North Rose Technologies
San Francisco, CA

AI Services in San Francisco

San Francisco is the global epicenter of artificial intelligence. OpenAI, Anthropic, Google DeepMind, and Meta AI all operate significant research labs here. The Bay Area has over 45,000 AI professionals — more than any other city in the world — and AI startups raised $27 billion in the region in 2024. But building production AI is different from doing research. We help SF companies turn models into products, prototypes into production systems, and AI experiments into revenue.

150+ Projects Delivered
60% Cost Savings
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50+ Happy Clients

Why San Francisco for AI Services?

San Francisco is not just an AI market — it is the AI market. Stanford's AI lab (HAI) and UC Berkeley's AI research group (BAIR) publish more cited AI papers than nearly any institutions on the planet. The city's AI ecosystem includes the companies building the foundation models, the startups building applications on top of them, and the enterprises adopting AI across their operations. The challenge in SF is not finding AI talent — it is finding AI talent that is available and affordable. That is where we come in.

  • OpenAI, Anthropic, Google DeepMind, and Meta AI all have major operations in San Francisco
  • Bay Area AI startups raised $27 billion in 2024, accounting for 40% of all US AI venture funding
  • Stanford HAI and UC Berkeley BAIR are the two most-cited AI research institutions in the world
  • 45,000+ AI professionals in the Bay Area, but demand still far outpaces supply for production ML engineers
AI Capabilities

AI Services for Bay Area Companies

San Francisco companies expect AI teams that ship production systems, not science experiments. Here is what we build.

LLM Application Development

Production-grade applications built on GPT-4, Claude, Llama, and Mistral. We build RAG pipelines, fine-tuned models, and multi-agent orchestration systems. SF companies use our LLM apps for customer support, content generation, and internal knowledge management.

AI Agent Architecture

Multi-step AI agents that handle complex workflows autonomously. We design agent systems with tool use, memory, and planning capabilities. Our agents handle everything from code review to sales prospecting for Bay Area SaaS companies.

ML Model Development & MLOps

Custom model training, experiment tracking, and production ML infrastructure. We build models and the systems that keep them running — feature stores, model registries, automated retraining pipelines, and monitoring dashboards.

Computer Vision & Multimodal AI

Image classification, video analysis, and multimodal models that process text, images, and structured data together. Bay Area hardware companies use our vision systems for quality inspection and product recognition.

Data Engineering & Vector Infrastructure

Data pipelines, vector databases, embedding infrastructure, and feature stores purpose-built for AI workloads. We work with Pinecone, Weaviate, pgvector, and custom solutions depending on your scale requirements.

AI Infrastructure & GPU Optimization

Cloud infrastructure designed for AI workloads — GPU cluster management, distributed training setup, and inference cost optimization. We help SF companies reduce their AI compute bills by 30-50% through architecture improvements.

Use Cases

How Bay Area Companies Use Our AI Services

From Series A startups to FAANG companies, San Francisco businesses come to us for AI engineering that ships.

SaaS

AI-Native SaaS Products

San Francisco SaaS companies are rebuilding their products around AI. We integrate LLM-powered features, build recommendation engines, and develop intelligent automation within existing platforms. Our work helps SaaS companies move from "AI as a feature" to "AI as the product."

Developer Tools

Developer Tools & AI Infrastructure

Bay Area developer tool companies use our team to build AI-powered code analysis, automated testing, and intelligent debugging features. We also help AI infrastructure startups build their core platform — model serving, fine-tuning pipelines, and evaluation frameworks.

Fintech

Fintech & Crypto AI

San Francisco fintech companies deploy our AI for fraud scoring, underwriting automation, and personalized financial advice. For crypto firms, we build on-chain analytics, anomaly detection, and automated trading strategies.

Biotech

Biotech & Life Sciences AI

South San Francisco's biotech corridor uses our ML for protein structure prediction, drug compound screening, and clinical trial optimization. We process genomic and molecular datasets to accelerate research timelines.

Our Process

How We Work with SF Teams

Bay Area companies move fast. Our process is optimized for speed without cutting corners on model quality or production reliability.

Step 1

Technical Deep Dive

We start by understanding your existing stack, data, and AI goals. SF companies usually have opinions about model architecture — we respect that and layer our engineering on top of your team's domain knowledge.

1
Step 2

Data & Infrastructure Setup

We set up or extend your ML infrastructure — data pipelines, feature stores, experiment tracking, and compute environments. If you are already on Databricks, Snowflake, or a custom stack, we plug into it.

2
Step 3

Rapid Experimentation

We run structured experiments to find the best model approach for your problem. Multiple architectures, training strategies, and data augmentation techniques get tested in parallel. Results are logged and reproducible.

3
Step 4

Production Engineering

Models get containerized, optimized for inference speed, and deployed with proper CI/CD. We build serving infrastructure that handles Bay Area scale — millions of requests per day with sub-100ms latency.

4
Step 5

Integration & Launch

We integrate AI features into your product, set up A/B testing frameworks, and coordinate launch with your engineering team. Feature flags and gradual rollouts ensure nothing breaks for existing users.

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Step 6

Optimization & Iteration

Post-launch, we analyze real user interactions to improve model performance. We run A/B tests on model variants, optimize for cost and latency, and retrain on production data to close the feedback loop.

6

Ready to get started? Let's discuss your project.

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Pricing

AI Engagement Models for SF Companies

San Francisco AI salaries average $250K-$400K total comp. We give you senior AI engineering at a fraction of that cost with zero recruiting overhead.

Most Popular

Embedded AI Engineering Team

A team of ML engineers, data engineers, and an AI architect that integrates directly with your San Francisco engineering organization. They use your tools, attend your standups, and ship code to your repos.

Custom pricing based on your requirements

  • Senior ML engineers experienced with LLMs, RAG, and agent systems
  • Fully embedded in your engineering workflow and tool chain
  • Scale from 2 to 15 engineers based on your roadmap
  • Sprint-based delivery with weekly demos and retrospectives
  • 60-75% savings compared to Bay Area full-time AI compensation
  • Start within 2 weeks, 3-month minimum commitment

AI Feature Sprint

A 4-8 week sprint focused on shipping a specific AI feature in your product. Designed for SF startups that need to move fast and show investors a working AI capability. We deliver production-ready code, not prototypes.

Custom pricing based on your requirements

  • Defined AI feature scope with clear acceptance criteria
  • Production-ready code merged into your codebase
  • Model evaluation report with performance benchmarks
  • A/B testing framework for measuring user impact
  • Full documentation and knowledge transfer to your team
  • Investment: $40K-$120K depending on feature complexity

AI Technical Due Diligence

Expert evaluation of your AI systems, model performance, and technical architecture. Used by VCs evaluating AI companies and by companies auditing their own AI capabilities before major initiatives.

Custom pricing based on your requirements

  • Full audit of model performance, data quality, and infrastructure
  • Assessment of AI team capabilities and technical debt
  • Evaluation of model robustness and failure modes
  • Competitive analysis of AI approach vs market alternatives
  • Executive summary with findings and recommendations
  • Delivered in 2-4 weeks with a presentation to stakeholders
All plans include a free consultation and project assessment
FAQ

AI Services in San Francisco Questions Answered

Quick answers to the questions we hear most often.

Still have questions?

Can't find what you're looking for? Our team is here to help.

Contact us

We do not compete on pure research talent — SF has that covered. We compete on production AI engineering. Many SF companies have brilliant researchers but lack the engineers to deploy and maintain models in production. Our team specializes in MLOps, data engineering, and the unglamorous work of turning experiments into reliable, scalable AI systems.

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