AI & Machine Learning Company in Pittsburgh

The honest structure is three bands. A feasibility and baseline engagement, where we assess data quality, build a defensible baseline, quantify realistic lift and tell you whether the project should proceed, typically runs USD 25,000 to 60,000 over four to eight weeks, and a meaningful share of those end with a recommendation not to build. A production model with real feature pipelines, validation documentation, serving infrastructure, monitoring and a retraining path typically runs USD 90,000 to 250,000 depending on how many upstream systems have to be integrated and how heavy the regulatory review is. A platform engagement, meaning a feature store, experiment tracking, model registry, CI for models, drift monitoring and multiple models in production, runs USD 250,000 to 700,000 and up. The variable that moves these numbers most is not model complexity, it is label quality. A project with clean, plentiful labels lands at the bottom of its band; a project where labels have to be manually adjudicated by clinicians, analysts or field supervisors can double the timeline before a single model is trained, so we price the labeling effort explicitly as its own line rather than burying it. We quote fixed fee against a signed statement of work.

There is no Pennsylvania AI statute and no Pennsylvania comprehensive privacy statute, and you should be suspicious of anyone who tells you otherwise. A consumer privacy bill has been moving through the General Assembly without reaching enactment, so the answer for a model you are training this quarter is that no state privacy regime governs it. Governor Shapiro's Executive Order 2023-19, signed September 20, 2023, governs generative AI inside Commonwealth executive agencies and created a Generative AI Governing Board, but it binds state government rather than your company. That distinction matters for modeling specifically, because the absence of a state statute means nothing about your model is exempt from scrutiny; it means the scrutiny arrives through federal sector regulators and through contract terms your customers impose, both of which ask harder questions about a model than a general privacy act would. What does apply: the Breach of Personal Information Notification Act as amended by Act 151 of 2022, effective May 2023, which expanded personal information to include medical and health insurance information; the Unfair Trade Practices and Consumer Protection Law, which the Attorney General uses against deceptive claims about what a model does; Pennsylvania Insurance Department Notice 2024-04, issued April 6, 2024, adopting the NAIC model bulletin on insurer AI use; and the federal sector regimes that carry most of the weight, meaning HIPAA, ECOA and Regulation B, GLBA, FERPA and FDA device rules.

You build the validation artifacts while you build the model, not afterward. The governing framework examiners use is the 2011 supervisory guidance issued as SR 11-7 by the Federal Reserve and OCC Bulletin 2011-12, which treats a model as needing a documented purpose, a conceptual soundness argument, evidence of outcome analysis, ongoing monitoring, and validation performed by someone independent of the developer. In practice that means a written model development document covering data lineage, variable selection rationale and every variable rejected and why, benchmark comparison against a simpler model, sensitivity analysis, stability testing across time periods and segments, and a clear statement of limitations and known failure modes. For credit decisions, Regulation B under the Equal Credit Opportunity Act requires that an adverse action notice give specific principal reasons, and federal consumer-protection guidance has been explicit that model complexity is not an excuse for a vague reason code. That constrains architecture: we favor models whose reason codes are derivable and defensible, and where a complex model genuinely outperforms, we build the reason-code layer as a first-class component with its own tests rather than bolting SHAP values on at the end.

Yes, and the process is slower and more structured than commercial work, which is correct. Everything starts with a signed business associate agreement, a minimum-necessary data scope and, for research use, institutional review board review. Where the use case allows, we work on de-identified or limited data sets so that the sensitive-data footprint stays small. Protected health information stays in US-region infrastructure, no PHI is used to train third-party models, and every training run and inference is logged. Model evaluation includes subgroup performance reporting across the demographic and clinical strata your quality team cares about, calibration analysis rather than AUC alone, and a prospective silent-evaluation period before any output reaches a clinician. The compliance line worth understanding early is whether the model becomes a regulated device: a model that provides a specific clinical recommendation the clinician cannot independently review may fall under FDA software-as-a-medical-device rules, while a model that surfaces information the clinician can verify usually does not. We raise that question in discovery with your regulatory affairs team. For Highmark-side payer models, Pennsylvania Insurance Department Notice 2024-04 adds written AI governance expectations.

Monitoring is the deliverable, not an afterthought. Every model we put into production ships with four layers. Input monitoring watches feature distributions for drift using population stability index and distributional distance measures, with thresholds set from historical variation rather than a default someone copied from a blog. Output monitoring watches the score distribution, approval or flag rates and calibration against realized outcomes as labels arrive, which for credit and clinical models can lag by months, so we build a delayed-label reconciliation job rather than pretending outcomes are immediate. Operational monitoring watches latency, error rates, feature-pipeline freshness and training-serving skew, because most production model failures are actually data pipeline failures wearing a costume. Business monitoring tracks the decision metric the model was built to move, which is the only number that tells you whether the model still earns its keep. Alerts go into the same on-call rotation your platform team already runs, not a separate dashboard nobody opens. Each model has a written retraining trigger, a scheduled review date and a decommission condition agreed in advance, so retiring a model is a normal event rather than an argument.

Start at the cheapest end and move only when evidence forces you. For most Pittsburgh enterprise use cases the answer is retrieval over your own documents with a strong hosted model, because the failure mode you actually care about is the model not knowing your policies, your codes, your contracts or your equipment history, and retrieval fixes that directly while keeping the content updatable without a training run. Fine-tuning earns its cost in three situations: when you need a consistent output format or house style that prompting keeps drifting away from, when latency or unit cost at volume makes a smaller tuned model cheaper than a large hosted one, and when the domain language is genuinely far from anything in the pretraining data, which happens with subsurface engineering notation, metallurgical terminology and some clinical coding work. Full pretraining is almost never justified outside a research setting. There is a fourth path that matters here: when data cannot leave your network at all, the calculus changes and a self-hosted open-weight model with LoRA adaptation becomes the only option, and we size the accuracy trade-off honestly before you commit to it.