI and AI data labelling? Let's meet. To schedule time, email hello@perle.ai or request a demo on our website ➡️ perle.ai/

100%. It’s highly specialized work that requires specialized humans providing inputs in a controlled environments (talking about the high end of the market where most of the demand and will be)

How can you leverage AI in the legal field? We used AI and expert validation to quickly refine workflows and improve data quality for a specialized legal provider. Here’s how we did it: We collected and labeled 30K commercial law contracts in Saudi Arabia. Then, we used experts to extract key values, tag every clause, and classify contracts properly. The new data allowed the model to effectively extract insights, compare contracts, and get suggestions on contract improvements. Plus, the project was completed in 4 weeks with a 99%+ acceptance rate on all labeled contracts. Learn more about Perle’s AI solutions: perle.ai/solutions

Catch us at @Ai4Conferences this August in Las Vegas. Our CEO @AhmedZRashad will be speaking about how to build robust computer vision data pipelines. His session will include: ▶️Challenges of curating large-scale datasets ensuring high-quality labeled data, and implementing efficient data workflows to support model training and optimization. ▶️How to structure and maintain a strong data pipeline that supports continuous learning and improvement in computer vision models ▶️Best practices for handling edge cases and imbalanced datasets. Going to be there? Let's connect! Get in touch: hello@perle.ai

AI in operating rooms: How can it be used? Dr. Jeremy Wano, MBA, MPA, our Head of Business Operations dives into AI-driven surgical diagnostics, how to improve accuracy, why experts are the key to success, and more. Read his insights here: perle.ai/resources/raising-t…

Learn more about our custom annotation solutions: perle.ai/solutions

Is your AI development slow, and you don’t know why? It could be because you don’t have clear project requirements. A lack of clear, actionable requirements at the start of a project can lead to slow progress and unnecessary bottlenecks. At Perle, we’re working on a solution to this challenge: an AI-powered assistant that translates your business and research goals into precise model-training specifications, saving you time and effort. Learn more and get in touch: perle.ai/solutions

Why is human annotation and expertise vital to AI? Because they’re the foundation of vertical AI agents—specialized AI systems tailored for specific industries. These AI agents are built with a deep understanding of certain industries, like law, allowing them to deliver insights within that context. In short, vertical AI agents are at the forefront of solving niche problems that demand precise and reliable solutions. In our latest blog post you’ll learn: 
 * The role of human annotation * The importance of domain knowledge * How to create strong AI agents for critical applications 
Read it here: perle.ai/resources/the-unsun…

Which part of the ML lifecycle deserves better tooling?

Josh Halliday @LLMenjoyerUK reveals the real impact of scope creep in AI annotation projects: 👉Ambiguous business objectives 👉Cross-functional misalignment 👉Endless iteration cycle 👉Undefined technical parameters Perle’s solution — launching soon — uses AI to translate business objectives into precise, executable model-training specifications. That way, you keep your scope in scope — and have a successful AI model. Read more on the blog: perle.ai/resources/the-hidde…

Want to stay ahead of the ever-evolving AI curve? Here are 5 AI training data trends we’re seeing right now: 1️⃣ Domain-specific labeling 2️⃣ Reinforcement Learning with Human Feedback (RLHF) 3️⃣ Custom annotation tools 4️⃣ Industry-specific data labeling 5️⃣ Synthetic data in AI training Looking to streamline AI data annotation? Don’t collect more data. Curate the right data with the right tools. Learn more on Perle’s blog in this article by @LLMenjoyerUK: perle.ai/resources/2025-ai-t…

In AI, good labels aren't just a starting point. They're a competitive edge. And in high-stakes use cases like healthcare, getting it right is crucial. A new dataset paper that looked at clinical trial outcomes outlines a feedback loop where LLMs actively revised, aligned, and improved labels based on a trusted human-verified subset. In our latest blog, we discuss what the dataset got right, why labeling can’t be considered a background task, a new era of dataset design, and more. Read it here: perle.ai/resources/better-la…

Ready to see the brilliance?

AI has a data problem. There is a scarcity of data, but also legal, quality, and network completeness issues. What’s the solution? Data licensing. Our SVP, AI Strategy Fabian Schonholz, Ph.D dives into how data licensing could impact legal, quality, network completeness, and data availability issues in our latest blog. Read it here: perle.ai/resources/the-impor…

✔️ Improve the quality and consistency of your existing datasets ✔️ Measure how your model performs using structured evaluation and expert feedback ✔️ Build scalable processes for ongoing data collection and annotation Learn more: perle.ai/solutions

🚨 Humans outperform AI in classification tasks by 77% according to a new study from @Microsoft. 🔑 The takeaway? Domain expertise matters. Humans > Generic AI. LLMs trained on noisy, low-quality annotations will never be as sharp as those trained on real, nuanced human knowledge. AI that actually works starts with better data. Perle optimizes data quality with precise, expert-driven feedback, so your AI gets smarter, faster. Let’s talk: perle.ai

Your data is only as good as the people who validate it. Don’t believe us? You can improve accuracy by up to 25% for complex AI models when you use experts.

We’re entering the age of data-aware AI. From cohort-specific labeling to smart dataset distillation, it’s clear: the next frontier isn’t just smarter models—it’s smarter data. That’s what our founding AI scientist Sajjad Abdoli overall takeaway was from this year’s #ICASSP2025. But that’s not all he took away from the event. On our blog, he dives into a few of the most compelling ICASSP papers and discusses how they intersect with the growing need for smarter, expert-driven data infrastructure in AI. Read his thoughts: perle.ai/resources/title-ica…

Legal document analysis is one of the most challenging yet crucial applications for AI. But legal documents written in Arabic present an entirely new challenge for LLM-based AI models due to the language’s rich linguistic features, right-to-left script, and regional dialects. These complexities make it difficult for generic AI models to effectively process contracts, assess compliance risks, or accurately extract legal insights. Not to mention, LLMs trained on English datasets can’t just be adapted as-is for use with languages like Arabic. Why? Unnatural text structure: The LLM may generate Arabic content with awkward sentence structures that don't reflect natural Arabic writing patterns. Poor inter-sentence coherence: Generated Arabic text by the model may lack proper connective elements between sentences, resulting in disjointed content that doesn't flow naturally. Translation artifacts: The Arabic outputs often read like direct translations from English rather than authentic Arabic, suggesting limitations in the model's understanding of language-specific expression. Safety calibration requirements: Initial testing revealed the original model produced harmful content in Arabic at high rates (89-90%), requiring specific mitigation strategies to bring this down to safer levels. Dataset representation imbalance: Arabic, like many non-English languages, suffers from underrepresentation in training data, with research showing that 73% of popular instruction datasets remain primarily English-focused. What’s the solution? Where do you go from here? Our founding AI scientist Sajjad Abdoli explains: perle.ai/resources/breaking-…

💡 Kiva AI had the exciting opportunity to pitch at an exclusive event alongside 35 MIT alumni-founded startups, presenting to 433+ VCs and corporations. Big things ahead for Kiva AI! 🚀 hubs.ly/Q032z3hR0 #startups #venturecapital #KivaAI #mit #founders #plugandplay #siliconvalley

Domain expert annotation is critical for Llama-level models. Why? Our founding AI scientist Sajjad Abdoli explains: 1. Enhanced multimodal understanding Domain experts can provide annotations that capture the nuanced relationships between these modalities (text and vision) in ways that generalist annotators cannot. 2. Improved reasoning and problem-solving The Llama 4 Behemoth model has exceptional mathematical reasoning. Training this type of model requires annotations from individuals who understand mathematical principles and problem-solving approaches. 3. Specialized knowledge domains Domain expert annotators can provide the high-quality labeled data needed to train specialized domains. 4. Long-context understanding Annotating long-context applications requires annotators who can maintain consistent understanding across extended content. Dive deeper into Sajjad’s insights on our blog: perle.ai/resources/beyond-th…

Will we see you at AIM 2025 next week? Our CEO and Founder @AhmedZRashad will be speaking about “Controllable AI Through Peer-Ranked Data” and discussing how: ➕Vision-language models are limited by data quality ➕Bad training data is the root cause of AI limitations ➕Human-centered data solves the quality gap Going to be there? We’d love to chat! Get in touch via our website: perle.ai/ #AIM2025

Josh Halliday @LLMenjoyerUK discusses this week in AI. A few highlights: 1️⃣ Spain just passed strict AI labeling regulations—with €35M fines for non-compliance. The message is clear: AI transparency isn’t optional.

We’ll be at AI4 this August in Las Vegas with our CEO Ahmed Rashad who will be giving a talk about how to build robust computer vision data pipelines. Going to be there? We’d love to connect! Get in touch: hello@perle.ai

From early prototypes to production systems, we help you evaluate AI with confidence. See how it works: perle.ai/solutions

Our CEO and Founder Ahmed will be speaking at AIM 2025 in June! The two day conference will cover all things AI and ML including: ➕AI algorithms ➕Data mining ➕Human computer interaction ➕AI in medicine And more! Stay tuned for more info on Ahmed’s panel. Going to be at #AIM2025? Let us know in the comments or get in touch via our website: perle.ai/

Apple’s recent paper “The Illusion of Thinking” confirms something we've long observed: language models are brilliant pattern matchers, but reasoning under abstraction remains a fundamentally different beast. Our AI Engineer Rudi Cilibrasi breaks down Apple’s latest research and explains why the next leap in AI depends on grounding, not just more scale. The biggest takeaway? As LLMs continue to impress with surface-level fluency, it’s important to recognize their limits and complement them with systems better suited to perception and abstraction. Read Rubi’s full breakdown on the blog: perle.ai/resources/apple-exp…

Do you use LLMs to generate code? If so, you’re not alone. @GitHub reported that 46% of code on their platform now comes from GitHub Copilot. But, security researchers have documented cases where LLM-generated code contained subtle flaws that could lead to injection attacks, memory leaks, authorization bypasses, and other serious security issues. How can you protect against this? Careful implementation, rigorous evaluation frameworks, and continuous human oversight throughout the development process. Sajjad Abdoli, Founding AI Scientist, explains more on our blog: perle.ai/resources/expert-in…

🧬 AlphaFold has ushered in a new era of biology, giving researchers instant access to protein structures and turning years of analysis into seconds. It's transforming drug discovery, vaccine development, and disease fighting. AI is proving to be a game-changer in biology, addressing global challenges. Excited to see how this revolutionary tool will continue to shape the future of science!

We’re hosting a happy hour in London! Join us and other industry experts for lively conversations, valuable networking, and great food and drinks. Together, we’ll explore topics like: 👉The evolving role of human data in AI 👉The difference between good and great AI 👉The future of AI Details here: Thursday, May 22 6-11pm The Parcel Yard, King Cross, London RSVP today: lu.ma/kyz2xrjc

Live in London and want to meet other AI industry experts for lively conversations, valuable networking, and great food and drinks? Join us for Perle Happy Hour: In the Loop – The Future of Human Data! Details here 👇 Thursday, May 22 6-11 pm The Parcel Yard, King Cross, London We’ll explore topics like: 👉 The evolving role of human data in AI 👉 The difference between good and great AI 👉 The role of human wisdom in machine learning RSVP today: lu.ma/kyz2xrjc

AI/ML models need quality data. But what is quality data? When our SVP, AI Strategy Fabian Schonholz talks about quality data, he means data that is cleaned, de-duplicated, network-complete, validated, and enriched with metadata that provides the deeper context necessary for effective model training. The problem is the lack of data available, not to mention quality data. On our blog, read his thoughts on: ▶️ The reason for the scarcity ▶️ How Perle addresses the problem ▶️ The role of 'humans in the loop' perle.ai/resources/why-quali…

Exciting times, and there’s progress.

Sure, LLM-assisted coding sounds great (and it can be). But before you implement LLMs into your code development consider these key security risks: ▪️Prompt injection: LLMs can misinterpret user input, potentially leading to system intrusions or security breaches when malicious instructions are embedded. ▪️Malicious code execution: Models may enable execution of harmful code, especially when connected to code interpreters. ▪️Vulnerability to cyber attacks: Advanced LLMs can be tricked into using their knowledge to assist with cyber attacks through carefully crafted prompts that exploit the model's helpful nature. ▪️Insecure code generation: LLMs frequently suggest unsafe code patterns that can be exploited. Our founding AI scientist Sajjad Abdoli shares how to ensure safe LLM integration: perle.ai/resources/expert-in…

Appreciate it, more to come.

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MLLM’s (mostly) can’t spot a chart with misleading visualizations. So, what’s the solution? It’s two fold: 1️⃣A shift toward integrating deeper, domain-specific human expertise into the training process. That means bringing experts like designers, data analysts, and subject matter specialists into the AI training loop to provide feedback on why a chart might be deceptive. 2️⃣The need for continuous feedback and iterative evaluation in AI models. These models should evolve as experts provide feedback, which requires ongoing collaboration between AI researchers and human annotators with deep contextual knowledge. Read more on our blog: perle.ai/resources/can-ai-sp…

Thanks! We believe great things are ahead too.

We’re building an AI system for Arabic language legal document understanding. Why? Because legal document analysis is one of the most crucial applications for AI. As we build this tool, here’s what we’re taking into consideration: 👉Native experts for benchmarking: We’re assembling a diverse team of native Arabic-speaking professionals. This team operates within a multi-tiered review framework that combines legal and linguistic expertise to ensure comprehensive evaluation. 👉Legal documents dataset: We’re prioritizing building a balanced corpus that represents the diversity of Arabic-speaking legal systems, with rigorous quality control processes ensuring both linguistic accuracy and legal authenticity. 👉Rigorous benchmarking assessment criteria for QA system based on: ▪️Formatting: Evaluation of text layout, paragraph structure, and proper handling of Arabic-specific formatting requirements ▪️Spelling and grammar: Assessment of linguistic accuracy including proper use of diacritics, case endings, and legal terminology ▪️Instruction following: Measurement of the system's ability to adhere to specific query parameters and legal context ▪️Verbosity: Evaluation of response length appropriateness, balancing comprehensiveness with conciseness ▪️Truthfulness: Verification of factual accuracy and legal correctness of generated responses ▪️Missing parts: Identification of critical information omissions in system outputs ▪️Overall quality: Holistic assessment of usefulness, relevance, and practical applicability in legal workflows 👉Examining multiple AI models for the best foundational model selection: Our model selection process involves rigorous comparative analysis of leading LLMs including GPT models, Llama, and Aya. We’re evaluating their respective strengths in handling Arabic legal content across diverse document types. We are also considering building our foundational model using the dataset that we are collecting to adapt the models according to legal terminology. Want to learn more? Our founding AI scientist Sajjad Abdoli breaks it down: perle.ai/resources/breaking-…

If your AI model requires deep domain expertise, why are you relying on generalist annotators? Don’t settle for higher iteration costs, low-quality data, unreliable models, and limited scalability. Learn more about: ✅ Why traditional annotation doesn’t work ✅ Why STEM experts are the key to high-quality AI training data ✅ The future of AI annotation work Read the blog: perle.ai/resources/the-power…

🚨 Heads-Up 🚨 We’ve spotted a fake account impersonating Kiva AI: hubs.la/Q031gGg20. We want to let you know this is not associated with us. #FakeAccount

BOXWRENCH, a new benchmark that pushes weak supervision to its limits, was recently introduced in a benchmark study. What came out of the study was striking: Models trained entirely using weak supervision rivaled those trained on fully hand-labeled data in object detection tasks. However, the benchmark makes clear that weak supervision alone isn't always enough. Our takeaway? Weak supervision scales well, but precision still requires a human touch. Read more on this topic and get our insights into how to augment weak supervision: perle.ai/resources/weak-supe…

The DataSeeds.AI Sample Dataset (DSD), created with our partners DataSeeds.AI and @TryBrickroad, put Amazon Rekognition to the test. Spoiler alert: It underperformed notably in compositional and stylistic categories. Compared to DSD’s expert-in-the-loop annotations, commercial systems struggled with detail, depth, and aesthetics. The takeaway? General-purpose systems have real limitations for nuanced visual tasks. Learn more in our whitepaper: perle.ai/white-paper