# AI consulting and development in Charlotte, NC > Charlotte is the second-largest banking centre in the United States by assets, with energy and a growing fintech layer around it, and the buying culture is conservative, audit-first and unimpressed by a demo. If that describes your company, it describes how we build. Canonical: https://www.techfabric.com/ai-development-charlotte Serving: Charlotte, Concord, Gastonia, Rock Hill, Winston-Salem, Greensboro Headquarters: 1530 E Williams Field Rd, Ste 201, Gilbert, AZ 85295. No office in Charlotte. --- ## Why Charlotte work is our work The pilot works, and it's been parked since model risk asked how anyone would audit it, while your engineers went back to the roadmap. Our senior engineers build the version model risk can review, with agents that call the same governed actions a person would, retrieval that cites the policy behind each answer, and evals that score it. The bank towers along Tryon Street hold AI to that bar, as do Concord's race shops. ## AI consulting in Charlotte AI consulting in Charlotte, a banking centre with a Richmond Fed branch on East Trade Street, puts the model risk team in the first meeting. We document each proposed use case the way a bank's validation function expects, with data lineage, failure modes and the human checkpoint, so the projects that survive review are the ones we build. [AI consulting in Phoenix](https://www.techfabric.com/ai-consulting-phoenix) ## Work a buyer here would recognise The iLending work automated lead and loan officer assignment, rate sheet processing and stipulation document management, and improved CRM integration and API performance. Manual errors dropped and agent onboarding got faster, which is what a bank's lending operations team wants from automation sitting on the path of a credit decision. ## The regulator asks about the model Bank of America and Truist both keep their headquarters in Charlotte, and banks of that size answer to SR 26-2, the model risk guidance the Federal Reserve, the OCC and the FDIC issued in April 2026 to replace SR 11-7. It takes a risk-based approach tailored to each bank's model risk profile, and at a bank this size the automated credit and fraud decisions are the ones model risk looks at first. ## Questions ### How do you handle model risk management? An automated decision records what it was given, what it decided and under whose authority, because a bank's model risk team will ask for exactly that, whatever the regulators' guidance says is in scope. Explainability is designed in, since a system that did not record its reasoning cannot have it added later. ### Do you have an office in Charlotte? 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. ## 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. ## Working with us from Arizona 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. Most delivery is remote or hybrid. We travel for discovery, workshops and the opening weeks of a build.