AI development · Washington, DC
AI development for Washington companies
Washington runs on the federal government and the contractors, associations and law firms arranged around it, with a cybersecurity concentration in Northern Virginia and a biotech corridor up the Maryland side that both answer to the same agencies.
Founded
2017, and still independent
Bench
115+ people, 80 Databricks-certified
Experience
Fifteen years average, per engineer
Serving
Washington, Arlington, Alexandria, Bethesda
An AI system that touches federal work has to show its authorisation boundary, its data handling and its decision trail before anyone asks, because the assessor will. We build the trail as a property of the runtime, with the model bounded by the same permissions as the person it acts for.
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 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.
Clients we have done this for
Fabric is our own governed platform: every mutation policy-gated, auditable and replayable. It is the control model federal work asks for, running in production.
AI systems · Our own product
Fabric
Fabric is a context store, memory and agent runtime built on Databricks, and it is the clearest evidence of what we build: across our clientele, work that took a team of ten now takes three.
The authorisation boundary decides the architecture
FedRAMP, NIST 800-53 and CMMC each want to know where the boundary is, what crosses it, and who approved the crossing. A system designed with the boundary drawn first is cheaper to authorise than one where it is drawn around whatever got built.
None of that is unusual for Washington, and none of it is a reason to build slowly. It is a reason to decide where the evidence comes from before the first handler is written, because the alternative is reconstructing it later from logs that were not designed to answer the question.
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.
Headquarters
1530 E Williams Field Rd, Ste 201Gilbert, AZ 85295
Phone
+1 (480) 681-6806Also
Amsterdam, Dnipro, Hyderabad
Working hours
Washington business hours, most days
FAQ
AI development in Washington: common questions
Do you work with Washington companies?
Yes, and with companies across the United States. Arizona does not observe daylight saving, so we are two hours behind you from November to March and three hours behind from March to November. That leaves most of your afternoon overlapping our morning, which is usually enough.
Do you have an office in Washington?
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.
Can you build inside a FedRAMP or CMMC environment?
We build inside authorised boundaries and design so the evidence an assessor wants is produced by the platform: least privilege, an event trail on every change, grants that resolve at read time. The authorisation itself is yours and your assessor's, and we do not claim it on your behalf. What we do is make the package easy to assemble.
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.
AI development elsewhere
Other metros we publish this for. 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