Build fast agents,
faster than ever.
Fine-tune with control
Drag, drop, train. Go from idea to optimised model without writing a single line of code.
Zero-code Fine-tuning
Build complete training pipelines visually. Connect agents, rewards, and datasets, code-free.
Research-grade Methods
Implement state-of-the-art research in minutes.
Any Model, Any Size
Swap between open-weight models in one click - Llama, Qwen, Gemma, Mistral, or bring your own.
One-click Cloud Training
Hit train and we handle the GPUs, checkpoints, and monitoring - no code, no boilerplate.
Why PBJ
pbj learns with you
As your platform scales, so does pbj's knowledge about how to make you more efficient, enabling continuously optimized compilation across the stack.
You create agent flows by hand or with our AI copilot. Our diagrammatic representation lets pbj automatically optimize your flows, offering suggestions with annotated production performance to discover the most efficient flows for your use case.
The result is your own self-growing moat of optimizations for better performance and lower costs.
No other platform combines copilot-led visual orchestration with multi-agent finetuning to learn with you.
Proprietary Optimizations
Research-backed techniques that compound with scale.
Simulated Finetuning
Cheap runtime finetuning, model-size agnostic.
Efficient Swarm Protocols
Training-free optimization for swarms of small-medium models.
Long-Context Inference
Maintain perplexity while slashing costs on long contexts.
Future full-stack optimizations
Built by Engineers, for Everyone.
Our team brings together expertise spanning ML research, kernel engineering, and compilers. We don't just integrate existing tools; we build the underlying infrastructure that makes fine-tuning and serving genuinely fast.
Compiler Design
We compile visual graphs into clean, debuggable Python so your pipeline remains transparent and hackable.
Kernel Engineering
Runtime kernels are tuned for memory and throughput, so training jobs scale efficiently across mixed hardware.
ML Research
We build and validate new optimisation strategies to improve convergence speed and model quality.
Production Systems
From autoscaling to rollout safety, we design deployment paths that are reliable in real-world traffic.
Team
Scientists and engineers with experience across the stack - from leading organisations like Oxford, Arm, Quantinuum, Morgan Stanley, and LinkedIn.
- SoC Engineer, Arm and Purdue SoCET
- AI for Physics & Optimization, ML Acceleration
- ML for Predictive Maintenance, Data Centers, LinkedIn
- ML Theory, Systems Engineering
- Oxford Cosmos Fellow & 2x Gibbs Prize
- Multi-Agent Systems, Human-Centric AI, Electronic Musician
- Quant, Squarepoint & Morgan Stanley
- Multi-Task Learning, Agent Swarms, ML for Finance
- Compiler Engineer, Quantinuum
- Performance Engineering, Programming Language Theory, Homotopy Theory
- Senior Researcher, Oxford & Quantinuum
- Category Theory, ML Theory
- CTO, Spher
- Full-Stack Systems, ML Infrastructure
Affiliations & Partners
Platform Comparison
| pbj | OpenAI Agent Builder | Langflow | LangGraph | Tinker | Modal | TRL | |
|---|---|---|---|---|---|---|---|
| Visual | Yes | Yes | Yes | No | No | No | No |
| Ease | Easy | Easy | Easy | Moderate | Expert | Moderate | Expert |
| Multi-agent | Yes | Yes | Yes | Yes | N/A | N/A | N/A |
| Fine-tune | Yes | No | No | No | Yes | Yes | Yes |
| Hosting | Yes | Yes | Partial | No | Yes | Yes | No |
| Models | Any | GPT only | Any | Any | Any | Any | Any |
Start from an Example
Open a pre-built pipeline in the editor to see what's possible, then tweak it to fit your use case.