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Data Enrichment

Enrichment thatreasons, not calculates.

Maestro doesn't derive static fields. It reads your data and reasons over it — turning raw products, orders, and customers into deep, living context. And because it's agentic, it enriches whenever it fits, continuously, never on a batch schedule.

Hire MaestroHow it works

reasoning layer

  • raw"CeraVe Moisturizing Cream 340g"
    Routine role
    Foundational barrier step · anchors AM/PM routine
  • raw"Oral-B Pro 3 Toothbrush"
    Replenishment
    Head wears ~3 mo · drives refill + toothpaste
  • raw"Royal Canin Indoor Cat 4kg"
    Retention
    ~5-6 wks per adult cat · vet-anchored loyalty
  • raw"Apple iPhone 15 Pro"
    Cross-sell
    Ecosystem hub · pulls AirPods, Watch, case

inferred by reasoning · not present in the raw fields

The difference

Calculated fields describe the past.Reasoning explains what to do next.

Traditional enrichment computes static scores and segments from what already happened. Maestro reads the same data and reasons over it — inferring intent, context, and the next right move that no formula can produce.

Calculated fields

static · rule-based

  • RFM score = 4.2
  • Segment: “High value”
  • Churn probability: 0.63
  • Days since last order: 41
  • Lifetime value: $1,240

Numbers you still have to interpret. They tell you that something is true, never why — or what to do about it.

Maestro reasoning

contextual · living

  • Barrier-repair routine anchored on CeraVe; reorder window closing — cross-sell serums + SPF now.
  • Premiumising from budget to mid-tier haircare; open to discovery, responds to ingredient stories.
  • Household expanded (baby products entered basket) — lifecycle shift, not just a spend increase.
  • Replenishment rhythm slipping 6 days late — early drift signal, still recoverable.

Context you can act on. Every attribute carries intent, timing, and the next right move — the signal a raw catalog and a scoring model can't contain.

The enrichment story

One chain of reasoning, fromproduct to person

Enrichment isn't a single step. It's a cascade — each layer built on the one before it, until a raw catalog and a purchase history become a living, decision-ready customer.

  1. It starts with the product

    Maestro enriches each product with reasoning

    Before anything else, Maestro reads every product and reasons over it — inferring category, audience, use case, brand positioning, and consumption rhythm. These are contextual, semantic attributes the reasoning layer produces, not calculated fields you send.

    Raw catalog recordContextual product intelligence
    Routine roleFoundational barrier step
    ReplenishmentDepletes ~5-6 wks at daily use
    Cross-sell logicAnchors AM/PM → serums, SPF

  2. Products meet people

    It builds a living product × customer relationship

    Enriched products don't sit in a table — Maestro connects them to each customer's real behaviour. What they buy, in what combination, at what intervals, and in what sequence becomes an evolving relationship between the catalog and the person.

    Enriched products + order historyProduct × customer relationship graph
    RhythmReorders every 5 weeks, ±3 days
    CombinationCream + serum bought together
    TransitionBudget → mid-tier over 4 orders

  3. A person takes shape

    From that relationship, it generates a customer profile

    Out of the enriched product × customer relationship, Maestro generates a customer profile — a synthesized view of who this shopper is: their motives, household, lifestyle, and relationship with your brand. It's created, not queried from a static CRM field.

    Relationship graphGenerated customer profile
    MotiveSkin-concern resolution
    HouseholdSingle professional, urban
    Brand relationshipSubscriber · 3-yr LTV

  4. The loop deepens

    Then it enriches that new profile with data

    The newly generated profile becomes its own subject of enrichment. Grounded in the enriched product × customer relation, Maestro layers on intent, lifecycle state, and behavioural momentum — a continuously evolving profile that feeds every downstream decision.

    Generated profile + fresh signalsEnriched, decision-ready profile
    IntentPremiumising, open to discovery
    LifecycleHousehold expanding
    MomentumReorder window closing now

See it live

Watch raw data becomecontextual intelligence

Pick a category and an input. Everything on the right is inferred by Maestro's reasoning layer — none of it lives in the raw record.

Raw catalog input

Personal Care

Enriched by Maestro

CeraVe Moisturizing Cream

Semantic attributes

Usage Frequency

daily

Consumable Type

cream

Primary Concerns

Dry skin, Barrier repair

Usage Context

After cleansing routine

Compatibility

Hyaluronic acid safe

Price Band

Mid-range

Seasonality

year-round

Reasoning layer — the signal your catalog doesn't contain

Replenishment Signal

Jar depletes ~5-6 wks at daily use

Routine Role

Foundational barrier step

Cross-Sell Logic

Anchors AM/PM routine → serums, SPF

Retention Risk

High churn if reorder missed

Agentic by nature

Maestro enricheswhenever it fits

There is no nightly job, no batch window, no schedule to wait for. Maestro is agentic — it decides when enrichment is needed and does it the moment a signal arrives, so context is never stale.

Batch enrichment

Runs on a fixed schedule. Between runs, decisions rely on context that's already out of date. New behaviour waits in a queue.

Agentic enrichment

Triggered by the event itself. The instant an order, product change, or behavioural shift arrives, Maestro re-reasons the affected context — nothing waits, nothing goes stale.

Maestro

always on

  • New order lands

    Behavioural state re-interpreted.

  • Product upserted

    Catalog re-reasoned in place.

  • Basket combination shifts

    Relationship graph updates.

  • New customer appears

    Profile generated on the spot.

  • Behaviour drifts

    Intent + lifecycle re-scored.

Why it matters

Enrichment is the fuel forevery decision

Reasoned, always-current context doesn't just sit in a profile — it makes every Maestro agent sharper, faster, and easier to trust.

Sharper cross-sell

Because products carry role and cross-sell logic, Maestro matches the next right product to each customer — not the statistically popular one.

Precise replenishment timing

Every product knows its depletion rhythm and every customer their reorder cadence, so refill prompts land in the window — not too early, never too late.

Decisions with a reason

Enriched context is the input to every Maestro agent. Each decision traces back to intent, lifecycle, and behaviour — reasoning, never a black-box score.

No new data pipeline to own

Enrichment runs internally on the data you already send. Attributes stay inside Replenit and power the engine — nothing to model, store, or maintain on your side.

Cold-start, warmed fast

Send 12-24 months of history at integration and the reasoning layer builds nuanced profiles immediately — enrichment begins the moment data lands.

Always current context

Continuous re-enrichment means every downstream decision reasons over the customer as they are today — not a snapshot from last week's batch run.

Data Enrichment

Give your decisions data that actually understands your customers

See how Maestro reasons over your catalog and order history to build living, decision-ready customer intelligence — on top of the stack you already run.

Hire MaestroRead the docs