01
Scope
A working session to identify the problem worth solving and the smallest spike that would prove it tractable. Written success criteria.
The engagement model under the Quantum Leap initiative. Production agents on Bedrock + Claude, evaluation harnesses, and the boring observability that keeps them honest — delivered in two-week spikes with honest exits.
Quantum Labs is the engagement model under the Quantum Leap Initiative — Orion's full-stack approach to production AI. Quantum Leap names the nine layers (infrastructure, data, models, retrieval, orchestration, tools, evaluation, observability, governance). Quantum Labs is how those layers get built into a working pipeline on a real engagement.
The work that actually matters in production — extracting structure from a domain-specific document set, building an agent that uses your internal tools, evaluating whether a model is reliable enough to ship — is vertical. We build it on AWS Bedrock and Claude, wire it into your stack with proper tool-use boundaries, and grade it with evaluation harnesses you own and can re-run after every model bump.
Every engagement starts as a two-week spike. At the end we either graduate it to a longer engagement, hand it off to your team to run, or kill it with honest reasoning. We do not run open-ended R&D retainers — that's how AI work becomes a sinkhole.
Every engagement has a defined exit. We name the success criteria before the work starts.
A working session to identify the problem worth solving and the smallest spike that would prove it tractable. Written success criteria.
Two weeks. We build a working end-to-end pipeline — data, model, evaluation, observability — and stress-test it on real inputs.
If the spike clears the bar, we either run a longer build engagement or hand off the spike code for your team to take to production.
If the spike does not clear the bar, we tell you honestly why and what would change the answer. No retainer renewal off momentum.
Retrieval pipelines. Chunking, embedding, indexing. We build them on your data, in your account, with your auth boundaries. OpenSearch + Bedrock embeddings by default; alternatives when they fit.
Agents with real tool use. Bedrock Agents or Claude with MCP servers, wired to your existing APIs with proper permission boundaries. Audit-loggable from the first call.
Evaluation harnesses. Test sets you own, scoring rubrics that match your domain, regression detection across model versions. Re-run every time you bump a model.
Observability that does not gaslight. Token spend, latency p50/p95/p99, refusal rate, tool-call success rate, downstream error rates. The dashboard your finance team sees and your engineering team sees are the same dashboard.