Feature Store Product Management: Designing Reusable Intelligence That Compounds

Across my AI product management career — from enterprise AI infrastructure at 2021.ai, to demand forecasting and procurement optimization at HelloFresh, to real-time credit scoring, to AI visibility systems at Azoma.ai — I’ve learned that most AI organizations do not fail because of weak models.

They fail because intelligence is fragmented.

The Feature Store layer is where raw behavioral data becomes reusable intelligence. It is the structural layer that determines whether AI scales across products or remains trapped in silos.

As a Feature Store PM, my focus has consistently been:

  • Designing reusable behavioral primitives

  • Standardizing intelligence across teams

  • Preventing duplication

  • Enabling faster experimentation

  • Creating compounding advantage

Moving From Features to Shared Intelligence

Early in my AI platform work at 2021.ai, I observed a common pattern across enterprise clients:

Each ML team engineered its own features.

Credit teams built repayment stability signals.
Forecasting teams built demand velocity metrics.
Compliance teams engineered behavioral risk indicators.

These signals often relied on the same underlying raw data — but were implemented differently, named differently, and calculated differently.

This fragmentation reduced:

  • Model portability

  • Evaluation consistency

  • Cross-domain intelligence reuse

  • Experimentation speed

My role was to shift the organization from “feature engineering per model” to “behavioral signal engineering per entity.”

Instead of asking, “What features does this model need?”
We asked, “What behavioral intelligence should exist at the platform level?”

That reframing is the core responsibility of a Feature Store PM.

Designing Entity-Centric Intelligence

Across multiple systems — credit scoring, demand forecasting, compliance monitoring, generative AI RAG platforms — I pushed for entity-centered feature design.

Every AI-native company revolves around a small set of entities:

  • User / Retailer / Customer

  • Product / SKU / Asset

  • Transaction / Interaction

  • Document / Query

  • Supplier / Counterparty

A feature store should define intelligence at the entity level.

For example:

In credit risk systems, instead of building ad-hoc repayment features per model, we standardized:

  • Repayment consistency score

  • Revenue growth stability

  • Volatility index

  • Order frequency delta

  • Engagement decay score

These features were then reused across:

  • Credit approval models

  • Credit limit adjustment models

  • Churn prediction models

  • Fraud detection systems

The marginal cost of launching new predictive surfaces decreased significantly because the intelligence foundation already existed.

Reusability is leverage.

Accelerating Iteration Through Standardization

At 2021.ai, when building ML observability and model lifecycle platforms, I worked closely with ML engineers to ensure feature definitions were versioned, documented, and reproducible.

This enabled:

  • Faster experimentation cycles

  • Consistent offline/online feature alignment

  • Reliable model comparisons

  • Reduced debugging time

Without a centralized feature layer, experimentation becomes chaotic. Feature leakage increases. Models trained in isolation become brittle in production.

By standardizing:

  • Recency

  • Frequency

  • Velocity

  • Trend acceleration

  • Behavioral consistency

We created a stable predictive backbone.

New models could be built on top of existing intelligence instead of rebuilding the same transformations repeatedly.

Enabling Cross-Domain Intelligence

One of the most powerful outcomes of a well-designed feature store is cross-domain intelligence reuse.

At HelloFresh, signals originally engineered for procurement forecasting later powered:

  • Supplier performance scoring

  • Anomaly detection in food quality

  • Recipe optimization based on demand shifts

Similarly, in enterprise LLM deployments at 2021.ai, structured entity features built for retrieval ranking later improved:

  • Hallucination mitigation strategies

  • Document prioritization

  • Context weighting logic

Feature reuse shortens the path from idea to production.

It also improves consistency across AI surfaces.

Preventing Siloed Optimization

Without a feature store, teams optimize locally.

Credit teams optimize for default prediction.
Forecasting teams optimize for demand accuracy.
Growth teams optimize for engagement.

Each team defines intelligence differently.

This leads to:

  • Conflicting predictions

  • Inconsistent scoring logic

  • Duplicate pipelines

  • Higher infrastructure cost

As Feature Store PM, I focused on creating shared definitions:

  • What does “active” mean?

  • How do we measure volatility?

  • What constitutes growth?

  • How is risk calibrated?

Shared definitions are not operational details — they are strategic alignment tools.

They ensure that intelligence compounds instead of diverging.

Balancing Feature Depth vs Feature Breadth

A key trade-off in feature store strategy is deciding when to expand surface area versus deepen signal richness.

Adding more features does not automatically improve model performance.

Often, refining core behavioral primitives provides greater lift than expanding breadth.

For example:

Improving the calculation of demand volatility across different time windows yielded greater forecasting gains than adding external macroeconomic signals.

Similarly, improving calibration of engagement decay signals improved churn detection more than adding new engagement metrics.

As Feature Store PM, my role was to prioritize:

  • Signal density over feature volume

  • Stability over novelty

  • Cross-team reuse over isolated experimentation

The objective is long-term leverage, not short-term model lift.

Supporting Regulated and Enterprise Environments

In regulated environments — healthcare, finance, public sector — feature transparency becomes critical.

At 2021.ai, feature definitions had to be:

  • Auditable

  • Reproducible

  • Traceable to raw sources

  • Explainable

A strong feature store supports:

  • Regulatory compliance

  • Model explainability

  • Fairness auditing

  • Deployment confidence

Without reproducible feature pipelines, model validation collapses under scrutiny.

Reusability also improves governance.

Designing for Compounding Advantage

The most important property of a feature store is that it compounds.

When built correctly:

  • New prediction problems are cheaper to solve.

  • Model iteration becomes faster.

  • Feedback loops become stronger.

  • Cross-domain intelligence increases.

  • Switching costs rise.

In credit systems, improving volatility signals improved both risk models and revenue expansion strategies.

In generative AI systems, improving entity embeddings improved retrieval, personalization, and recommendation simultaneously.

In forecasting systems, improving velocity features improved procurement, inventory planning, and operational optimization.

This is compounding intelligence.

The Strategic View

The Feature Store PM role sits at a structural leverage point.

You are deciding:

  • What intelligence exists in the company

  • Whether it is reusable

  • Whether it is consistent

  • Whether it is scalable

  • Whether it compounds

Without a feature store, AI systems become fragmented experiments.

With a strong feature layer, the organization develops a shared intelligence core that powers multiple high-value prediction surfaces.

In every company I’ve worked in, from consumer platforms to enterprise AI infrastructure, the feature layer determined how quickly the company could launch new AI products.

Reusability is speed.
Shared intelligence is defensibility.
A well-designed feature store is not just infrastructure — it is strategic leverage.

Product Managment, My ExperienceFrancesca Tabor