AI development · Phoenix metro
AI development in Phoenix, for systems that have to stay up
TechFabric is an AI development company in the Phoenix metropolitan area, with the office in Gilbert, Arizona. We build production AI systems for companies across Phoenix, Scottsdale, Tempe, Mesa and Chandler: agents that hold state, retrieval that cites its sources, and workflows that survive a restart.
Most AI work that reaches us has already been prototyped. The demo worked, and then it met real traffic, real permissions and a real audit question. That gap is what we are for.
Office
1530 E Williams Field Rd, Ste 201Gilbert, AZ 85295
Phone
+1 (480) 681-6806Serving
Phoenix, Scottsdale, Tempe, Mesa, Chandler
Bench
115+ people, 80 Databricks-certified
Why Phoenix companies work with us
Production, not a pilot
An agent that answers well in a notebook and loses its context on deploy is not a system yet. We build the durable half: state that survives a restart, retries that do not duplicate work, and a record of what the model was asked and what it did.
Permissions the model cannot talk its way around
An agent gets its data through the same grants a person would. On Databricks that means Unity Catalog, so an agent reaching a table it has no grant for fails the way any other principal fails, rather than because a prompt told it not to.
In the metro, not flying in
The office is in Gilbert, which is twenty minutes from Tempe and Chandler and an easy drive from the city. Workshops and the first weeks of a build are better in a room, and we can be in yours.
One senior team
115+ people, 80 of them Databricks-certified, averaging fifteen years of production experience each.
What ai development covers here
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, which is what makes the difference between a demo and something a regulated team is allowed to ship.
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.
Where we have done this work
Named sectors rather than a logo grid. Every engagement referenced here is written up on this site with the client named, because a claim you cannot check is not proof.
Financial services and lending
Loan origination and refinancing platforms where a wrong write costs money and somebody has to reconstruct afterwards what changed. Auto Approve, iLending and SWBC are on this site with the work named.
Sport and media
Live streaming and audience data, and pipelines that stitch 400M+ athlete records across sources with retries and real-time visibility.
Manufacturing and distribution
Estates that outgrew the systems underneath them, where the AI question arrives on top of a modernisation that has to happen anyway.
Consumer products
High-traffic commerce and the data work behind it, where a system is judged on the day the load arrives rather than on the average day.
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.
Production software, built from the Phoenix metro.
FAQ
AI development in Phoenix: common questions
Is TechFabric an AI development company in Phoenix?
Yes. We are in the Phoenix metropolitan area, at 1530 E Williams Field Rd, Ste 201, Gilbert, AZ 85295, and we build production AI systems for companies across Phoenix, Scottsdale, Tempe, Mesa and Chandler. The company was founded in 2017 and the team is 115+ people, 80 of them Databricks-certified.
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.
Do you work on site with Phoenix clients?
Where it helps. Discovery, workshops and the opening weeks of a build usually go better in a room, and Gilbert is a short drive from most of the metro. Steady delivery is mostly remote or hybrid, because that suits the work rather than the postcode.
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.
Do I need to be on Databricks to work with you?
No. The deepest work is on Databricks because that is where the bench is, but the AI systems we build run on Azure, AWS and Google Cloud too. What matters more than the platform is whether the thing has to be governed, durable and explainable, because that is what we are built around.