Fractional AI Product Manager · Atlanta, GA

Hire an AI product manager in Atlanta.

Atlanta's fintech companies: Global Payments, NCR Voyix, Greenlight Financial, ship AI features into regulated product lines. Compliance documentation is not a post-launch task. OCC model risk reviews and CFPB adverse action guidance require model cards, fairness metrics, and decision audit trails. A spec that omits these produces a compliance finding, not a feature.

Logistics companies like UPS regional operations and Delta cargo face a different constraint. AI routing and scheduling features carry safety implications. The product spec must define failure modes and human override procedures before a dispatcher ever touches the interface.

Fixed engagement, fixed scope. Compliance documentation included from day one.

How we work

Tell us about your fintech or logistics AI feature.

The Atlanta product context.

Atlanta is one of the largest fintech hubs outside New York. Global Payments processes over 50 billion transactions annually. NCR Voyix runs point-of-sale and banking software for thousands of financial institutions. Greenlight Financial manages money accounts for families with children. Every AI feature touching financial decisions at these companies lands in a regulatory environment that consumer software does not face.

OCC examiners review model risk management documentation under the 2021 Interagency Guidance. The examiner does not ask for a demo. They ask for the model card, the validation report, and the audit trail. An AI PM who treats those as documentation tasks rather than spec sections produces them too late to be useful.

Delta Air Lines and UPS operate major logistics infrastructure from Atlanta. Their vendors and internal product teams ship AI features for routing, scheduling, and load optimization. These are not consumer features. A routing AI that fails silently can put a driver on a road that is closed or unsafe. The spec must define what the system does when the model is wrong.

A fractional AI PM for Atlanta companies writes the compliance requirements and safety requirements into the product spec alongside the functional requirements. They are not separate documents reviewed after engineering ships. They are sections in the spec that engineering builds to.

Financial AI: compliance in the spec, not after the launch.

Model cards, fairness metrics, and decision audit trails are product requirements. Writing them after engineering ships doubles the work and produces documentation that does not match what was built.

Model card as a spec section

The model card documents intended use, training data, performance metrics by demographic subgroup, and known failure conditions. It is written during product spec, reviewed by the model risk committee, and updated when the model version changes.

Fairness metric selection

Demographic parity, equalized odds, and calibration often conflict. The spec states which metric takes precedence for the specific use case, the measurement population, the pass threshold, and the ongoing monitoring cadence.

Decision audit trail specification

Every AI-influenced decision is logged with the model version, input features used, output, confidence score, and the user action that followed. Retention period matches the relevant regulatory requirement for the product category.

Adverse action compliance

CFPB adverse action requirements for AI-driven decisions require specific reason codes. The spec defines what explanation text accompanies each output type. Engineering builds the logging layer to that spec, not to a retroactive requirement.

Logistics: safety-critical feature specifications.

AI routing and scheduling features in logistics are not the same category as a recommendation engine for consumer products. A mislabeled product recommendation is a minor miss. An AI scheduling feature that routes a vehicle to a closed road or an overloaded bridge is a safety event. The spec must treat these differently.

A safety-critical AI product spec defines failure modes at the model level and the system level. Model-level failures: what happens when confidence is low, when input data is missing, or when the model returns a result outside the feasible range. System-level failures: what happens when the AI service is unreachable, when GPS data is stale, or when real-time traffic feeds are interrupted.

Human override procedures belong in the spec before the interface is designed. The procedure defines which role can override an AI recommendation, through which UI path, with what minimum documentation, and how that override is logged for model improvement. A dispatcher overriding a routing recommendation should generate a feedback signal, not just a support ticket.

Atlanta logistics companies shipping AI features into field operations face a specific constraint: the human operator on the ground has information the model does not have. The spec states explicitly that the human decision takes precedence in defined conditions. That is not a technical limitation to apologize for. It is a product requirement to document.

How we work.

Scope defined before any work starts

Discovery call to understand the feature, the regulatory context, and the relevant stakeholders. Written scope and fixed price before we start. No open-ended retainers.

Compliance requirements written in spec phase

Model cards, fairness metrics, audit trail specs, and override procedures are sections in the product spec. Not a separate compliance review after engineering finishes.

Deliverables your team owns

Every document produced in the engagement is yours. The spec, the model card template, the fairness metric framework, and the override procedure. Your team runs these processes after the engagement closes.

Questions from Atlanta fintech and logistics teams.

What does a model card for a fintech AI feature contain, and which regulators actually ask to see them?: A model card for a fintech AI feature documents six things: the model's intended use and explicit out-of-scope uses, the training data sources and date range, performance metrics broken down by demographic subgroup, known limitations and failure conditions, the evaluation methodology, and the fairness metrics used. OCC examiners reviewing AI-assisted underwriting or risk scoring tools have asked to see model cards under the 2021 Interagency Guidance on Model Risk Management. The CFPB has cited model card absence as a factor in adverse action notice inadequacy findings. At Global Payments and NCR Voyix, internal model risk committees use model cards as the primary artifact in model validation reviews. A model card written after the feature ships is harder to defend than one written during the product spec phase, because the design decisions are already locked.
How do you define fairness metrics for a financial services AI product?: Fairness metrics for a financial services AI product depend on the regulatory context and the specific decision the model influences. For a credit scoring or risk classification feature, the standard metrics are demographic parity (equal approval rates across groups), equalized odds (equal true positive and false positive rates across groups), and calibration (predicted probabilities match actual outcomes across groups). These three metrics often conflict. A model that satisfies demographic parity may violate equalized odds. The product spec must state which metric takes precedence and explain why, based on the specific harm profile of the use case. At Greenlight Financial, a feature that restricts teen spending needs different fairness metrics than one that classifies business transactions. The spec defines the metric, the measurement population, the threshold, and the cadence of ongoing monitoring.
What does a human override procedure look like in a logistics AI product spec?: A human override procedure in a logistics AI product spec covers four things. First, the trigger conditions: which AI outputs are flagged for human review before execution, based on confidence score, output type, or route characteristics. Second, the override interface: which role (dispatcher, fleet manager, safety officer) can override a specific AI recommendation and through which UI path. Third, the override log: every override is recorded with the user ID, the original AI recommendation, the override decision, and the stated reason. Fourth, the feedback loop: overrides above a threshold frequency for a specific route type or output category trigger a model retraining review. At UPS and Delta cargo operations, the override procedure is also a safety document. The spec must define what happens when an AI routing recommendation conflicts with a driver's direct knowledge of a road condition. The human wins. The spec says so explicitly.
How do you write explainability requirements for a financial AI feature reviewed by a compliance examiner?: Explainability requirements for a financial AI feature reviewed by a compliance examiner address three audiences: the end user, the internal compliance team, and the external examiner. For the end user, the spec defines what explanation text accompanies every AI-generated output and what level of detail is appropriate for the product context. For the internal compliance team, the spec defines what explanation data is logged for each decision and how long it is retained. For the external examiner, the spec defines what the institution can produce on request: a feature contribution breakdown for a specific decision, the model version active at the time of that decision, and the training data vintage. CFPB adverse action requirements for AI-driven decisions require a reason code or explanation that is specific enough to be actionable. Writing this as a product requirement before engineering builds the logging layer costs half a day. Adding it after the feature ships requires a retroactive logging infrastructure project.

Start with a discovery call.

Describe the fintech or logistics AI feature you are building. We reply within one business day with a rough scope and price range.

Hire an AI product manager in Atlanta.

Fixed engagement, fixed scope. Compliance documentation included from day one.

The Atlanta product context.

Financial AI: compliance in the spec, not after the launch.

Model cards, fairness metrics, and decision audit trails are product requirements. Writing them after engineering ships doubles the work and produces documentation that does not match what was built.

Model card as a spec section

Fairness metric selection

Decision audit trail specification

Adverse action compliance

Logistics: safety-critical feature specifications.

How we work.

Scope defined before any work starts

Discovery call to understand the feature, the regulatory context, and the relevant stakeholders. Written scope and fixed price before we start. No open-ended retainers.

Compliance requirements written in spec phase

Model cards, fairness metrics, audit trail specs, and override procedures are sections in the product spec. Not a separate compliance review after engineering finishes.

Deliverables your team owns

Questions from Atlanta fintech and logistics teams.

What does a model card for a fintech AI feature contain, and which regulators actually ask to see them?

A model card for a fintech AI feature documents six things: the model's intended use and explicit out-of-scope uses, the training data sources and date range, performance metrics broken down by demographic subgroup, known limitations and failure conditions, the evaluation methodology, and the fairness metrics used. OCC examiners reviewing AI-assisted underwriting or risk scoring tools have asked to see model cards under the 2021 Interagency Guidance on Model Risk Management. The CFPB has cited model card absence as a factor in adverse action notice inadequacy findings. At Global Payments and NCR Voyix, internal model risk committees use model cards as the primary artifact in model validation reviews. A model card written after the feature ships is harder to defend than one written during the product spec phase, because the design decisions are already locked.

How do you define fairness metrics for a financial services AI product?

Fairness metrics for a financial services AI product depend on the regulatory context and the specific decision the model influences. For a credit scoring or risk classification feature, the standard metrics are demographic parity (equal approval rates across groups), equalized odds (equal true positive and false positive rates across groups), and calibration (predicted probabilities match actual outcomes across groups). These three metrics often conflict. A model that satisfies demographic parity may violate equalized odds. The product spec must state which metric takes precedence and explain why, based on the specific harm profile of the use case. At Greenlight Financial, a feature that restricts teen spending needs different fairness metrics than one that classifies business transactions. The spec defines the metric, the measurement population, the threshold, and the cadence of ongoing monitoring.

What does a human override procedure look like in a logistics AI product spec?

A human override procedure in a logistics AI product spec covers four things. First, the trigger conditions: which AI outputs are flagged for human review before execution, based on confidence score, output type, or route characteristics. Second, the override interface: which role (dispatcher, fleet manager, safety officer) can override a specific AI recommendation and through which UI path. Third, the override log: every override is recorded with the user ID, the original AI recommendation, the override decision, and the stated reason. Fourth, the feedback loop: overrides above a threshold frequency for a specific route type or output category trigger a model retraining review. At UPS and Delta cargo operations, the override procedure is also a safety document. The spec must define what happens when an AI routing recommendation conflicts with a driver's direct knowledge of a road condition. The human wins. The spec says so explicitly.

How do you write explainability requirements for a financial AI feature reviewed by a compliance examiner?

Explainability requirements for a financial AI feature reviewed by a compliance examiner address three audiences: the end user, the internal compliance team, and the external examiner. For the end user, the spec defines what explanation text accompanies every AI-generated output and what level of detail is appropriate for the product context. For the internal compliance team, the spec defines what explanation data is logged for each decision and how long it is retained. For the external examiner, the spec defines what the institution can produce on request: a feature contribution breakdown for a specific decision, the model version active at the time of that decision, and the training data vintage. CFPB adverse action requirements for AI-driven decisions require a reason code or explanation that is specific enough to be actionable. Writing this as a product requirement before engineering builds the logging layer costs half a day. Adding it after the feature ships requires a retroactive logging infrastructure project.

Start with a discovery call.

Describe the fintech or logistics AI feature you are building. We reply within one business day with a rough scope and price range.