Connect any source. AI maps schemas, resolves entities, enforces ACL. Cross-source questions answered in seconds, not months.
10,000 customers exist across 12 systems. Each spells names differently, uses different IDs, tracks different fields. Nobody agrees on revenue.
The CEO asks a simple question. It takes a month, three analysts, and a slide deck that everyone quietly disagrees with.
The problem is not data volume. The problem is that no system tracks where each fact came from, how confident it is, or what to do when two sources disagree.
Reads table structures, API responses, and file headers. Infers types, cardinality, and null rates from a statistical sample.
LLM classifies each field against the claim taxonomy. "Annual Contract Value" and "ACV_USD" resolve to the same claim type.
Detects overlapping entities and conflicting definitions across sources. Flags mismatches before ingestion.
Normalized text comparison, phonetic encoding, and edit distance. Handles typos, abbreviations, and transliterations.
When systems share an identifier (domain, DUNS, CUI), Attest links records deterministically. No model uncertainty.
Industry-specific logic: subsidiary rollups, name normalization, alias registries. Configurable per tenant.
Permissions propagate from source to claim automatically. No separate permission model to maintain.
Okta, Azure AD, Google Workspace. Groups and roles map to namespaces. SCIM provisioning supported.
Every query filters by the caller's entitlements. No data leaks between teams, regions, or tenants.
Break the question into entity lookups, predicate filters, and aggregation operations.
Entity-first retrieval across all sources. Exact match, then BM25, then LLM extraction. Sub-second on 85M claims.
When sources disagree, surface both values with confidence scores. Higher-provenance claims rank first.
Apply ACL. Format the response with inline citations. Flag low-confidence segments.
Every claim decays over time. Revenue data older than a week, risk signals older than 3 days — the system knows and flags it. High-value entities with stale data trigger automatic re-sync from source systems.
The CEO asks "what's our risk for the top 100 customers?" Every Monday. The engine learns this pattern and pre-builds the answer over the weekend. Query time: <200ms instead of 5 seconds.
Instead of pulling 50 atomic facts about a customer and synthesizing them in real-time, the engine pre-builds composite claims — a single risk assessment, revenue summary, or relationship health score per entity, updated continuously.
The engine scans its own knowledge graph for blind spots: entities with data from only one source, top customers missing satisfaction data, composites that haven't been refreshed. Problems surface before they become wrong answers.
Reviewers confirm or reject surfaced claims. Each decision updates the source reliability model.
Sources that consistently produce confirmed claims earn higher default confidence. Unreliable sources get demoted.
Old claims lose confidence over time. Configurable decay curves per claim type. Stale data never silently persists.
Per-source health monitoring. Failing connectors back off exponentially. No cascade failures.
Claims that fail validation are quarantined, not dropped. Inspect and replay after fixing the source.
When a source changes its schema, Attest detects it before ingestion breaks. Alerts, not silent corruption.
Per-tenant, per-source, per-endpoint. Burst allowance with sliding window. No noisy neighbor problems.
Merkle hash chain on every write. Tamper-evident log. Export to SIEM. SOC 2 ready.
Namespace isolation with federation. Run separate instances per region. Sync selectively across boundaries.
See how Attest connects to your existing systems, unifies your data, and actively maintains the truth. Living Database features available on Team ($249/mo) and Enterprise plans.
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