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Just‑in‑Time Data Retrieval & Caching

Just‑in‑Time Data Retrieval & Caching

GTM teams and engineering organizations struggle with fragmented, stale, and costly enrichment workflows that either blow budgets with repeated vendor calls or drift stale with batch-only caches. Just‑in‑Time Data Retrieval & Caching gives you a single, normalized access layer—API + UI—that returns deduped, provenance‑backed company and person profiles instantly, while only fetching external data when necessary using field‑level freshness policies and a cost‑aware query planner.

  • check Fast, reliable front‑line answers

    Sales reps and AEs get sub‑second profile responses (with provenance and freshness markers) so they can act in the moment without waiting for nightly jobs or manuallookups.

  • check Lower cost, predictable enrichment spend

    Field‑level TTLs, negative caching, and a cost‑aware query planner dramatically reduce external API calls—so teams fetch only what’s missing or stale and can set budgets, per‑source quotas, and hit/miss reporting.

  • check One source of truth with auditability

    Deterministic identity resolution, per‑attribute confidence scores, and a retained provenance graph make the merge decisions transparent and auditable for RevOps, data stewards, and compliance owners.

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Just‑in‑Time Data Retrieval & Caching is a unified access layer that abstracts source‑specific complexity into a single API and UI surface. Inputs (domain, company name, person email or profile URL, or filter queries) map to canonical entity_id/person_id keys in Creso’s knowledge graph. Responses return normalized fields. That unified contract removes vendor glue code from your stack and delivers consistent, predictable outputs across apps.

Under the hood the feature combines a layered cache (L1 in‑memory, L2 Redis/KeyDB with attribute‑level TTLs, L3 encrypted object store) with stale‑while‑revalidate behavior and negative caching to deliver sub‑second responses while keeping data current. A cost‑aware query planner inspects requested fields, checks cache coverage and freshness, and only invokes preferred external sources for missing or expired attributes—honoring per‑tenant budgets, rate limits, and compliance rules. Parallelized fetches, single‑flight deduplication, and circuit breakers keep latency bounded and prevent vendor thrashing.

When data conflicts arise the merge engine reconciles values at the field level using configurable prompts so you can write your reconciliation rules in plain language. Every value remains traceable via a provenance graph so data stewards can audit why a field was chosen and adjust source priorities.

How It Works

1

Unified Request Surface

A single REST API that returns normalized entity withper‑field confidence, provenance and freshness.

2

Identity Resolution & Canonicalization

Inputs are normalized and matched to canonical entity_id/person_id keys using deterministic identifiers (domain, verified email, profile URL) and probabilistic signals (name + geo + employer) to produce a single source of truth.

3

Layered Caching & Freshness Control

L1 in‑memory, L2 distributed cache (Redis/KeyDB) with attribute‑level TTLs, and L3 encrypted object store implement stale‑while‑revalidate, negative caching, event‑based invalidation, and pre‑warm/predictive caching for low latency and controlled freshness.

4

Cost‑Aware Query Planner & Merge Engine

The planner inspects requested fields, consults a per‑field source policy matrix and budget constraints to fetch only missing or stale attributes. The merge engine reconciles conflicting values using confidence weighting and records provenance for auditability.