LLM Integration Company in San Francisco

What you commission decides the number: a focused LLM proof of concept at SF rates, a production RAG or copilot for a mid-market SF buyer (legal-tech search, customer support deflection, internal engineering knowledge base, healthcare prior-auth assistant), or enterprise programs with multi-model routing, HIPAA BAAs, SOC 2 control mapping, AB 2013 disclosure files, and observability through Langfuse and Arize. Scope drivers include vector store design, retrieval and reranking, eval harnesses in CI, prompt injection red-team, and integration with two or three source systems. SF rates run above other US markets because of the OpenAI, Anthropic, and Pinecone talent premium, but we deliver from Edmonton and Chandigarh on PST hours so headline rates compress meaningfully. Fixed-fee proposals in USD, with one-time build cost separated from ongoing inference, reranking, and vector storage fees.

It depends on the task shape, residency posture, and cost at projected QPS. OpenAI GPT-4o is our default for general-purpose enterprise RAG and customer-facing copilots because tool use, structured outputs, and the Assistants API are mature, and the enterprise tier disables training on customer data by default. OpenAI o3 wins on reasoning-heavy or planning workloads where the latency budget tolerates longer responses. Anthropic Claude Sonnet 4 and Opus 4 through Bedrock us-west-2 win on long-context document analysis, careful refusal behavior, and instruction-following discipline that enterprise buyers and trust and safety teams appreciate. Gemini 2.5 Pro through Vertex AI wins where the workload sits next to BigQuery or the client is already in a Google cloud agreement. Self-hosted Llama 3.1 70B or Mistral Large on GPUs is the right answer when AB 2013 exposure or HIPAA posture rules out frontier APIs, when zero egress is a contractual requirement, or when inference cost at scale outweighs hosting complexity. We run the trade-off explicitly during discovery rather than picking a vendor for you.

Enterprise RAG for SF buyers is more than embed plus chat. We split retrieval into a dense vector layer (OpenAI text-embedding-3-large, Voyage 3, or Nomic on Pinecone, Weaviate, Chroma, Qdrant, or pgvector), a sparse keyword layer where the corpus benefits, and a Cohere Rerank or Voyage Rerank pass before generation. Generation grounds against retrieved chunks with citation-level provenance the user can click through, and refusal behavior is explicit when retrieval misses. Every response logs the model version, prompt template hash, retrieved document IDs, reranker scores, and tool calls into a tenant-scoped store the customer security team can sample. Evaluation runs on Braintrust or LangSmith covering accuracy on a curated gold set, hallucination rate against grounded sources, citation fidelity, refusal on out-of-policy prompts, p95 and p99 latency, and USD cost per resolved task. Regressions fail the GitHub Actions build before merge.

Yes. Prompt injection is the highest-impact production risk for customer-facing LLMs at SF unicorns and we treat it as a primary engineering concern. Defenses include strict separation of system and user instructions through provider primitives, input sanitization, retrieval source allow-lists with content classification on indirect injection in retrieved documents, output schemas validated through structured generation, tool-use confirmation gates for any state-changing action, content filters on both sides of the model, per-session rate limits, and per-tool monetary or impact caps enforced at the tool layer regardless of model instructions. We red-team every customer-facing deployment against direct, indirect, and supply-chain payloads aligned with the OWASP LLM Top 10 and MITRE ATLAS, plus a client-specific payload set. Findings ship as a written report with severity ratings, fix recommendations, and a retest pass before launch. For high-risk surfaces we run continuous red-team in production with synthetic payloads.

Healthcare AI in the Stanford Medicine, UCSF, and Sutter Health orbit requires HIPAA-aligned pipelines with a signed Business Associate Agreement covering every component that touches PHI. We route inference through Azure OpenAI Service in eastus2 and westus3 or Anthropic Claude on AWS Bedrock us-west-2 under signed BAAs, with PHI kept inside the customer VPC and de-identified where the use case allows. Vector storage runs on pgvector in Aurora us-west-2 inside the customer VPC, embeddings are computed inside the BAA boundary, and prompt and completion logs are stored with PHI-aware retention rules. For self-hosted alternatives we deploy Llama 3.1 70B on GPUs inside the customer VPC with zero egress. Audit logs are tamper-evident, indexed by patient identifier where required, and queryable by the customer privacy officer. We also map California Confidentiality of Medical Information Act obligations alongside HIPAA where the surface touches California residents.

We default to AWS us-west-2 (Oregon) primary with us-west-1 (N. California) secondary for latency-sensitive edges, GCP us-central1 or us-west1, and Azure West US 3 or East US 2. For HIPAA workloads we route through Azure OpenAI Service or Anthropic on AWS Bedrock under a signed BAA, keeping PHI inside the customer VPC. For EU exposure we set up parallel inference in eu-west-1 or eu-central-1 with GDPR DPAs and EU AI Act mapping. CPRA and CCPA obligations are mapped during discovery and documented in the data processing addendum. The enterprise tiers of OpenAI, Anthropic, and Google now disable training on customer data by default, and we contract for that posture explicitly. For zero-egress builds we self-host Llama 3.1 70B, Mistral Large, or Qwen on GPUs in the customer VPC so no prompt or completion leaves the customer's cloud account.

AB 2013 targets developers of generative AI systems made available to Californians, which has been read broadly to include teams that fine-tune or substantially modify a base model. Pure integration that wraps an OpenAI or Anthropic API without fine-tuning generally falls outside the obligation, but the moment we run a LoRA adapter, supervised fine-tune, RLHF pass, or DPO on the base model, the resulting artifact is in scope. For SF LLM integrations we run an AB 2013 trigger analysis early, structure the training-data manifest and rights posture so the disclosure is defensible if scope shifts, and produce a public-facing summary your counsel will sign off. We also flag adjacent obligations including the California AI Transparency Act and the Colorado AI Act for cross-state deployments. Frontier API providers will publish their own summaries for the base models, so the obligation cleanly bounds at the fine-tune layer.

A SF enterprise LLM project typically takes ten to eighteen weeks from kickoff to production, depending on integration depth and regulatory tier. Weeks one to three are discovery, model selection, AB 2013 trigger analysis, HIPAA BAA scoping where PHI is in scope, and SOC 2 control mapping. Weeks four to seven are retrieval design, eval set construction on Braintrust or LangSmith, and the first runnable end-to-end pipeline with citation grounding. Weeks eight to twelve are integration with source systems (Confluence, Notion, Slack, Google Drive, SharePoint, Snowflake, Salesforce, Zendesk), reranking and grounding tuning, and prompt injection red-team testing aligned with the OWASP LLM Top 10. Weeks thirteen to sixteen are shadow mode, security review, and observability wiring through Langfuse, Helicone, or Arize. Week seventeen onward is gradual rollout behind feature flags with measured quality, latency, and USD cost per resolved task. Healthcare programs with HIPAA documentation can extend to twenty-four weeks.