AI & Machine Learning Company in Raleigh

Ranges tied to scope, because the same words describe very different projects. A feasibility and baseline engagement, meaning data assessment, leakage audit, a baseline model, an honest error analysis against the incumbent process, and a go or no-go recommendation, typically runs USD 25,000 to 60,000 over four to eight weeks, and it is the phase most likely to save you money by killing a bad idea early. A production model deployment with feature pipelines, training and serving parity, a model registry, monitoring, and retraining automation typically runs USD 90,000 to 260,000 depending on how many upstream systems have to be integrated and how much of the data engineering already exists. A platform engagement, meaning a shared feature store, multiple models under one governance framework, and a validated MLOps pipeline, runs USD 260,000 to 700,000 and up across phases. Computer vision inside a plant adds cost for edge hardware, lighting and fixturing work, and image collection. GxP validation adds documentation effort that scales with intended use, not with model complexity. Triangle senior engineering rates run roughly 25 to 40 percent below San Francisco and New York equivalents, though SAS, IBM, Cisco, and the biologics plants keep the local market above most secondary US metros. We quote fixed fee against a signed statement of work.

North Carolina has no comprehensive consumer data privacy statute. Comprehensive privacy bills have been introduced in recent General Assembly sessions without passing, so there is currently no state-level equivalent to Virginia's VCDPA or Colorado's CPA that would give you a statutory basis for training-data minimization, opt-out handling, or sensitive-data consent. That has two consequences. First, your obligations come from federal sectoral law: HIPAA for protected health information, GLBA and the FTC Safeguards Rule for financial data, FERPA for education records, COPPA for anything touching children under thirteen. Second, if you serve customers outside North Carolina, their state laws follow the data into your training corpus, so a Raleigh company training on customer data from California, Virginia, Colorado, Connecticut, or Texas inherits deletion and opt-out obligations that must propagate into feature stores and training snapshots, not just the operational database. The Identity Theft Protection Act at Chapter 75, Article 2A still governs breach response, including notice to the Attorney General's Consumer Protection Division, and G.S. 75-1.1 supplies unfair and deceptive trade practice exposure, with treble damages available under G.S. 75-16. We design retention and deletion propagation for the strictest jurisdiction in your customer base.

Under HIPAA there are exactly two defensible paths and we pick one explicitly in discovery. Safe Harbor removes the eighteen enumerated identifiers, including all elements of dates other than year for dates directly related to an individual, and all geographic subdivisions smaller than a state except the initial three digits of the ZIP code where the area those digits cover contains more than 20,000 people. It is fast, but stripping dates to the year destroys the temporal granularity most clinical models depend on. Expert Determination has a qualified statistician certify that the re-identification risk is very small given the recipient and the release context, and it lets you keep day-level dates and richer geography at the cost of a formal analysis and a written opinion. For readmission, no-show, and capacity models at Duke Health, UNC Health, or WakeMed we usually recommend Expert Determination or a limited data set under a data use agreement, because a model that cannot see the interval between discharge and follow-up is not a useful model. If substance use disorder treatment records from a Part 2 program are in scope, 42 CFR Part 2 imposes consent requirements that HIPAA de-identification does not satisfy on its own, and those records get segregated. Free-text notes get a named entity scrubbing pass with manual audit sampling, because dates and provider names hide in narrative.

Yes, and in the Triangle it is a routine requirement rather than an exception. Biologics plants in Clayton, Holly Springs, and Wilson, the Wolfspeed silicon carbide operations in Durham, and the Toyota battery plant in Liberty all run production networks that do not forward process data or inspection imagery to hosted APIs. We deploy inference at the edge using ONNX Runtime or Triton on plant-side hardware, keep the model artifact under configuration control, and pipe only aggregated results and monitoring telemetry back across the network boundary. For GxP scope, meaning any model whose output influences a batch record, a deviation disposition, an environmental monitoring excursion, or a release decision, we write an intended-use statement, a risk assessment scaled under FDA computer software assurance thinking, and installation, operational, and performance qualification evidence, with 21 CFR Part 11 controls on the electronic records and signatures involved. Model versions are configuration items under change control, so retraining is a change event with a documented approval, not a silent redeploy. The model produces a recommendation with evidence; a qualified person makes the call. That division is what makes the system approvable.

Degradation is the default outcome, so monitoring is part of the build rather than a follow-on project. We instrument four separate things because they fail differently. Data quality monitors catch schema drift, null-rate shifts, and upstream pipeline changes, which cause most incidents that get misdiagnosed as model decay. Input distribution monitors track covariate shift on the features that carry the most model weight, using population stability index and per-feature distance metrics with thresholds set from the training window rather than from a default. Prediction distribution monitors catch shifts in output even when ground truth is delayed, which matters for credit and clinical models where labels arrive months later. Outcome monitors compare predictions to realized labels once available and recompute the performance metric the business signed off on. Every model ships with a documented retraining trigger tied to those thresholds, a champion-challenger path so a new candidate runs in shadow before promotion, and a rollback to the previous registered version that we exercise once during deployment so nobody is learning the runbook during an incident. Monitoring alerts route into the observability stack your operations team already watches.

Codazz delivers to Triangle clients from Edmonton, Canada and Chandigarh, India. There is no Research Triangle office and we do not pretend there is one. The practical arrangement is built around Eastern Time. Edmonton sits on Mountain Time, two hours behind Raleigh, so our senior engineers join a 9:00 AM ET standup at 7:00 AM MT and stay overlapped through the Raleigh working afternoon, with Thursday reviews at 2:00 PM ET. Chandigarh is nine and a half hours ahead of Eastern Daylight Time, which is unusually well suited to machine learning work: training runs, hyperparameter sweeps, backfills, and evaluation batches execute overnight in Raleigh terms and results are ready to review when the East Coast morning starts. Where data cannot leave a jurisdiction or a perimeter, meaning PHI environments, GxP plant networks, or programs with US-person access requirements, we scope training and inference to US-region infrastructure with access controls that match the constraint, and offshore engineering works on the layers outside that boundary through clean interfaces. That split is designed with your compliance function during discovery.