Data Quality Software That Stops the 'Why Doesn't This Number Match' Loop.

We include their rule primitives (schema, custom SQL, freshness, distribution) plus ML-based anomaly detection, lineage-aware routing, stakeholder dashboards, and steward workflows - all managed, not self-hosted. Great Expectations is open-source and capable but operationally heavy; you run the runtime, the metadata store, and the alerting yourself. Soda is managed but rule-only; the issues that hurt are rarely the ones you wrote rules for. Logiciel layers anomaly detection on top of rules, catching the 'this number changed 30% and nobody knows why' patterns that pure rule-based systems miss entirely. For US mid-market and enterprise customers, Logiciel typically replaces Great Expectations + a separate alerting layer + a separate stakeholder dashboard.

Yes - Logiciel runs your existing dbt tests as part of unified pipeline monitoring and adds anomaly detection on top. Drop your dbt project (manifest, tests, profiles) into Logiciel and the platform orchestrates dbt runs, surfaces test results in unified observability, and routes failures through lineage-aware alerting. dbt's `not_null`, `unique`, `accepted_values`, and custom tests all flow through naturally. Logiciel adds the layer dbt tests can't reach: anomaly detection on volumes and distributions, schema drift detection, freshness lag monitoring, and stakeholder SLA dashboards. About 80% of our customers run dbt; we make dbt's quality story complete rather than competing with it.

Per-domain dashboards and signoff workflows let stewards co-own quality without writing SQL. Stewards see their domain's quality SLAs (freshness, accuracy, completeness, timeliness), can author business-rule quality checks via templates (no SQL required for common patterns), approve anomaly investigations, and sign off on schema changes. Engineering writes the technical primitives; stewards govern the meaning. This split - instead of forcing stewards to learn SQL or forcing engineers to manage business rules - is what makes data quality programs sustainable. For regulated customers (SOX, HIPAA, GDPR), steward signoff is auditable evidence of data governance.

Yes - operational databases (Postgres, MySQL, MongoDB, SQL Server, Oracle), data lakes (S3, ADLS, GCS with Iceberg/Delta/Hudi), streaming sources (Kafka, Kinesis, Pub/Sub), and SaaS source systems (Salesforce, HubSpot, NetSuite) all support quality monitoring. The depth of monitoring depends on the source: operational DBs and lakes get full anomaly detection; streaming sources get latency and throughput SLAs; SaaS sources get schema and freshness monitoring. Many customers monitor source-system quality (catching upstream issues before they propagate to the warehouse) in addition to warehouse-side monitoring - this is one of the patterns that materially shifts the failure mode from reactive to proactive.

Quality monitoring runs on metadata (schema, row counts, distributions over hashed values, freshness timestamps) and sampled non-PII data; PII stays masked or in-place. For deeper analysis on PII-containing fields, we support customer-managed encryption keys and tokenization patterns where the platform sees only obfuscated values. Auto-classification identifies PII columns (name, email, SSN, payment data) and applies appropriate masking automatically. For HIPAA, GDPR, CCPA, and other regimes, we configure region-specific PII rules by default and provide auditable evidence of masking enforcement. PII handling is a frequent regulated-customer concern and we have specific reference architectures for healthcare and financial services.

Yes - up to 25 datasets monitored free, forever, with full anomaly detection, freshness monitoring, schema drift, lineage routing, and Slack alerting on those datasets. No credit card, no time limit, no feature crippling within the free tier scope. About 30% of free-tier users upgrade within 6 months when dataset count outgrows 25 or when enterprise governance becomes important. The other 70% stay on free, which is the design goal - making data quality accessible to teams that can't budget enterprise tooling but still need their pipelines to be trustworthy. Free tier is functionally complete for small teams, not a crippled marketing trial.

24 hours to first anomaly detected on your top datasets. Connect your warehouse, we auto-profile the top 100 datasets and establish 30-60 day baselines from query history (no waiting period required), and anomaly detection starts immediately. The first surfaced issue typically arrives within 48-72 hours and often catches a real problem the team hadn't noticed. Week 1 is baseline stabilization; weeks 2-4 are routing and stakeholder dashboards; by day 30, most teams have eliminated 60-80% of 'is the data right?' Slack threads and have measurable improvement in stakeholder trust. ROI in the first quarter is typically expressed as engineer hours regained plus financial close cycle time reduction.