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Hire an AI Consultant · San Diego, CA
San Diego's AI strategy questions are different from the ones asked in San Francisco. SAIC, Leidos, and Cubic Defense need to know which AI capabilities can run air-gapped, without sending a byte to a cloud provider. Illumina and Neurocrine need to know whether an AI tool in a clinical workflow triggers a 510(k) or PMA submission before a single line of code is written.
These are not general AI strategy questions. They require someone who knows both AI capabilities and defense or FDA compliance. A consultant who only knows one side gives advice that either overestimates what the regulation allows or underestimates what the technology can actually do.
Fixed scope, fixed price, scoped to your engagement. Three to four weeks. Written deliverables yours to keep.
Tell us about your ITAR or regulated AI strategy question.
Two distinct client types. Both need compliance-aware AI advice, but for different regulatory regimes.
Companies like SAIC, Leidos, and Cubic Defense face an AI strategy problem that standard cloud AI services cannot solve. The data they work with is ITAR-controlled or CUI. The tools their teams want to use send data to servers outside their control. The question is which AI capabilities can be deployed on-premise, which models run air-gapped, and what that infrastructure actually costs. A general AI consultant does not know the answer. We do.
Companies like Illumina and Neurocrine are building AI tools that touch clinical workflows. Those tools are Software as a Medical Device under FDA's framework, which means 510(k) or PMA before commercialization. An AI consultant who has never read the SaMD guidance will design a system architecture that makes the regulatory pathway harder and longer. We help biotech teams make the pathway determination before system design, not after it.
San Diego's telecom sector (Qualcomm, Verizon's network operations presence) and its hospital systems face AI vendor procurement questions: Does this vendor's security posture match the data classification of what we're sending them? A structured vendor assessment before contract signature costs less than the remediation required after a procurement compliance finding.
A San Diego SaaS company selling into defense or healthcare customers faces secondary compliance requirements when it adds AI features. If a healthcare customer's data touches your AI pipeline, HIPAA's minimum necessary standard applies. If a defense customer's data touches it, ITAR and CMMC obligations may follow. An AI feature roadmap that ignores this creates enterprise sales blockers.
Most commercial AI tools are disqualified for defense use before you finish reading their privacy policy. The strategy question is what remains.
We evaluate which open-weight models (Llama 4, Mistral, Code Llama) are mature enough to replace cloud APIs for your specific use cases. The answer depends on task type, quality bar, and inference latency requirements. We test against your data, not benchmarks.
For use cases that require air-gapped deployment, we specify the hardware stack, the inference framework, and the data flow architecture. A standard air-gapped LLM setup runs on a two-GPU workstation for $8,000 to $12,000 in hardware. We scope what your use case actually requires before you order equipment.
We map your intended AI use cases against the USML categories that apply to your programs. Use cases that can proceed with commercial tools under appropriate controls are distinguished from those that require on-premise deployment or exclusion entirely. The output is a written risk assessment you can share with your legal and compliance team.
The pathway determination is not a legal opinion. It is a product strategy decision that affects system architecture, data requirements, and timeline.
510(k) is available when a substantially equivalent predicate exists in the FDA 510(k) database. Clearance typically takes 12 to 18 months. PMA is required for life-sustaining indications or where no predicate exists, and clinical trial evidence is usually required. The choice determines your data labeling strategy, validation study design, and the documentation FDA will require. Making this determination after system design means rebuilding.
FDA's 2024 guidance on AI/ML-based SaMD requires a predetermined change control plan, algorithm change documentation, and performance monitoring with defined retraining triggers. Companies that arrive at FDA review without these documents receive a refuse-to-accept determination. We build the governance program in parallel with product development so submissions are not delayed by documentation gaps.
Week 1
Two or three interviews: technical lead, product or regulatory lead, and business stakeholder. We review existing documentation including any vendor proposals, compliance assessments, or prior AI evaluations. Written summary of findings delivered at the end of week one.
Weeks 2–3
Use case scoring, build vs. buy cost modeling, vendor evaluation against your specific requirements, and compliance risk mapping. For defense clients: on-premise model testing against your data. For biotech clients: regulatory pathway analysis and governance gap assessment.
Week 4
Written deliverables: use case analysis, compliance risk map, build vs. buy recommendation, and implementation roadmap. Final review session to answer questions. All documents are yours to share with your board, legal team, or development team.
Describe the ITAR constraint or FDA pathway question you're working through. We reply within one business day with a rough scope and price range. No commitment required.