Memory & Intelligence

Overview

Podium’s memory and intelligence system gives agents a deep, evolving understanding of each user. Instead of treating every conversation as stateless, the agent accumulates structured knowledge — preferences, goals, concerns, products tried, price sensitivity — and uses that knowledge to score and explain recommendations. The system covers three capabilities:

Structured user memory — a versioned schema that captures everything the agent learns about a user
ML scoring layer — popularity signals, temporal decay, and domain-specific weights that rank products intelligently
Reason tags — human-readable explanations attached to every recommendation

AgentMemory

The agent builds and evolves a structured memory for each user. Memory is stored on the user’s intent profile and persists across sessions, channels, and devices.

Schema

interface AgentMemory {
  schemaVersion: 2
  attributes: Record<string, {
    value: string
    confidence: "high" | "medium" | "low"
    source: "quiz" | "declared" | "inferred"
    lastConfirmed: string
  }>
  goals: MemorySignal[]
  concerns: MemorySignal[]
  avoidances: MemorySignal[]
  productsTried: ProductMemory[]
  priceRange: {
    overall: { min: number, max: number, lastUpdated: string } | null
    byCategory: Record<string, { min: number, max: number, lastUpdated: string }>
  } | null
  lastConversationDate: string
}

interface MemorySignal {
  text: string
  confidence: "high" | "medium" | "low"
  firstMentioned: string
  lastConfirmed: string
  mentionCount: number
}

interface ProductMemory {
  name: string
  brand: string
  productId: string | null
  sentiment: "positive" | "negative" | "neutral"
  outcome: string | null
  dateSurfaced: string
}

Key Design Decisions

Category-aware price ranges. A user might spend up to

50 on serums but cap moisturizers at

30. The priceRange.byCategory map captures this naturally — the agent uses the most specific range available when filtering products. Confidence-tracked signals. Every goal, concern, and avoidance carries a confidence level, mentionCount, and timestamps. Signals the user mentions repeatedly are weighted higher than one-off statements. Signals that haven’t been confirmed recently decay in influence. Multi-source attributes. User attributes can originate from three sources:

Source	How It’s Set	Confidence
`quiz`	Onboarding or profile-building quiz	High — explicit answers
`declared`	User states something directly in conversation	High — explicit statement
`inferred`	Agent deduces from context or product choices	Medium to low — requires confirmation

Products tried with outcome tracking. The agent records not just which products a user has tried, but their sentiment and outcome. “Broke me out” vs “holy grail” is critical context for future recommendations.

Automatic Summarization

The agent extracts and updates memory at three trigger points:

Trigger	Condition	Purpose
Early capture	Turns 3–5, no existing memory	Capture initial preferences quickly
Regular cadence	Every 10 user turns	Keep memory current as conversation evolves
Session gap	>2 hours since last conversation	Summarize before context goes cold

Summaries are stored on the user’s intent profile (agentSummary field), making conversation-derived intelligence available to recommendations, the agentic product feed, and downstream analytics.

Memory extraction runs as a background job — it does not add latency to the agent’s response. The user sees no delay.

Domain Taxonomy

Products are organized into a two-level domain:subcategory system. This taxonomy drives scoring weights, expert personas, and brand tier calibration.

Domains

Beauty

moisturizer, serum, cleanser, sunscreen, toner, mask, exfoliant, eye cream, lip, makeup, hair care, body care, tools, fragrance

Wellness

supplements, probiotics, protein, greens, vitamins, minerals, adaptogens, nootropics, collagen, sleep, longevity, fitness

Fashion

clothing, shoes, accessories, bags, jewelry, watches

Home

decor, kitchen, bedding, bath, storage, lighting, furniture

Each product is tagged with a domain and subcategory. When the agent scores and re-ranks recommendations, it loads the appropriate domain configuration — weights, persona, and brand tier thresholds.

Domain-Specific Expert Personas

When re-ranking recommendations, the system activates a domain-specific expert persona. This shapes the language, evaluation criteria, and recommendation style:

Domain	Persona	Evaluation Focus
Beauty	Curated beauty recommendation expert	Ingredients, skin compatibility, texture, routine fit
Wellness	Formulation science advisor	Bioavailability, clinical evidence, dosage, sourcing
Fashion	Premium personal stylist	Fit, occasion, wardrobe coherence, brand positioning
Home	Design-forward specialty buyer	Aesthetics, materials, space fit, value per dollar

Personas are applied during the re-ranking step — after initial retrieval and scoring, but before final product selection. The persona influences both which products surface and how the agent describes them.

ML Scoring Layer

Beyond profile matching, the intelligence system uses machine learning signals to rank products:

Impression Logging

Every product shown to a user is logged with its position. This creates a feedback loop: the system knows which products were seen (not just clicked), enabling proper conversion-rate estimation and position-bias correction.

Popularity Scoring

Product popularity is computed from weighted user interactions:

Interaction	Weight
`PURCHASED`	10
`PURCHASE_INTENT`	5
`AFFILIATE_CLICK`	3
`RANK_UP`	2
`NUDGE_OPENED`	1
`RANK_DOWN`	−2
`SKIP`	−1

The raw score is normalized: popularityScore = weightedSum / sqrt(totalInteractions). This prevents products with many low-quality interactions from outscoring products with fewer but stronger signals.

Temporal Decay

Interaction signals decay over time with a 720-hour half-life (approximately 30 days). A purchase from yesterday weighs more than one from three months ago. This keeps recommendations responsive to shifting user interests and seasonal trends.

Feature Cache

For each product, the system maintains a pre-computed feature vector:

Feature	Description
`qualityScore`	Aggregated quality signal from enrichment data
`brandTier`	1 (prestige) through 4 (mass market)
`popularityScore`	Normalized weighted interaction score
`pricePercentile`	Where this product sits in its category’s price distribution
`enrichmentDepth`	How many enrichment signals back this product

These features are cached for fast retrieval during scoring — no recomputation on every request.

Reason Tags

Every recommended product can include a reasonTag — a concise, human-readable explanation of why it was selected. Reason tags build user trust by making the agent’s logic transparent.

Tag Types

Tag	Example	Triggered By
Skin type match	`"For oily skin"`	User’s declared skin type matches product suitability
Profile concern	`"Hydration"`	Product addresses a user concern from memory
Budget	`"Under your $50 budget"`	Product price fits the user’s category price range
Brand affinity	`"Matches your taste"`	User has positive history with this brand
Compound	`"For oily skin · Hydration"`	Multiple reasons combined with `·` separator

Reason tags are generated during the re-ranking step and returned alongside product data in both the conversational agent responses and the companion recommendations endpoint.

Signal Scoring with Polarity

The scoring system handles negation and avoidances intelligently — a common pitfall in keyword-based matching.

How It Works

When computing signal scores, the system applies domain-specific weights and checks polarity:

Avoidance-aware matching: If a user lists “fragrance” as an avoidance, a product described as “fragrance-free” is treated as a positive signal, not penalized. Similarly, “paraben-free” is not penalized when “parabens” is in the user’s avoidance list.
Negative descriptor detection: Terms like “greasy”, “harsh”, and “irritating” carry negative weight regardless of user preferences. These are universally undesirable product attributes.
Domain-specific weighting: A “lightweight texture” signal carries more weight in beauty (where texture is critical) than in wellness (where formulation matters more).

This polarity-aware scoring prevents the classic failure mode where an agent penalizes products for not containing an avoided ingredient — the exact opposite of what the user wants.

How Memory Feeds Into the Platform

Memory is not isolated to conversations. It flows into multiple platform capabilities:

Consumer	How It Uses Memory
Conversational Agent	Personalizes responses, remembers past products and preferences
Product Feed	Memory-aware intent scoring and domain-specific weights
Enrichment Pipeline	Domain taxonomy aligns enrichment attributes with scoring
Recommendations	Reason tags, avoidance filtering, price range enforcement
Proactive Nudges	Targets re-engagement based on memory gaps and product outcomes

Overview

Intelligence Layer

Commerce Primitives

Building on Podium

Memory & Intelligence

Overview

AgentMemory

Schema

Key Design Decisions

Automatic Summarization

Domain Taxonomy

Domains

Domain-Specific Expert Personas

ML Scoring Layer

Impression Logging

Popularity Scoring

Temporal Decay

Feature Cache

Reason Tags

Tag Types

Signal Scoring with Polarity

How It Works

How Memory Feeds Into the Platform

​Overview

​AgentMemory

​Schema

​Key Design Decisions

​Automatic Summarization

​Domain Taxonomy

​Domains

​Domain-Specific Expert Personas

​ML Scoring Layer

​Impression Logging

​Popularity Scoring

​Temporal Decay

​Feature Cache

​Reason Tags

​Tag Types

​Signal Scoring with Polarity

​How It Works

​How Memory Feeds Into the Platform

Overview

AgentMemory

Schema

Key Design Decisions

Automatic Summarization

Domain Taxonomy

Domains

Domain-Specific Expert Personas

ML Scoring Layer

Impression Logging

Popularity Scoring

Temporal Decay

Feature Cache

Reason Tags

Tag Types

Signal Scoring with Polarity

How It Works

How Memory Feeds Into the Platform