Hire a RAG Developer, San Diego, CA

Hire a RAG developer in San Diego.

A research informatics lead at Neurocrine Biosciences needs to query five years of internal experimental data: LIMS exports, research notes, and patent prior art: in a single interface. The structured and unstructured data live in separate systems that do not talk to each other. A well-built RAG pipeline bridges both.

A technical lead at Leidos or Cubic Defense faces a different constraint: Controlled Unclassified Information cannot touch cloud APIs. The RAG pipeline must run entirely on company infrastructure: embeddings, vector store, and LLM all on-premise. OpenAI and Anthropic are not options for that data.

San Diego's biotech and defense sectors require RAG architectures that most vendors do not build. We do, with fixed-scope engagements sized to the project.

How we work

Tell us about your research data or ITAR requirements.

Two distinct use cases, two different architectures.

Biotech companies: Illumina, Neurocrine Biosciences, Retractable Technologies: accumulate experimental data across LIMS systems, electronic lab notebooks, and internal research reports. A scientist querying prior experiments needs to find results from both structured assay data (compound ID, IC50, assay conditions) and unstructured research notes that describe the experimental context. Traditional database search handles the structured side; traditional full-text search handles the unstructured side. Neither handles the combined query well.

RAG on a unified index of structured records and unstructured notes answers mixed queries in a single pass. A researcher at Illumina asking about prior sequencing runs on a specific sample type retrieves LIMS records alongside any related notes from the same experiments.

Defense contractors: General Dynamics IT, Leidos, Cubic Defense : work with CUI data that cannot touch external cloud services. ITAR regulations prohibit transmitting certain technical data outside US jurisdiction without authorization. Sending that data to OpenAI or Anthropic APIs is not compliant, regardless of the provider's data handling agreements.

An ITAR-compliant RAG system runs entirely on company infrastructure: BGE-M3 or E5-mistral-7b for embeddings, a self-hosted vector store (Qdrant or Chroma on-premise), and Llama 4 or Mistral on a GPU server within the company network. No data leaves the perimeter at any stage of the pipeline.

Biotech: querying internal experimental data.

Unified index for LIMS and research notes

LIMS exports (CSV, JSON from LabVantage or Labguru) are parsed as structured records with assay type, compound ID, result values, and date as metadata fields, plus a natural-language text representation. Research notes are chunked as standard text. Both live in the same vector index. A single query retrieves from both.

Patent prior art with hybrid search

IPC/CPC classification codes narrow the search space for prior art queries. RAG then runs on the filtered set for semantic relevance ranking. A Illumina scientist querying prior art for a new library preparation technique gets classification-filtered results ranked by semantic similarity, not just keyword match.

Five-year experimental corpus with date filtering

Experimental data queries often need temporal scope: 'results from the Q3 2022 assay campaign on this compound class.' Date metadata on every chunk enables range filters that scope results to the relevant experimental period. Historical queries return the right experimental context.

Evaluation on real research queries

Before handoff, we run an evaluation suite against 80-100 queries drawn from questions your scientists have actually asked. We measure precision, recall, and answer accuracy on both structured data retrieval (exact numeric values) and unstructured research note retrieval (relevant context). You receive benchmark numbers.

Defense: ITAR-compliant on-premise RAG.

Fully on-premise pipeline architecture

The complete RAG pipeline runs within the company network: embedding model (BGE-M3 or E5-mistral-7b), vector store (Qdrant or Chroma deployed on-premise), and LLM (Llama 4 or Mistral-7B-Instruct on GPU hardware). No data leaves the perimeter. No external API calls at any stage. The architecture is auditable at every layer.

Hardware scoping for GPU deployment

A 70B parameter model requires approximately 140GB of GPU memory at float16 precision, four A100 80GB cards or two H100 80GB cards. We scope the hardware requirements alongside the software architecture. If your existing GPU infrastructure is insufficient, we specify the minimum hardware footprint and include that in the project scope before any code is written.

Air-gapped deployment for classified environments

For environments where even internal network access is restricted, we build an air-gapped deployment package: all models, dependencies, and the application are packaged for offline installation. The package is verified against a manifest before installation. Updates arrive as a new verified package, not as runtime pulls from the internet.

Document access controls for multi-program environments

Defense contractors often handle documents from multiple programs with different access levels. Document-level access controls enforce program boundaries at the vector store metadata query layer. A user cleared for Program A cannot retrieve chunks tagged for Program B, regardless of semantic similarity.

How we work.

We start with a one-hour discovery call. For biotech projects, we ask to see a sample of your LIMS export format and a few representative research notes before the call. The data schema in LIMS exports varies significantly by platform: LabVantage exports look different from Labguru exports, and both differ from custom in-house systems.

For defense projects, the discovery call focuses on data classification level, network architecture (connected to corporate network, air-gapped, or classified network), existing GPU hardware inventory, and document access control requirements. We do not require access to the actual documents at the scoping stage.

After the call we deliver a written scope within two business days. The scope includes a hardware requirement specification for on-premise deployments. Build takes six to ten weeks for on-premise ITAR deployments due to the additional testing required for air-gapped packaging.

Full process details →

Questions about biotech research data and ITAR-compliant RAG.

What open-source embedding models and LLMs work well for on-premise ITAR-compliant RAG deployments?: For on-premise ITAR deployments, the embedding model and LLM must run entirely on company infrastructure, no external API calls to OpenAI, Anthropic, Cohere, or any cloud provider. For embeddings, BGE-M3 (from BAAI) and E5-mistral-7b run on GPU hardware and perform well on technical document retrieval without requiring cloud access. For generation, Llama 4 or Mistral-7B-Instruct can be deployed on-premise on a 4-GPU server (A100 or H100). For defense contractors like Leidos or Cubic Defense working with CUI data, the deployment runs entirely within the classified or company network perimeter. The practical constraint is hardware: a 70B parameter model requires approximately 140GB of GPU memory at float16 precision. We scope the hardware requirements alongside the software architecture so there are no surprises at deployment time.
How do you handle experimental data in structured formats (CSV, JSON from LIMS) alongside unstructured research notes in the same RAG system?: Structured and unstructured data require different ingestion paths that converge at a unified query interface. LIMS exports (CSV or JSON from platforms like LabVantage or Labguru) are parsed as structured records, each experiment gets metadata fields (assay type, compound ID, result value, date, researcher) stored alongside a natural-language text representation of the record. Unstructured research notes are chunked with standard text-based chunking. Both types are embedded and stored in the same vector index. A query from a Neurocrine Biosciences or Illumina researcher like 'what were the IC50 values for compound NBI-001 in the July assay runs' retrieves from both structured LIMS records and any related research notes in a single pass. The answer includes structured data fields for precision and unstructured context for interpretation.
What does the latency look like for on-premise RAG vs. cloud-based RAG: is it acceptable for research workflows?: On-premise RAG latency is typically 2-5 seconds end-to-end for a standard query against a corpus of 100,000 document chunks, using a 7B parameter generation model on modern GPU hardware. Cloud-based RAG with GPT-5 or Claude typically runs 1-3 seconds for the same query. The gap narrows with faster hardware and smaller models. For research workflows: where a scientist is querying to find a result or a prior study, 3-4 seconds is acceptable. The response time that matters is the time from the researcher's question to a usable answer, not the raw API latency. Replacing a 45-minute manual literature search with a 4-second RAG query is a substantial improvement even if the 4 seconds is slower than cloud. The on-premise constraint for ITAR data is non-negotiable regardless of the latency comparison.
How do you handle the patent prior art search use case specifically, is RAG better or worse than traditional patent search tools?: RAG and traditional patent search tools serve different retrieval objectives. Traditional tools like Derwent Innovation, PatSnap, or Google Patents Patent Search use keyword search, classification codes (IPC/CPC), and citation networks. They are optimized for the structured attributes of patents: filing date, assignee, classification code, claims language. RAG on a patent corpus is better at semantic queries that do not map cleanly to keywords or classification codes: 'what prior art exists for managing thermal dissipation in dense array LED architectures' returns conceptually related patents even when the exact terminology differs. The most effective approach for Illumina or a UCSD tech transfer office combines both: use classification code filters to narrow the search space, then apply RAG on the filtered set for semantic relevance ranking. We build hybrid pipelines that use structured patent metadata as pre-filters before the vector search runs.

Scope your San Diego research or defense RAG project.

Describe your data environment, classification requirements, and the queries your team needs to answer. We reply within one business day.

Hire a RAG developer in San Diego.

San Diego's biotech and defense sectors require RAG architectures that most vendors do not build. We do, with fixed-scope engagements sized to the project.

Two distinct use cases, two different architectures.

Biotech: querying internal experimental data.

Unified index for LIMS and research notes

Patent prior art with hybrid search

Five-year experimental corpus with date filtering

Evaluation on real research queries

Defense: ITAR-compliant on-premise RAG.

Fully on-premise pipeline architecture

Hardware scoping for GPU deployment

Air-gapped deployment for classified environments

Document access controls for multi-program environments

How we work.

Questions about biotech research data and ITAR-compliant RAG.

What open-source embedding models and LLMs work well for on-premise ITAR-compliant RAG deployments?

For on-premise ITAR deployments, the embedding model and LLM must run entirely on company infrastructure, no external API calls to OpenAI, Anthropic, Cohere, or any cloud provider. For embeddings, BGE-M3 (from BAAI) and E5-mistral-7b run on GPU hardware and perform well on technical document retrieval without requiring cloud access. For generation, Llama 4 or Mistral-7B-Instruct can be deployed on-premise on a 4-GPU server (A100 or H100). For defense contractors like Leidos or Cubic Defense working with CUI data, the deployment runs entirely within the classified or company network perimeter. The practical constraint is hardware: a 70B parameter model requires approximately 140GB of GPU memory at float16 precision. We scope the hardware requirements alongside the software architecture so there are no surprises at deployment time.

How do you handle experimental data in structured formats (CSV, JSON from LIMS) alongside unstructured research notes in the same RAG system?

Structured and unstructured data require different ingestion paths that converge at a unified query interface. LIMS exports (CSV or JSON from platforms like LabVantage or Labguru) are parsed as structured records, each experiment gets metadata fields (assay type, compound ID, result value, date, researcher) stored alongside a natural-language text representation of the record. Unstructured research notes are chunked with standard text-based chunking. Both types are embedded and stored in the same vector index. A query from a Neurocrine Biosciences or Illumina researcher like 'what were the IC50 values for compound NBI-001 in the July assay runs' retrieves from both structured LIMS records and any related research notes in a single pass. The answer includes structured data fields for precision and unstructured context for interpretation.

What does the latency look like for on-premise RAG vs. cloud-based RAG: is it acceptable for research workflows?

On-premise RAG latency is typically 2-5 seconds end-to-end for a standard query against a corpus of 100,000 document chunks, using a 7B parameter generation model on modern GPU hardware. Cloud-based RAG with GPT-5 or Claude typically runs 1-3 seconds for the same query. The gap narrows with faster hardware and smaller models. For research workflows: where a scientist is querying to find a result or a prior study, 3-4 seconds is acceptable. The response time that matters is the time from the researcher's question to a usable answer, not the raw API latency. Replacing a 45-minute manual literature search with a 4-second RAG query is a substantial improvement even if the 4 seconds is slower than cloud. The on-premise constraint for ITAR data is non-negotiable regardless of the latency comparison.

How do you handle the patent prior art search use case specifically, is RAG better or worse than traditional patent search tools?

RAG and traditional patent search tools serve different retrieval objectives. Traditional tools like Derwent Innovation, PatSnap, or Google Patents Patent Search use keyword search, classification codes (IPC/CPC), and citation networks. They are optimized for the structured attributes of patents: filing date, assignee, classification code, claims language. RAG on a patent corpus is better at semantic queries that do not map cleanly to keywords or classification codes: 'what prior art exists for managing thermal dissipation in dense array LED architectures' returns conceptually related patents even when the exact terminology differs. The most effective approach for Illumina or a UCSD tech transfer office combines both: use classification code filters to narrow the search space, then apply RAG on the filtered set for semantic relevance ranking. We build hybrid pipelines that use structured patent metadata as pre-filters before the vector search runs.

Scope your San Diego research or defense RAG project.

Describe your data environment, classification requirements, and the queries your team needs to answer. We reply within one business day.