Compound your AI utilization
across the enterprise.
Purple Oyster orchestrates autonomous agentic workflows that orchestrate inference, RAG retrieval, fine-tuning, and tool-use into one self-optimizing pipeline — turning idle model capacity into measurable enterprise throughput.
Every workflow travels one luminous spine.
Requests enter at the top and slide down through six autonomous stages — each one routing, caching, and reasoning until your models are running hot enough to glow.
Semantic intake & routing
Every prompt, document, and tool-call is parsed, embedded, and routed to the cheapest model that can still do the job. Semantic routing keeps your context window lean.
Vector retrieval & RAG
Hybrid dense-sparse retrieval grounds every agent in your enterprise knowledge. Re-ranking and citation tracing keep hallucinations out of the inference loop.
Chain-of-thought planning
Autonomous planners decompose the goal into sub-tasks, pick tools, and self-critique before they spend a token. Inference only fires when the plan is sound.
Agentic tool-use at scale
Parallel tool-calls fan out across APIs, databases, and runbooks — then fold back into the conversation. GPU scheduling keeps the hot path warm and the cold path cheap.
KV-cache optimisation
Prompt prefixes are cached, quantized, and re-warmed across runs. Throughput climbs while latency and token cost fall — your utilization curve goes vertical.
Full observability & feedback
Every trace, span, and tool-call is replayable. Evaluation loops feed fine-tuning back upstream, so the next inference is sharper than the last.
Autonomous orchestration
Multi-agent graphs that plan, delegate, and self-heal. You declare the outcome; the agents fight over the steps.
Inference optimisation
Speculative decoding, batching, and KV-cache reuse squeeze more tokens per GPU-second out of every model you run.
Grounded retrieval
Hybrid RAG with re-ranking and citation tracing keeps agents honest, even across a million internal documents.
Continuous fine-tuning
Production traces become DPO datasets overnight. Your models drift toward your domain while you sleep.
Tool arbitrage
A semantic router sends each sub-task to the cheapest capable model. Frontier when you need it, distilled when you don't.
End-to-end observability
Replayable traces, span-level latency, and auto-evals turn agent runs into a dashboard your ops team can actually trust.
Where idle models become measurable ROI.
Enterprises don't have an AI problem — they have a utilization problem. Purple Oyster turns latent model capacity into throughput you can put on a CFO's slide.
Everything you need to run agentic in production.
Semantic routing
Intent classifiers send each request to the cheapest capable model. Frontier for hard reasoning, distilled for the rest.
KV-cache warming
Prefix caches stay hot across runs so repeat prompts cost a fraction of the tokens and return in milliseconds.
Tool-use graphs
Parallel function calls fan out and fold back. Agents call your APIs, databases, and runbooks as first-class tools.
Hybrid RAG
Dense + sparse retrieval with re-ranking and citation tracing. Grounded answers your auditors can verify.
Auto fine-tuning
Production traces become DPO datasets overnight. LoRA adapters ship without touching the base weights.
Replayable traces
Every span, tool-call, and token is replayable. Debug an agent run like a stack trace — but for cognition.
We build the orchestration layer for agentic AI.
Purple Oyster exists because enterprises bought the models but never the throughput. We turn idle inference capacity into autonomous workflows that actually compound.
The thesis
Most enterprises run their frontier models at a fraction of their useful capacity. Prompts sit in queues, context windows go cold, and tool-calls fire one at a time when they could fan out in parallel. The models aren't the bottleneck — the orchestration around them is.
Purple Oyster is the control plane that fixes that. We sit between your models and your business logic, routing, caching, planning, and observing every inference so your utilization curve goes vertical instead of flat.
We're an independent, product-led team of researchers and engineers obsessed with one question: how much intelligence can you extract per dollar of compute?
How we got here
A problem, not a product
Watching enterprise teams burn GPU budgets on sequential, un-cached, un-routed inference. The waste was the spec.
The inference spine
Prototyped a six-stage pipeline that cached prefixes, routed by intent, and replayed every trace. Throughput jumped 7× on internal benchmarks.
Purple Oyster ships
Production control plane for agentic workflows — semantic routing, KV-cache warming, tool-use graphs, and full observability behind one API.
Compound utilization
Enterprise customers running thousands of agentic runs per minute, with auto-evals feeding fine-tuning loops back upstream overnight.
Utilization over headcount
We measure success in tokens-per-GPU-second, not dashboards. If the curve isn't climbing, we're not done.
Replayable by default
Every inference is a trace you can rewind. Cognition should be debuggable like a stack trace.
Cheapest capable model
Frontier for hard reasoning, distilled for the rest. Semantic routing keeps token cost on a leash.
Agents that self-critique
A plan that can't criticise itself before it spends a token is just a loop with a budget. We build the former.
Tell us where your utilization is leaking.
Book a workflow audit or drop us a line. We'll map your idle model capacity to the agentic pipelines that will move the numbers that matter.
Let's talk throughput.
Whether you're running 50 models or 50,000 agentic runs a minute, we'll find the stage in your pipeline where tokens are evaporating and show you the fix on a live trace.
Request received.
Our team will reach out within one business day to map your pipeline.