AI consulting and development · San Diego, CA
AI consulting and development for San Diego companies
An AI pilot that can't show what it read never gets past review into production. Our senior engineers build retrieval that cites the paper, spec, test record or contract behind an answer, agents that stop for a reviewer where the decision warrants it, and evals that produce a number someone can defend. San Diego's biotech, defence and wireless companies hold that bar highest, and a hospital or a property manager benefits too.
Founded
2017, and still independent
Bench
115+ engineers, 80 Databricks-certified
Experience
Fifteen years average, per engineer
Serving
San Diego, La Jolla, Carlsbad, Chula Vista
Biotechnology, defence contracting and wireless telecommunications, three industries with long development cycles and no tolerance for an unexplainable result. If you're in one of them, a model that informs a decision has to show what it read, and evaluation has to produce a number someone can defend.
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 Diego
AI consulting in San Diego starts with a low tolerance for unexplained results. Torrey Pines biotech, Navy contractors and Sorrento Mesa's wireless companies need a model that shows its sources and an evaluation that produces a defensible number, so we set the evaluation method before recommending any build.
Defence and biotech ask for the same replay
San Diego builds for two kinds of reviewer. Contractors supporting NAVWAR and the Navy commands around San Diego Bay now find CMMC requirements written into Defense Department contracts, and the labs on Torrey Pines Mesa answer to the FDA's 21 CFR Part 11 for electronic records. Both want access controlled and a result that can be reproduced from the records as they stood at the time.
The usual answer is a snapshot taken at month end and a spreadsheet saying which version fed which report. Reviewers stop trusting that the first time a number moves. We'd make reproduction a property of the platform, with every table versioned and every result tied to the version it read, so rerunning last March's analysis gives last March's answer without relying on anyone's memory.
We start with one result somebody has already been asked to reproduce, trace it back through the pipeline, move those tables onto Delta with retention set to cover your review cycle, and record lineage in Unity Catalog. If the rerun matches, that path becomes the pattern for the rest, and if it doesn't, the mismatch shows the real scope.
Clients we have done this for
Synapse and SQL Server gave way to Databricks Lakehouse and Lakebase at Location Services, cut over in reversible waves, and the rules engine that validates a repossession assignment on arrival now runs against governed lakehouse data. Any team whose decisions run on rules over a SQL Server history will find its own migration in that sequence.
Automotive / Fintech
Location Services
Location Services' repossession platform automates assignment intake, so fewer people handle each case, and our engineers built it with the company's own technology team. When it was time to leave Synapse and SQL Server, we moved it onto Databricks in waves that could each be rolled back, and the rules engine that checks every assignment came through unchanged.
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 Diego
San Diego's machine learning regulars
San Diego Machine Learning has met more than 550 times, every Saturday at noon in Rancho Bernardo, working through fine-tuning, dataset engineering, RAG and agents. GDG San Diego has held evenings at Google's Sorrento Valley office and run an ADK master class this year, plus an introductory AI workshop in Spanish. MLcon brings a commercial conference and expo to the Hyatt Regency La Jolla in late May 2027.
Retrieval against fine-tuning is a question those Saturdays keep returning to, and for a biotech team or a defence programme the choice decides what can be audited later. A retrieval system can show the paper or the spec behind each answer. A fine-tuned model can't, so its evaluation has to carry the weight, with a scored test set that a reviewer can rerun and get the same result.
The San Diego-Chula Vista-Carlsbad metro area. California: San Diego County.
Lighter shape: the city itself. Dot: downtown. County boundaries from the US Census Bureau, 2023.
Meetup · Weekly, Saturdays at noon
San Diego Machine Learning
Every Saturday this group works through fine-tuning, RAG and agents in detail, the right place to test whether retrieval or a tuned model suits your audit needs.
Volunteer organisers. Aquillius, 10918 Technology Pl, Rancho Bernardo.
Meetup · Every one to three months, weekday evenings
AI Tinkerers San Diego
Evenings every one to three months, some at Google, give a biotech or defence team a technical room to test a builder's claims about retrieval and evaluation.
AI Tinkerers, San Diego chapter. Venues vary in San Diego, including Google.
User group · Every one to two months, weekday evenings
GDG San Diego
Sessions on AI agents and Google's Agent Development Kit help a team on Google Cloud decide how much of an agent to build by hand.
Google Developer Groups volunteers. Venues vary, including Google, 6420 Sequence Dr, Sorrento Valley.
Conference · 24 to 28 May 2027, expo 25 to 26 May
MLcon San Diego 2027
Workshop days at MLcon in La Jolla suit an engineering lead who wants hands-on time with generative AI tooling before committing a budget.
S&S Media. Hyatt Regency La Jolla, 3777 La Jolla Village Dr.
Meetup · Weekly, Wednesday evenings
San Diego AI Meetup Group
A weekly Wednesday happy hour at Botanica in North Park, informal enough to compare notes on AI tools with whoever turns up.
Volunteer organisers. Botanica, 3139 University Ave, North Park.
More in San Diego (3)
- San Diego Python Users Group
A large Python user group with a monthly speaker night, also streamed live, and weekly online study groups on Saturdays.
Monthly, usually the fourth Thursday evening. Qualcomm Building Q, 6455 Lusk Blvd, Sorrento Mesa.
- Halıcıoğlu Data Science Institute (HDSI), UC San Diego
UC San Diego's data science institute runs research and degree programmes, including an MS in Data Science, a PhD, an online master's and undergraduate data science majors.
Ongoing. UC San Diego, 9500 Gilman Dr, La Jolla.
- Master of Advanced Studies in Data Science and Engineering (MAS-DSE), UC San Diego
A part-time, in-person master's in data science and engineering for working professionals, with classes held at weekends, taught with Computer Science and Engineering.
Ongoing degree programme. UC San Diego, La Jolla.
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 Diego: common questions
Do you work with San Diego 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 Diego?
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 need results a reviewer can reproduce exactly.
Replay is how. The event log is the source of truth and read models derive from it, so reproducing a result means replaying to a point rather than trusting that a snapshot was taken. Defence and biotech both need that and neither accepts an approximation.
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 Diego
What else we build for San Diego companies.
Software development in San Diego
When a project is stuck waiting for engineers, the shortcut is a vendor whose delivery team you never meet.
Databricks consulting in San Diego
Auditors keep asking for a result reproduced exactly, months later, from the data as it was, and your warehouse can only approximate it.
AI consulting and development elsewhere
The same work, in other metros. The full list is here.
New York, NY
New York, Brooklyn, Queens
Boston, MA
Boston, Cambridge, Somerville
Atlanta, GA
Atlanta, Alpharetta, Marietta
Miami, FL
Miami, Fort Lauderdale, Coral Gables
Charlotte, NC
Charlotte, Concord, Gastonia
Philadelphia, PA
Philadelphia, King of Prussia, Wilmington
Detroit, MI
Detroit, Ann Arbor, Dearborn
Tampa, FL
Tampa, St. Petersburg, Clearwater
Chicago, IL
Chicago, Naperville, Evanston
Phoenix · Dallas · Houston · Austin · Nashville · Minneapolis · Denver · Salt Lake City · Los Angeles · San Francisco · Seattle · Washington · Orlando · Raleigh · Portland