AI
AI consulting and development, built to survive production.
TechFabric builds AI systems that run in production: agents with governed data access, workflows that survive a restart, and the evaluation harness that tells you whether any of it is working. We run our own delivery on the same stack, so the patterns we recommend are ones we operate.
Most AI work stalls in the same place. The demo is convincing, and then somebody asks what happens when the model times out halfway through a refund, or who approved the thing it did at two in the morning, or whether last month's version was better. We build the system around the model.
- Durable
- A run survives a restart, a deploy and a provider outage, and picks up where it stopped rather than starting again
- Governed
- An agent runs under its own service principal and inherits Unity Catalog grants, so it cannot read what its caller could not
- Measured
- A fixed set of cases with known answers, scored on every change, so quality is a number rather than whoever spoke last
The demo worked. Everything after it did not.
Almost nobody gets stuck building the model. They get stuck at the point where somebody responsible has to be able to answer for what it did, and that shows up in three ways.
It reads things its user could not
The prototype connects with a service account that can see everything, because that was the fastest way to make it work. Now the answer it gives a salesperson quietly includes a row from payroll, and the fix is not a prompt.
It dies halfway and nobody notices
A run that takes forty minutes and waits on a person does not survive a deploy. It fails somewhere in the middle, the retry starts again from nothing, and the only evidence is a customer asking why their refund never arrived.
Nobody can say whether the change helped
Somebody swaps the model, or edits a prompt, and the team argues about whether it got better. Without an evaluation set the argument is settled by whoever is most confident, which is a poor way to run anything.
All three come from the same gap. The model was the project, and the system around the model was left for later, so permissions, durability and measurement each arrived as an afterthought or not at all.
The shift
Three ways this goes, and only one of them ships
Same ambition, same models available to everybody. What differs is the order the work is done in.
Promote the prototype
Fast, and it stays a prototype
Buy a platform first
Governed, and nothing ships
Build the system around the model
Slower to the first demo, and it survives contact
What actually gets built
Six parts, in your environment, on your cloud. Most teams need four of them and already have one, so the first job is working out which.
Retrieval that cites its source
Every answer comes back attached to the passage behind it, so checking it means reading the source instead of trusting the summary. This is also what makes an answer defensible to somebody who was not in the room.
Permissions the agent inherits
On Databricks the agent runs under Unity Catalog grants, so it cannot read what its caller could not. Row filters and column masks apply without anybody reimplementing them in application code, which is where they otherwise drift.
A runtime that survives a restart
Long runs and human waits are the normal shape of this work, and both outlast a deploy. Durable execution means the run resumes where it stopped, and the history of what happened is a property of the system rather than something somebody remembered to log.
A place a person says yes
Some decisions should never be automatic. The approval step is part of the workflow, with the context that decision needs on the same screen, and the record of who approved what kept alongside the run.
An evaluation harness
A held-out set, scored on every change, so the question of whether a new model helped has an answer rather than an advocate. Without this you cannot safely upgrade, which means you are stuck on whatever you launched with.
Model routing you can change your mind about
Providers are swapped through a registry rather than a rewrite, because the best model for a workload will not be the best one in six months. On Databricks that is Unity Gateway.
Work this is built out of
Three systems we built and operate. Fabric and AutoFabric are our own products; Canvass we built and run for a client. Each one has a case study rather than a logo.
AI systems · Our own product
Fabric
Your project's quality shouldn't depend on which engineer is on it. Our engineers work inside Fabric, an AI system with more than fifty agents that remember each client project's context, and across our clients it's why delivery work that took a team of ten now takes three.
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.
Automotive finance · Our own product
AutoFabric
Origination and refinance steps that slow a deal down, such as credit pulls, VIN decode, deal structuring, stipulations and payoff quotes, run by agents in the lender's own environment. It came out of years of origination and refinance work for lenders and dealer groups. It connects to DealerTrack, RouteOne and CDK, and every action is checked against policy and recorded for the examiner.
Two ways to start, both fixed fee
Fixed length, fixed price, and a written deliverable whether or not you do anything else with us. Which one you want depends on whether the question is what to build or what you are already paying for.
Two weeks
AI Readiness Assessment
Most AI programmes stall on data the business does not trust, permissions no one can explain, and a platform where getting an agent to production means six approvals and a service account somebody created in 2023. Two weeks tells you which of those you have, named specifically enough that an engineer could start on the fix the following Monday.
Book a readiness assessmentTwo weeks
AI Tools Assessment
Three years of buying has left most companies with Copilot seats, a ChatGPT tenancy, a coding assistant per team, and four pilots nobody measured. Finance holds the contracts, IT admins hold the Microsoft 365 usage reports, and the GitHub and OpenAI data sits behind APIs a platform team has to query, so nobody has the whole picture or the week it takes to put it together.
Book a tools assessmentAnd four ways the work continues
Agentic AI development
Engineering leaders whose agent pilot cannot get past review
AI assistants and enterprise search
IT and data leaders asked for a company assistant that can't leak
AI workflow automation
Operations and engineering leaders with a process that half-completes
AI enablement
Engineering leaders whose team needs to own this, not outsource it
AI transformation
Executives with a mandate, a budget, and fifteen pilots
What we build on
The accelerators are free, open source, and run in your workspace
These are not products for sale. They exist because we needed them on client work, they are what makes the first agent take weeks instead of quarters, and they deploy into your own account rather than ours.
TechFabric Platform
Platform is how the applications your team builds on Databricks keep an audit trail. Every change those applications make, by a person or an AI agent, has to pass a policy before it happens and is recorded with who made it and why, so when someone asks who changed a record and on whose authority, the answer is a query.
TechFabric Harness
Harness is how your team runs AI agents on your Databricks data safely in production. Each agent can only read what Unity Catalog grants it, stops for a person's approval before the actions that need one, and keeps working through a restart instead of losing the run.
Fabric Tower
Tower is the console your people use to supervise AI agents working on your Databricks data. They see every run, approve anything that would leave the workspace, and can pause or redirect one agent without turning the whole service off.
TechFabric Experiments
Experiments is how your data team proves a new model or agent beats the one in production before it ships. It scores every change on your own Databricks data, so you can show the business a number instead of an opinion, and a version that fails its quality gate doesn't reach production.
FAQ
AI development: common questions
What are AI development services?
Building the system around the model rather than the model itself. In practice that is retrieval and context, permissions so an agent cannot read what its caller could not, durable execution so a long run survives a restart, human approval where a decision needs one, and evaluation so you can tell whether a change made things better.
Most teams can get a prototype working. The engineering is in everything after.
Do you build on a specific model or provider?
No. Model choice changes every few months and a system tied to one provider is a system you rebuild. Our agent framework routes through a provider registry, and on Databricks that is Unity Gateway. We will have an opinion about which model fits your workload, and it is not an opinion worth hard-coding.
We have a pilot that works in a demo and nowhere else. Can you help?
That is the most common way this starts. Usually the demo is fine and the gap is durability, permissions, cost, or the fact that nobody can measure whether it is any good. We work out which of those it is before proposing anything, because the four have very different answers.
Do we have to be on Databricks?
No. Databricks is where we go deepest and where the governance story is strongest, because an agent can inherit Unity Catalog permissions instead of routing around them. We also build on Azure, AWS, Google Cloud and Cloudflare, and plenty of this work starts before a lakehouse exists.
How do you price AI work?
The assessments are fixed fee and fixed length, so you know the cost before the first meeting. Build work is scoped after one of those, or after a conversation if you already know what you need. We would rather write a scope we can defend than quote a number against a brief nobody has read properly.
Where we are
TechFabric is an AI development company in the Phoenix metropolitan area, founded in Gilbert, Arizona in 2017 and still run from here. Most of our work is national, and for companies across Phoenix, Scottsdale, Tempe, Mesa and Chandler we are a short drive rather than a flight.
Office
1530 E Williams Field Rd, Ste 201Gilbert, AZ 85295
Phone
+1 (480) 681-6806Serving
Phoenix, Scottsdale, Tempe, Mesa, Chandler
AI development in Phoenix
An AI development company in the Phoenix metro. Production agents, retrieval and workflow automation, built in Gilbert AZ for Phoenix, Scottsdale and Tempe.
AI consulting in Phoenix
AI consulting for Phoenix-area companies, from a Gilbert AZ office. Readiness assessments, tool and spend audits, and a build team that ships what it advises.