We envision a world where real-time machine learning drives rapid, meaningful decisions that transform industries and empower every data team to innovate without constraints. At Chalk, we are committed to building the foundational AI infrastructure that eliminates complexity and unlocks the true potential of machine intelligence.
Our unified platform streamlines feature and prompt engineering, model evaluation, and inference with unmatched speed and transparency, making advanced AI accessible and maintainable through software engineering principles. By embedding real-time observability and stringent security at every layer, we enable organizations to trust and scale their AI solutions confidently.
Through relentless innovation and a developer-first approach, we are shaping a future where AI-driven insights answer the toughest questions in finance, healthcare, e-commerce, and beyond—empowering clients worldwide to harness the full power of machine learning at the speed of thought.
Our Review
When we first looked at Chalk, we weren't sure the world needed another ML platform. But after diving into what they've built, we get why they raised $50 million at a $500 million valuation just three years after launching. This isn't just another data tool—it's a complete rethink of how AI teams should work.
The "Finally" Moment
What caught our attention is how Chalk tackles the messiest part of machine learning: the endless pipeline of data wrangling, feature engineering, and model deployment. Instead of forcing teams to stitch together five different tools, they've created one platform that handles everything from feature creation to real-time inference.
The kicker? You can define ML features using Python classes instead of wrestling with domain-specific languages. For any data scientist who's lost weeks to pipeline debugging, this feels like a genuine breakthrough.
Speed That Actually Matters
We've seen plenty of platforms promise "real-time" capabilities, but Chalk's Rust-based runtime delivers queries in milliseconds—not the "real-time" that means "faster than yesterday." This isn't academic; companies like Socure and Found are using it for fraud detection and instant loan decisions where every millisecond counts.
The fact that you can deploy this on your own cloud infrastructure while keeping your existing databases is smart positioning. No one wants to migrate their entire data stack just to try a new ML platform.
Who This Really Serves
Chalk isn't trying to be everything to everyone, which we appreciate. They're laser-focused on enterprises building mission-critical AI applications—think fintech fraud detection, healthcare risk assessment, and e-commerce personalization where downtime isn't an option.
If you're a startup experimenting with ML models, this might be overkill. But if you're at the scale where model observability and sub-second inference actually impact your bottom line, Chalk starts looking like exactly what you didn't know you needed.
Features
Programmable feature engine using Python classes
Real-time, low-latency ML inference pipelines
Unified platform for feature engineering, prompt engineering, LLM evaluation, and observability
SDKs for multi-language support (Python, JavaScript, Java)
Deployable on customer cloud infrastructure with existing database integration
Security-certified with SOC-2 Type 2 and ISO/IEC 27001:2022 compliance
FAQs
Ploy was founded in 2024.
Context + infra for agents + models. Built for speed. In your cloud.
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