Making inventory
legible to agents.

Discovery & personalization optimization

Shoppers who find the right product faster convert better and return more often. Most recommendation systems leave that opportunity on the table — optimizing for the wrong signal, ignoring session context, or breaking down on sparse data. We redesign discovery and personalization architecture to be AI-native and continuously improving: session-aware, intent-driven, and built to compound as behavioral data accumulates.

• Audit of existing recommendation and search relevance systems
• Architecture design for retrieval-augmented or embedding-based recommendation
• Cold-start and sparse-data strategies for new users and new inventory
• Instrumentation and evaluation framework: baseline, A/B structure, iteration cadence

Strategic value: A well-designed personalization system gets measurably better with every interaction. The architecture we design is built for continuous optimization — not a one-time lift that plateaus.

Particularly relevant for marketplaces, specialty retailers, and D2C brands with catalogs of 10,000+ SKUs.

Perspectives → Personalization in an agent-mediated world

Seller advertising optimization

Ecommerce platforms and marketplaces are sitting on a monetization opportunity their sellers are already asking for: the ability to pay for visibility. But building a seller advertising product that actually works — where promoted results are relevant to buyers, not just purchased — requires the same infrastructure as great organic search: relevance signals, auction mechanics, and attribution sellers can trust.

Drawing on direct experience building and operating eBay's promoted listings and ads products, we help platforms design and optimize first-party seller advertising systems from the ground up, or improve the ones they already have.

• Assessment of current seller monetization and revenue leakage points
• Architecture for relevance-aware sponsored product and promoted listing systems
• Auction design: bid mechanics, ranking integration, and quality scoring
• Seller experience: campaign creation, budget controls, and performance transparency
• Attribution and reporting: connecting ad spend to actual sales outcomes

Strategic value: Seller advertising compounds. Sellers who succeed with ads invest more in their listings; better listings improve organic results; improved results attract more sellers. When built on relevance, the ad product reinforces the platform rather than degrading it.

Particularly relevant for marketplaces and ecommerce platforms looking to build or significantly improve a first-party seller advertising product.

Catalog & search optimization

Every AI system downstream — personalization, search, seller ads — depends on the same foundation: structured, enriched product data. Most failures at the model layer are actually catalog failures: missing attributes, inconsistent taxonomy, no semantic representation of product meaning. We diagnose catalog quality systematically and rebuild the enrichment pipeline, applying the Progressive Enrichment Framework to produce a catalog that functions as a precision retrieval engine.

• Catalog quality assessment: attribute completeness, taxonomy consistency, embedding readiness
• Enrichment pipeline design: extraction, normalization, classification, embedding generation
• Taxonomy rationalization and attribute harmonization across categories
• Agent Card generation: structured product representations for AI agent consumption

Strategic value: Catalog enrichment is a compounding investment. Every attribute added improves search, recommendations, and seller ads simultaneously. A well-structured catalog is also the prerequisite for agentic commerce readiness — agents reason over structured data, not visual merchandising.

Particularly relevant for any organization where search, recommendation, or ad performance is underperforming — the root cause is frequently in the catalog, not the model.

Perspectives → The Merchant's New Moat

Agentic commerce readiness

The commerce interface is changing. AI agents are beginning to shop on behalf of consumers — querying structured data, evaluating options algorithmically, and completing transactions without a human browsing a product page. Merchants and retailers who have structured their catalog, pricing, and fulfillment data for agent consumption will have a meaningful advantage in this transition.

This service prepares your commerce infrastructure for the agentic era: structured intelligence layers, Answer Engine Optimization for commerce, and Agent Card infrastructure that makes your inventory discoverable and transactable by AI systems.

• Readiness assessment: current discoverability and transactability by AI agents
• Structured data layer design: schema, enrichment, and API surface for agent queries
• Answer Engine Optimization (AEO): positioning products for agent-mediated discovery
• Agent Card implementation using the Progressive Enrichment Framework (Layers 0–6)

Strategic value: This is infrastructure investment ahead of the curve — building structural advantage before agentic commerce becomes a baseline requirement rather than a differentiator.

Particularly relevant for organizations with a 12–24 month technology horizon thinking about the next commerce transition.

Perspectives → Agency, Autonomy, and Intelligence

Enterprise process modernization

Most enterprise AI projects fail not because of the technology but because of how the problem is framed: the wrong process targeted, the wrong pattern applied, or a production system no one trusts enough to use. We help organizations identify where AI can genuinely move the needle, select the right architectural approach, and deploy systems that earn adoption — not just proof of concepts that stall.

This work draws on a structured methodology: a process audit, pattern matching, rapid POC, trust-first deployment, and a roadmap for expansion that treats each engagement as a foundation rather than a finish line.

• Process audit: mapping high-friction, knowledge-intensive workflows and scoring by impact and feasibility
• Pattern matching: selecting the right AI approach (RAG, classification, extraction, agentic workflow) for each problem
• Proof of concept development: time-boxed builds with clear evaluation criteria before broader commitment
• Deployment with trust scaffolding: human-in-the-loop design, explainability, feedback instrumentation
• Expansion roadmap: turning a successful first deployment into a repeatable organizational capability

Strategic value: Each deployment, done well, becomes the reference case and operating template for the next one. We design engagements to leave your team with the capability to continue — not a dependency on continued consulting.

Relevant across industries. Particularly high-value in knowledge-intensive functions: legal, compliance, customer operations, procurement, and internal knowledge management.

Perspectives → Toward a More Mature Culture of Experimentation

Data science & AI transformation

Many data science organizations are well-equipped for the world of classical ML — tabular data, batch inference, carefully labeled training sets — and underprepared for the world of LLMs, agentic systems, and real-time inference. The gap is not just technical: it involves team structure, evaluation practices, tooling choices, and a different relationship between models and production systems.

We help data science and AI leaders assess where their teams and practices stand, define the target state, and chart a practical transition that doesn't require rebuilding from scratch.

• Team assessment: current skills, roles, and structure relative to modern AI demands
• Target state design: team architecture, role definitions, and capability mix for the agentic era
• Tooling and stack recommendations: evaluation frameworks, orchestration, observability, inference infrastructure
• Practice modernization: prompt engineering, evaluation design, guardrails, human-in-the-loop patterns, responsible AI governance
• Transition roadmap: sequenced plan for reskilling, hiring, and tooling adoption

Strategic value: The organizations that will lead in AI over the next five years are not those with the most models — they are those with the clearest practices for building, evaluating, and improving AI systems at organizational scale. This engagement is about building that operating capability.

Particularly relevant for VPs of Data Science, Chief Data and AI Officers, and CTOs managing the transition from classical ML to modern AI at team scale.