Code Intelligence That Actually Understands C++
Let me put on my hard hat and explain: we're building specialized AI for C++ engineering. Not another "just ask GPT" solution—a real system that speaks your language, understands your debugger, and doesn't think std::move is a dance move.
The Specialist Ensemble
Three products, one mission: make C++ development feel less like archaeology and more like the future.
FAISS Extended
We didn't just fork FAISS. We taught it new tricks—sorted inverted lists, TBB parallelism, pluggable storage backends. It's still Facebook's baby, just with better manners.
- 3-5x faster search
- S3/Azure/SSD backends
- TBB parallelism
MLGraph
Turbopuffer showed us the way, but we went off-road. A distributed vector database that runs on your SSDs, not someone else's cloud. Vectors on disk, centroids in memory, latency in microseconds.
- Distributed centroids
- Mirror group replication
- 100M+ vectors
SLM Ensemble
The scaling hypothesis is dead—long live specialists. Our ensemble of 8 models (4B-8B params each, 0.8B-1.6B active), each a virtuoso in its domain, outperforms the 70B generalists at a fraction of the cost. Mamba 3 + Transformers hybrid architecture.
- NVFP4 inference, FP16 training
- 100-200B tokens each
- Muon optimizer
Built Different (Literally)
We didn't start with a whiteboard and "what if." We started with production C++ code, real debugger sessions, and the question: "why is this still so hard?"
Sorted Inverted Lists
Distance-ordered storage enables early termination. Binary search replaces brute force. Your queries return faster because we're not checking vectors that can't possibly win.
State of Truth
Our models don't guess what your code does—they know. gdb integration, rr time-travel debugging, real stack traces. Less hallucination, more debugging.
Tiered Storage
Hot data in memory, warm data on NVMe, cold data on spinning rust. Automatic promotion and demotion. Your vectors flow like water to their natural resting place.
C++ Native
std::vector gets its own token. Template metaprogramming doesn't confuse us. We speak fluent C++23 and tolerate your legacy C++11 with grace.
The Scaling Hypothesis is Dead
Here's the thing about trillion-parameter models: they're expensive, they're slow, and they still think reinterpret_cast is a Harry Potter spell. The industry keeps adding more parameters hoping that intelligence will magically emerge. We respectfully disagree.
Our approach is different. Instead of training one massive model on everything from Shakespeare to StackOverflow, we train specialized models that are experts in exactly one thing: C++. Every parameter pulls its weight. No cognitive budget wasted on Python indentation rules or JavaScript callback hell.
The result? 8 models with 4B-8B parameters each (0.8B-1.6B active via MoE) that outperform 70B generalists on C++ tasks, run on consumer hardware with NVFP4 quantization, and actually understand that std::unique_ptr and std::shared_ptr are fundamentally different philosophies, not interchangeable types.
Ready to Stop Fighting Your Tools?
Whether you're building the next operating system, optimizing game engines, or just trying to understand why that template instantiation takes 47 seconds—we're here to help.