Production AI is
a stack, not a
single model.
The Quantum Leap Initiative is Orion's full-stack approach to vertical AI — nine layers from infrastructure to governance, with the tooling, evaluation, and operating discipline to keep them honest in production.
Generic models are extraordinary. Production AI is a different problem.
The frontier of horizontal AI is moving so fast that any single benchmark, model release, or framework choice is obsolete in months.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.
Nine layers. One stack.
The canonical Quantum Leap stack. Every engagement maps to these nine layers. The chips show where we do the actual work — custom code built for your domain, configuration of bought primitives, or evaluation and selection of frontier components. Reference architecture, tooling, and the five mistakes per layer live in Research.
Bedrock-on-VPC, account isolation, KMS keys you own. Sprintsail primitives + raw CDK for AI-specific resources.
Source ingestion. Lineage. Document parsing. Permissioned at the row, with content-addressable hashes.
Claude on Bedrock by default. Per-task selection — Sonnet for reasoning, Haiku for routing. Versioned.
Hybrid dense + BM25 + RRF fusion. Layout-aware chunking. Permission filtering at the index, not after.
Single-turn pipelines by default. Agents only when the workflow earns the multi-step surface area.
Named owner per tool. Audit log with the prompt that triggered each call. Side effects behind human review.
Faithfulness, coverage, refusal correctness. The harness is the contract. Re-runs on every model bump.
Token spend, refusal rate, retrieval hit rate, tool-call success. One dashboard for finance and engineering.
Layered prompt-injection defense. PII masking before the prompt is built. Lineage in every audit record.
Three verticals carry the stack.
The playbook is the same everywhere; the domain shapes which layers do the heaviest lifting. These are the surfaces where Orion currently runs the most work.
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.
Audit-ready AI for regulated, set-aside, and federal contexts.
WOSB-certified, AWS Select Partner, Bedrock-on-VPC posture. The infrastructure and governance layers carry the engagement.
Bedrock + human-in-the-loop for workflows that touch money.
Tool-use boundaries, audit logs that survive a real review, PII masking at the orchestration layer. The tools and governance layers carry the engagement.
What two weeks looked like, anonymized.
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.
Representative spike, anonymized. Numbers reflect the kind of improvement you can expect when hybrid retrieval + a real evaluation harness replaces a generic RAG vendor on a domain-specific corpus.
What we hold constant across every engagement.
Vertical depth over horizontal breadth.
Generic models are extraordinary and generic. The work that matters in production is domain-specific. We build for the domain.
A stack, not a stunt.
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.
Honest exits.
Every engagement names a success bar before the work starts. Graduate the spike, hand it off, or kill it — never run momentum for revenue.
Your data, your account, your IP.
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 the engagement model.
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.
The full playbook lives in Research.
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.
Bring us the problem.
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.