AI consulting and development · San Francisco, CA
AI consulting and development for San Francisco companies
Startups in SoMa and Mission Bay shipped AI early, and many now run an agent that loses context on deploy, retrieval nobody can audit and a model bill nobody forecast, with no engineers spare to fix it. Our senior engineers build the durable half, with agent loops on a runtime that survives a deploy, RAG that cites what it read, evals that attach a number to every change, and cost instrumented from week one.
Founded
2017, and still independent
Bench
115+ engineers, 80 Databricks-certified
Experience
Fifteen years average, per engineer
Serving
San Francisco, Oakland, San Jose, Palo Alto
The Bay Area's software industry, from SoMa's AI labs down the 101 to San Jose, needs no introduction, and much of its AI work is now maintenance. If you built a system quickly and now have to make it durable, governed and affordable, that's the work we do here.
AzureTechFabric is a Databricks partner with an award-winning Databricks engineering team, 80 of them Databricks-certified, and a Temporal partner. Where a system reaches past the workspace we build on Azure, AWS and Google Cloud.
What ai consulting and development covers
Agents that hold state
A task that runs for three days, calls six systems and waits twice on a person cannot live in a request handler. We build agents on a durable runtime, so a deploy in the middle of a run does not lose the run, and a retry does not send the same email twice.
Retrieval that cites what it used
An answer nobody can trace is an answer nobody can defend. Retrieval is built so every response carries the documents behind it.
Evaluation, before and after
Without a score, every prompt change is a matter of opinion. We put deterministic scorers and model-based judges in front of the thing so a change ships with a number attached rather than with a feeling.
Human approval where it belongs
Some actions should stop and wait for a person. That gate is part of the system rather than a policy document, so an agent that wants to issue a refund pauses, the approval is recorded, and the run picks up where it left off.
Cost you can see
Model spend, retrieval infrastructure and the review time a system still needs are the numbers that decide whether it stays live after the first quarter. They get instrumented at build time, not discovered on an invoice.
Governed data access
An agent reads through the same grants a person would. On Databricks that is Unity Catalog, so a model that tries to reach a table it has no grant for fails the way any principal fails, rather than because the prompt asked it nicely.
AI consulting in San Francisco
Most of our AI consulting in San Francisco is for SoMa and Peninsula startups that built fast and now need the system to hold up. We audit the agents and retrieval already in production, measure accuracy and cost, and give you a prioritised list of what to fix, govern or rebuild.
The bill arrives before the governance does
A lot of San Francisco's AI systems went from demo to production inside a quarter, on a per-token model API and a vector database nobody budgeted. The bill shows up first. Then an enterprise customer's procurement team wants an audit trail, and from January 2027 the California Privacy Protection Agency's 2025 rules on automated decision-making technology will give consumers notice and opt-out rights when such a system makes a significant decision about them.
Switching to a cheaper model is the reflex, and it buys a month. The spend usually comes from the system's shape, with prompts that resend the whole context on every step, retries that restart an agent and no cache in front of retrieval. We'd fix durability and caching before touching the model, and record cost per request in the same trace an auditor will later read.
Measurement comes first. Every model call gets a trace carrying its cost, its inputs and the user it served, your existing evals run against it or we write the first twenty, and the spend gets ranked by workflow. Changes then go in order of money saved, and the trace stays on as the record a reviewer reads.
Clients we have done this for
Canvass is a production Cloudflare application for account research and campaign review. Campaign preparation, approval and delivery states are kept distinct, and per-tenant isolation is enforced in the data layer, which is the study to read if your own system now has to show who approved what.
Agentic GTM
Canvass
An application on Cloudflare that researches accounts and prepares outbound campaigns for Canvass's clients, where one bad email costs a prospect. A person approves every send, a run that dies halfway can't message a contact twice, and each email is checked again the moment it goes out.
Also for
How an engagement runs
01
Two weeks, fixed fee
An AI Readiness Assessment reads what you already run, names the use cases that will survive your data and your permissions, and puts a number on what each will cost to operate. It ends in a written document that is yours either way.
02
One system, named
We pick the first thing to build with you and say what done looks like before starting. A first system that reaches production beats three that reach a demo.
03
Build it with your team
Your engineers are in the repository with ours from the first week. The pattern has to be one they can extend after we leave, which does not happen if they meet it at handover.
04
Run it, then hand it over
We stay on the pager while it settles, because the design decisions look different when the person making them is the one being woken. Handover happens when it is boring, not when the invoice is due.
Two weeks, fixed fee, and a written plan whether or not you build it with us.
Tell an engineer what you want to build
A senior engineer reads it and replies within one business day, usually with the questions that decide the first two weeks.
Around San Francisco
Demo nights and the Palace Hotel
San Francisco has more AI evenings than anyone could attend. AI Tinkerers San Francisco runs demo nights, build nights and weekend hackathons several times a month, often in sponsors' offices, and AI Engineer brings its World's Fair to Moscone West from 29 June to 1 July 2027. AI Council, vendor-neutral and run since 2015 as Data Council, takes the Palace Hotel on 23 and 24 March 2027.
Most of what gets shown on demo nights is a first version. If you're the one keeping yours alive, two days of AI Council at the Palace Hotel are the closer fit, because it's aimed at engineers building AI and data infrastructure. Take your model bill and your eval coverage, and by the end of day one you'll know whether your problems are the ones SoMa's other teams already have.
The San Francisco-Oakland-Fremont metro area. California: Alameda, Contra Costa, Marin, San Francisco and San Mateo counties.
Lighter shape: the city itself. Dot: downtown. County boundaries from the US Census Bureau, 2023.
Conference · 23 to 24 March 2027
AI Council
Vendor-neutral since 2015, this is the conference for engineers maintaining AI and data infrastructure, so bring your model bill and your eval coverage.
AI Council, formerly Data Council. Palace Hotel, San Francisco.
Meetup · Several times a month, plus weekend hackathons
AI Tinkerers San Francisco
Demo nights here show first versions, and watching which ones survive a few months tells you more about production than the demos themselves.
AI Tinkerers, San Francisco chapter. Venues vary, often sponsors' offices in San Francisco.
Conference · 29 June to 1 July 2027
AI Engineer World's Fair 2027
Three days at Moscone West put founders and heads of AI in one place, a quick way to compare how other teams staff and run agents.
AI Engineer. Moscone West, SoMa.
Conference · 21 to 24 June 2027
Data + AI Summit
Four days on data engineering, analytics and AI, with an online option for engineers who can't leave the product for a week.
Databricks. Moscone North, West and South, 747 Howard St, SoMa.
University · Ongoing 12-month degree programme
MS in Data Science & Artificial Intelligence, University of San Francisco
USF's practicum pairs graduate students with outside partners, a reasonable route for a well-scoped analysis while your own engineers stay on the product.
University of San Francisco College of Arts and Sciences. USF Downtown Campus, 101 Howard St, San Francisco.
More in San Francisco (5)
- ODSC AI West 2026
A training conference for AI and data science practitioners, with a virtual-only day followed by three in-person days at the Hyatt Regency by SFO in Burlingame.
27 to 29 October 2026, with a virtual day on 26 October. Hyatt Regency SFO, Burlingame.
- AI Engineer Code Summit
A three-day conference for developers on coding agents, development tools and production workflows. Attendance is by application.
10 to 12 November 2026. San Francisco, venue announced to registrants.
- QCon San Francisco
A three-day software engineering conference for senior developers and architects, followed by two days of hands-on training.
16 to 18 November 2026, training 19 to 20 November. Hyatt Regency San Francisco, Embarcadero.
- GDG San Francisco
The San Francisco Google Developer Group runs monthly meetups on Google technologies including Android, web and AI, and co-hosts DevFest Bay Area.
Monthly. San Francisco, venues vary.
- PyBay
The Bay Area's regional Python developer conference, with talks on software development, AI, security and data science.
Annual, usually in the fall. Mission Bay Conference Center, San Francisco.
Each listing was checked on its organiser's own page on 12 October 2026. Dates and venues move, so the link is the source.
FAQ
AI consulting and development in San Francisco: common questions
Do you work with San Francisco companies?
Yes, and with companies across the United States. Arizona does not observe daylight saving, so from March to November we are on the same clock as you and from November to March we are an hour ahead. Either way the working day overlaps almost completely.
Do you have an office in San Francisco?
The office is in Gilbert, Arizona, with teams in Amsterdam, Dnipro and Hyderabad. Delivery is remote or hybrid, and we travel for discovery, workshops and the opening weeks of a build.
We built this fast and now the model bill is a problem.
That is the most common brief we get here. Model spend, retrieval infrastructure and the human review a system still needs get instrumented, and then the architecture questions become answerable. Usually the fix is durability and caching rather than a different model.
What kind of AI development do you do?
Agents that carry state across a long task, retrieval that cites what it used, workflow automation that survives a restart, and the evaluation that tells you whether any of it got better. The common thread is that it has to run unattended and be explainable afterwards, which is a different problem from getting a good answer once.
How does an AI engagement start?
Usually with something short and fixed so the cost is known before you commit. The AI Readiness Assessment runs two weeks for a fixed fee and ends with a written deliverable that is yours whether or not you continue.
Also in San Francisco
What else we build for San Francisco companies.
Software development in San Francisco
The system your startup shipped in a quarter now runs the business, every change takes longer because the people who wrote it have moved on, and you can't hire fast enough.
Databricks consulting in San Francisco
Your Snowflake bill, a vector database and a model spend nobody forecast all landed in one quarter, and finance wants to know which to cut.
AI consulting and development elsewhere
The same work, in other metros. The full list is here.
New York, NY
New York, Brooklyn, Queens
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Atlanta, Alpharetta, Marietta
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Philadelphia, King of Prussia, Wilmington
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Detroit, Ann Arbor, Dearborn
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Tampa, St. Petersburg, Clearwater
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Chicago, Naperville, Evanston
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