Contract & legal docs
Document intelligence on the corpus your business runs on.
Clause-aware chunking, hybrid retrieval, citation-grounded answers, refusal on out-of-corpus questions. The retrieval and evaluation layers carry the engagement.
Every Quantum Leap engagement ships an evaluation harness as the delivered artifact — your test sets, your domain rubrics, your CI gate. It re-runs on every model bump, every prompt change, every retrieval tweak. The harness is the contract; everything else is implementation detail.
The frontier of horizontal AI is moving so fast that any single benchmark, model release, or framework choice is obsolete in months. That is not the part of the problem most teams actually need help with.
The work that ships — extracting structure from a domain-specific document set, building an agent that uses your internal tools, grading whether a model is reliable enough to put in front of customers — is vertical. It requires the model, the data, the retrieval, the evaluation, the observability, and the operating model to line up. When one layer is missing, the demo works and production silently drifts.
The Quantum Leap Initiative is the way Orion thinks about that whole stack. It is the playbook we use on every AI engagement: which layers exist, what choices each layer forces, what we build vs. buy, and how we hand the result back to your team.
Our stack — what every engagement runs against. Reference architecture, tooling, and the five mistakes per layer live in Research.
The playbook is the same everywhere; the domain shapes which layers do the heaviest lifting.
Clause-aware chunking, hybrid retrieval, citation-grounded answers, refusal on out-of-corpus questions. The retrieval and evaluation layers carry the engagement.
WOSB-certified, AWS Select Partner, Bedrock-on-VPC posture. The infrastructure and governance layers carry the engagement.
Tool-use boundaries, audit logs that survive a real review, PII masking at the orchestration layer. The tools and governance layers carry the engagement.
Mid-market firm with ~12,000 vendor contracts. Started the spike on a bought RAG-as-a-service that was returning fluent-sounding answers with citations to the wrong clauses. Replaced it in two weeks with custom clause-aware chunking, hybrid (dense + BM25 + RRF) retrieval, and an evaluation harness co-authored with their procurement team.
Outcome: graduated to a six-week build. The customer's team now operates the system without us. The eval harness re-runs on every model bump.
One anonymized spike. The numbers belong to this engagement — what hybrid retrieval and a real evaluation harness did against that customer's corpus. Treat them as a worked example, not a forecast.
Generic models are extraordinary and generic. The work that matters in production is domain-specific. We build for the domain.
AI ships on infrastructure, data, evaluation, and operations. The model is one layer of nine. Skip the others and the system silently fails in production.
Every engagement names a success bar before the work starts. Graduate the spike, hand it off, or kill it — never run momentum for revenue.
Bedrock-on-VPC. Test sets, prompts, agents — you own them. We keep nothing of yours and ship runbooks on the way out.
Quantum Labs is how Orion delivers against the Quantum Leap stack on a real engagement. Two-week spikes with defined success criteria. Either graduate it to a longer build, hand it off to your team, or kill it with honest reasoning.
The spike is where we map your problem to the nine layers, decide what to build vs. buy at each one, and stand up the smallest end-to-end pipeline that would prove the system can ship — data, model, retrieval, evaluation, observability, all the way to a working interface.
We do not run open-ended AI retainers. That is how AI work becomes a sinkhole. The Quantum Leap stack is the map; Quantum Labs is the constrained way we walk it with you.
Reference architectures per layer. Build-vs-buy decision frameworks. The tooling catalog we maintain across customer engagements. Standards and concepts we use as common ground. Long-form, technical, opinionated.
A paragraph about what you are trying to do is enough. We will tell you which layers are load-bearing for your use case — and whether a two-week spike is the right starting move.