AI & Machine Learning Company in Charlotte

Ranges tied to scope, because the modeling is rarely the expensive part. A data and feasibility assessment, meaning source profiling, leakage audit, point-in-time reconstruction of a training set, and a baseline model with an honest read on whether the signal exists, typically runs USD 30,000 to 70,000 over four to eight weeks and is the single best money a Charlotte buyer spends. A production model with a full training pipeline, feature store integration, monitoring, serving, and documentation typically runs USD 110,000 to 300,000. A regulated decisioning model at a bank, where independent validation, fair lending testing, adverse action reason code design, and the complete model development document are in scope, runs USD 200,000 to 500,000 and takes longer than the modeling alone would suggest. A platform engagement building the MLOps foundation itself, covering feature store, registry, CI for models, monitoring, and a governance workflow, runs USD 250,000 to 750,000. Charlotte senior data science and ML engineering rates run roughly 25 to 40 percent below San Francisco and New York at equivalent seniority, though the banks bid aggressively against Raleigh and Atlanta for the same people. Everything is fixed fee against a signed statement of work.

SR 11-7, issued by the Federal Reserve on April 4 2011 and paired with OCC Bulletin 2011-12, sets the expectation, and it rests on three pillars: conceptual soundness, ongoing monitoring, and outcomes analysis, all subject to effective challenge from a validation group independent of the developers. Practically, the validation team will ask for a written rationale for the model choice and the feature set, evidence that the training data represents the population the model will score, sensitivity analysis showing how the output responds to input perturbation, benchmarking against at least one alternative approach, and a limitations register that states plainly where the model should not be used. They will try to reproduce your results, so we ship a container and a pinned dataset that regenerate every table and figure in the documentation from raw inputs. They will ask how you will know the model has degraded, which means named metrics, named thresholds, named owners, and a defined action when a threshold trips. Model tiering determines how much of this you face, and we push to establish the tier during discovery. Building the documentation as you go adds perhaps ten percent to the engineering effort. Reconstructing it afterward routinely adds a quarter to the calendar.

Two federal regimes drive the design. The Equal Credit Opportunity Act and Regulation B require that a declined applicant receive specific principal reasons for the decision, and the CFPB has said directly that complexity is not an excuse. Circular 2022-03, issued May 26 2022, states that creditors using complex algorithms must still provide accurate and specific reasons, and Circular 2023-03, issued September 19 2023, adds that a creditor cannot simply pick the closest entry from a sample reason list when the real driver is not on it. That rules out any architecture where reasons are reverse-engineered loosely from a black box, so we design reason codes as a first-class output using SHAP or an equivalent attribution method validated against the model's actual behavior, and we test the generated reasons for accuracy on held-out declines. The second regime is disparate impact. We run adverse impact ratio testing across protected class proxies where direct attributes are unavailable, using BISG or a comparable method with its limitations documented, and we search for less discriminatory alternatives with comparable performance because that search is itself part of what examiners expect to see. Fair lending counsel owns the legal conclusions; we build the evidence they need to reach one.

Yes, and the discipline is documentation more than mathematics. BSA and anti-money-laundering monitoring in a Charlotte bank is examined, so any change to detection scenarios or thresholds needs a defensible record of why it was made and what effect it had. We start with above-the-line and below-the-line testing on historical alerts, sampling productive and unproductive alerts around each threshold to quantify what tightening or loosening actually does to both false positives and missed suspicious activity. Machine learning enters as a triage and prioritization layer on top of rule-based scenarios rather than as a replacement for them, because scenario coverage is what examiners map against typologies. We keep the rules producing the alerts and use models to rank them, which preserves the regulatory narrative while cutting analyst time on the lowest-value queue. Every model-driven suppression or deprioritization is logged with the alert, the score, and the reason, and nothing is auto-closed without a documented policy and a sampling program that reviews suppressed alerts. Segment-level performance and model drift are monitored continuously, and we produce a change-control record for each retuning that your BSA officer can hand to an examiner without preparation.

Calibration by subgroup decides whether a clinical model ships, and discrimination metrics alone will not carry a governance committee. We evaluate readmission, deterioration, sepsis, no-show, and utilization models with calibration curves and net benefit analysis cut by age, sex, race, payer, and site of care, because a model that is well calibrated overall and badly calibrated for one campus will produce harm at that campus. Alert burden is treated as a design constraint, since a model that adds fifteen low-value alerts per clinician per shift will be ignored regardless of its AUC. Protected health information stays inside the system's own cloud tenancy under a business associate agreement, no PHI is used for supplier model training, and every inference is logged. If the model is surfaced through certified health IT, ONC's HTI-1 rule is directly relevant: certified Health IT Modules had to meet the decision support intervention criterion at 45 CFR 170.315(b)(11) by January 1 2025, including the source attribute transparency set for predictive interventions covering training data description, validation, fairness assessment, and update cadence. We produce that source attribute documentation as a deliverable rather than leaving your informatics team to reconstruct it.

Retraining on a calendar is a habit, not a policy, and it either wastes cycles or misses real degradation. We instrument three separate layers. Input drift watches feature distributions against the training reference using population stability index and Kolmogorov-Smirnov statistics per feature, with thresholds set from historical variation rather than from a textbook default, so seasonal features do not fire alerts every quarter. Prediction drift watches the score distribution and the approval or alert rate, which often moves before ground truth arrives. Performance monitoring watches the actual outcome metric once labels land, and for problems with long label delay such as credit default or asset failure we define proxy metrics that resolve sooner and document them as proxies. Each layer has named thresholds, a named owner, and a defined action: investigate, retrain, or roll back. Retraining runs through the same pipeline as the original build, produces a new model version in the registry, and goes through a champion-challenger comparison on a common holdout before promotion. At a bank, material retraining triggers a validation review, so we design the change-control record to answer that question in advance rather than reopening the whole model file.