We work on ITAR-controlled technical documents. Can a RAG system actually handle that, or does cloud LLM access rule it out?

ITAR compliance for a RAG pipeline is a deployment and access-control question, not an impossibility. The architecture that works for most San Diego defense-adjacent engineering teams keeps the retrieval index and document store entirely within your controlled network boundary. The vector database, the document store, and the inference layer all run on infrastructure you control, whether that is on-prem in your facility or in a GovCloud-adjacent AWS or Azure region with the appropriate boundary controls. The LLM inference call goes to a model deployed within your boundary rather than to a public API endpoint. For Controlled Unclassified Information, this typically means Azure Government or AWS GovCloud with ITAR-specific BAAs and access logging. We do not try to bolt ITAR controls onto a consumer-grade cloud architecture. We design the network boundary first, then build the pipeline inside it.

Our team maintains FDA validation documentation for a Class II medical device. What does RAG over that corpus actually look like?

FDA validation document corpora have a structure that favors RAG well: they are deeply hierarchical (Design History File, Risk Management File, Verification and Validation reports, 510(k) summaries), they reference each other extensively, and the questions your engineers ask are almost always about a specific version of a specific control. Ingestion preserves the DHF hierarchy as document metadata so a query about design inputs filters to the correct DHF chapter rather than returning the nearest semantic neighbor across the entire corpus. Version control is critical: we tag every document chunk with its revision number and effective date, and retrieval defaults to the current approved version unless the query explicitly requests historical. Audit logging captures every retrieval event with the user, the query, and the document revision surfaced, which satisfies the traceability requirements your QA team will ask about in an FDA audit.

Illumina and Becton Dickinson run genomic and clinical data pipelines. What does data residency mean in practice for a biotech RAG system?

For a San Diego biotech, data residency has two dimensions. The first is where the embeddings live. Embeddings generated from proprietary sequence data or clinical trial documents are themselves considered sensitive IP, and some biotech legal teams treat them as regulated data under their agreements with CROs and clinical partners. We store embeddings in a vector database within a defined geographic region (typically US-West in AWS or Azure) and confirm this in writing during the project kickoff. The second dimension is the inference call. When the retrieval context contains controlled clinical data, the generation call cannot go to a public LLM API endpoint under standard terms of service. We route inference to a model endpoint with a BAA in place, or we deploy a self-hosted model within the same network boundary as the index. Both paths add cost relative to a consumer-cloud architecture, and we scope that into the project price rather than surfacing it as a surprise.

SAIC and Leidos handle documents across multiple classification-adjacent tiers. How do you handle access control when different engineers have different clearance levels?

The right architecture for multi-tier access control in a defense-contractor RAG system is attribute-based access control at the retrieval layer, not at the generation layer. We tag every document chunk at ingestion with its access tier, program office, and any additional access attributes your security team specifies. The retrieval query carries the user's identity and their associated access attributes, authenticated against your existing identity provider (typically Active Directory or Okta for large primes). The vector search runs with a metadata pre-filter that restricts the search space to documents the requesting identity is authorized to see before similarity scoring happens. This means a cleared engineer with access to Program X documents never receives a retrieval result from Program Y even if the semantic similarity is high. We validate this with adversarial test cases during the evaluation phase before handoff.

RAG Development · San Diego, CA

RAG for San Diego's regulated-industry engineering teams.

San Diego is not a typical enterprise software city. Qualcomm writes modem firmware under export controls. Illumina processes genomic data under agreements with clinical partners. SAIC and Leidos move documents across program offices with different access tiers. Becton Dickinson maintains a Design History File for every medical device it ships. These are not knowledge-base problems. They are regulated-document problems where retrieval precision and access control matter more than speed.

We build RAG systems for San Diego engineering teams that treat compliance as a first-class design constraint. ITAR-aware deployment boundaries, FDA validation document retrieval with version tagging, biotech data residency within defined AWS or Azure regions, and attribute-based access control at the retrieval layer rather than at the generation layer.

Engagement pricing is scoped to your document environment and compliance requirements. Regulated deployments that require additional infrastructure and compliance-validation work scope accordingly.

How we work

Tell us your document environment and your compliance requirements.

Who this is for.

You are a Director of Engineering or VP of Engineering at a San Diego defense, biotech, or medical device company. Your team maintains large document corpora that your security review, your QA organization, or your government customer takes seriously.

The problem is not that your documents are hard to find in isolation. It is that the corpus is too large and too cross-referenced for any engineer to hold in their head, and the existing search tools do not understand the semantic relationships between a design requirement, its verification test, and the associated risk control measure.

You have already heard the vendor pitch about AI assistants. What you need is a concrete answer to the question your CISO will ask: where does the data go, who can see it, and how do you know?

This is not the right fit if

—Your documents are unclassified internal HR or marketing content with no special handling requirements. A commodity tool like Glean or Guru will cost less.
—You need retrieval over fewer than 500 documents. A well-organized Notion or Confluence with good search covers that without a custom pipeline.
—You need the system running in four weeks. Regulated deployments take longer because the compliance validation is part of the deliverable.
—Your classified document corpus requires a system operating at TS/SCI or above. We work with CUI and ITAR-controlled data, not classified materials.

Technical approach.

Four areas where regulated-industry RAG diverges from standard knowledge-base retrieval.

Ingestion with hierarchical compliance metadata

Every document chunk carries its source system, document type, version number, effective date, and any access-control attributes your security team specifies. For FDA documentation, that means DHF chapter, revision, and approval status. For defense documents, it means program office, contract number, and distribution statement. Metadata-based pre-filtering happens before vector similarity scoring, not after, so a stale revision never surfaces as a retrieval result alongside the current approved version.

Hybrid retrieval tuned for technical precision

Regulated-industry documents use controlled terminology where exact phrase matching matters as much as semantic similarity. BM25 keyword search runs in parallel with dense vector retrieval, and the results are fused with reciprocal rank fusion before re-ranking. For specifications and standards documents, this combination outperforms pure vector search on precision metrics because it catches exact cross-references like section numbers and part numbers that semantic embeddings treat as arbitrary tokens.

Security and access control at the retrieval layer

Attribute-based access control enforced at query time against your existing identity provider. Every retrieval event is logged with user identity, query text, documents surfaced, and document version. The audit log satisfies the traceability requirements your QA organization will ask about in an FDA audit and the access-control validation your government customer will ask about in a DCSA review. The logs are written to append-only storage your team controls.

Evaluation harness with compliance test cases

A test set of 150 to 200 questions drawn from your actual document corpus, validated by your subject-matter experts. Precision and recall benchmarked before handoff. Adversarial test cases that verify access-control boundaries, version-isolation behavior, and retrieval accuracy on ambiguous cross-references. The evaluation report is a deliverable, not an internal benchmark. Your security review team can read it.

What makes San Diego different.

San Diego has one of the highest concentrations of ITAR-controlled engineering work outside of Washington, DC. Qualcomm's chip designs, SAIC and Leidos program documentation, and the defense-adjacent work at dozens of smaller primes along Sorrento Valley and Mira Mesa all fall under Export Administration Regulations or ITAR depending on the end-use classification. The practical consequence for a RAG deployment is that you cannot send retrieval context to a public LLM API and expect your export-control team to sign off. The deployment boundary has to be designed around the data classification, not retrofitted to it.

The biotech corridor from Torrey Pines to Sorrento Mesa concentrates the FDA validation document problem. Illumina, Becton Dickinson, Hologic, and a dense cluster of clinical-stage biotechs all maintain Design History Files, Risk Management Files, and validation protocol libraries that run to thousands of pages. The engineers who query these documents need version- correct answers. A retrieval system that returns the superseded version of a verification procedure is not just unhelpful; it is a quality-system event.

Biotech data residency is a contract obligation before it is a regulatory one. CRO agreements, clinical partner agreements, and some NIH grant conditions specify where data generated under the agreement can be processed and stored. For a San Diego biotech running a RAG pipeline over clinical trial data, we confirm the residency requirement in writing during project kickoff and select the infrastructure accordingly. This is not a nice-to-have. It is the condition under which your legal team will allow the project to proceed.

ServiceNow's San Diego campus and the broader enterprise software presence add a different shape: large internal knowledge bases for technical support, implementation guides, and partner enablement content that do not carry defense or medical-device compliance requirements but do carry enterprise security expectations around SSO, audit logging, and data handling that commodity tools frequently do not meet.

What you receive.

Ingestion pipeline

Document connectors for your source systems with full metadata preservation, version tagging, and access-attribute extraction. Incremental sync so newly approved document versions propagate without a full re-index.

Access-controlled retrieval API

Attribute-based access control enforced at query time against your identity provider. Per-user retrieval scoping validated against your access-control matrix before handoff.

Compliance-grade audit log

Append-only log of every retrieval event: user identity, query, documents surfaced, document versions. Written to storage your team controls and formatted for review by your QA or security team.

Evaluation report

150 to 200 question test set validated by your subject-matter experts. Precision, recall, and access-control boundary verification documented before handoff. The report is a formal deliverable.

Deployment within your boundary

Infrastructure configured within your controlled network, GovCloud region, or data-residency-compliant cloud environment. Network architecture diagram and data flow documentation included.

Runbook and handoff

Operational runbook for your engineering team covering index updates, access-attribute management, performance monitoring, and the steps to add a new document source. One-hour walkthrough with your team included.

Questions from San Diego engineering buyers.

We work on ITAR-controlled technical documents. Can a RAG system actually handle that, or does cloud LLM access rule it out?: ITAR compliance for a RAG pipeline is a deployment and access-control question, not an impossibility. The architecture that works for most San Diego defense-adjacent engineering teams keeps the retrieval index and document store entirely within your controlled network boundary. The vector database, the document store, and the inference layer all run on infrastructure you control, whether that is on-prem in your facility or in a GovCloud-adjacent AWS or Azure region with the appropriate boundary controls. The LLM inference call goes to a model deployed within your boundary rather than to a public API endpoint. For Controlled Unclassified Information, this typically means Azure Government or AWS GovCloud with ITAR-specific BAAs and access logging. We do not try to bolt ITAR controls onto a consumer-grade cloud architecture. We design the network boundary first, then build the pipeline inside it.
Our team maintains FDA validation documentation for a Class II medical device. What does RAG over that corpus actually look like?: FDA validation document corpora have a structure that favors RAG well: they are deeply hierarchical (Design History File, Risk Management File, Verification and Validation reports, 510(k) summaries), they reference each other extensively, and the questions your engineers ask are almost always about a specific version of a specific control. Ingestion preserves the DHF hierarchy as document metadata so a query about design inputs filters to the correct DHF chapter rather than returning the nearest semantic neighbor across the entire corpus. Version control is critical: we tag every document chunk with its revision number and effective date, and retrieval defaults to the current approved version unless the query explicitly requests historical. Audit logging captures every retrieval event with the user, the query, and the document revision surfaced, which satisfies the traceability requirements your QA team will ask about in an FDA audit.
Illumina and Becton Dickinson run genomic and clinical data pipelines. What does data residency mean in practice for a biotech RAG system?: For a San Diego biotech, data residency has two dimensions. The first is where the embeddings live. Embeddings generated from proprietary sequence data or clinical trial documents are themselves considered sensitive IP, and some biotech legal teams treat them as regulated data under their agreements with CROs and clinical partners. We store embeddings in a vector database within a defined geographic region (typically US-West in AWS or Azure) and confirm this in writing during the project kickoff. The second dimension is the inference call. When the retrieval context contains controlled clinical data, the generation call cannot go to a public LLM API endpoint under standard terms of service. We route inference to a model endpoint with a BAA in place, or we deploy a self-hosted model within the same network boundary as the index. Both paths add cost relative to a consumer-cloud architecture, and we scope that into the project price rather than surfacing it as a surprise.
SAIC and Leidos handle documents across multiple classification-adjacent tiers. How do you handle access control when different engineers have different clearance levels?: The right architecture for multi-tier access control in a defense-contractor RAG system is attribute-based access control at the retrieval layer, not at the generation layer. We tag every document chunk at ingestion with its access tier, program office, and any additional access attributes your security team specifies. The retrieval query carries the user's identity and their associated access attributes, authenticated against your existing identity provider (typically Active Directory or Okta for large primes). The vector search runs with a metadata pre-filter that restricts the search space to documents the requesting identity is authorized to see before similarity scoring happens. This means a cleared engineer with access to Program X documents never receives a retrieval result from Program Y even if the semantic similarity is high. We validate this with adversarial test cases during the evaluation phase before handoff.

Ready to scope your regulated-industry pipeline?

Tell us your document environment, your compliance requirements, and what your security review will care about. We reply within one business day with a rough scope and price range.