End-to-end visibility. Anomaly detection. Lineage-aware alerts. Less Slack pings.
Your dbt tests catch what you wrote tests for. Your monitors catch what you remembered to monitor. Logiciel's data observability platform catches the rest - schema drift, volume anomalies, freshness lag, lineage breaks - and routes alerts to the team that owns the broken thing.
Reality check on most US data teams' observability:
Teams searching this typically have:
A dbt test suite that grew to 2,000 tests and still misses things. Massive dbt test suites with persistent quality issues are evidence that rule-based coverage has structural limits; anomaly detection complements rules without replacing them.
Stakeholders losing trust because last week's number disagreed with this week's number. Stakeholder trust erosion is the leading indicator that quality investment is overdue; the cost of restoration is always higher than the cost of prevention.
An exec asking 'how do we make sure this doesn't happen again?' - and the answer is 'better observability.' Executive 'never again' mandates are the typical trigger for observability investment; the right product gives you the answer before the next mandate.
Observability that catches what humans can't anticipate.
Trading data, risk models, regulatory reporting - sub-second SLAs and audit-ready governance.
Listing data, transaction pipelines, geospatial analytics - multi-source consolidation.
EHR integration, claims pipelines, clinical analytics - HIPAA-aware infrastructure.
Product analytics, customer 360, usage-based billing - embedded and operational data.
Inventory, pricing, order, and customer pipelines - real-time and high-throughput.
IoT, project, and supply-chain data - operational analytics on hybrid stacks.
| Dedicated Pod | Staff Augmentation | Project-Based Delivery |
|---|---|---|
| Embedded data engineering pod aligned to your sprint cadence - typically 3–6 engineers + a US lead. | Senior data engineers, architects, and SMEs slotted into your team to unblock specific work. | Fixed-scope, milestone-driven engagements with clear deliverables and outcomes. |
We map your stack, workloads, team, and constraints in a working session - not an RFP response.
Reference architecture grounded in your reality, with capacity, cost, and migration plans.
Iterative implementation with weekly demos, code reviews, and your team in the loop.
Managed operations or knowledge transfer - your choice. Both with US-aligned coverage.
Continuous tuning of cost, performance, and reliability against measurable SLAs.
Detect stale data within minutes of expected refresh.
Catch unexpected row count changes before they become incidents.
Detect upstream schema changes; classify and route automatically.
Detect distribution shifts in critical columns.
Alerts go to upstream owners + downstream consumers.
Per-domain SLAs visible to business owners.
Same monitoring primitives (freshness, volume, schema, anomaly detection, lineage), broader scope, better TCO at scale. Monte Carlo, Bigeye, and Anomalo are pure-play data observability vendors - capable but expensive, and they only solve observability. Logiciel includes their feature set plus pipeline observability (catching issues at orchestration not just at the warehouse), BI dashboard health monitoring, reverse-ETL observability, and cost telemetry. For teams with 1,000+ datasets, Logiciel typically saves 40-60% versus per-table pricing of pure observability vendors. Lineage-aware alert routing reduces alert fatigue dramatically - most customers cut false-positive alert volume 70% in the first month versus their previous tool.
By column-level lineage - owners of the affected pipeline plus downstream consumer teams are notified with a single ack flow that prevents the same alert from waking up four people. Alerts route through Slack, PagerDuty, Opsgenie, Microsoft Teams, ServiceNow, Jira, email, or webhook. Severity tiers control which channels fire (P1 = PagerDuty wakes oncall, P3 = Slack channel only). The lineage routing prevents the typical 'who owns this' fire drill - when an upstream column changes, the upstream owner, downstream dbt model owner, and BI dashboard owner all see the alert as one threaded conversation. Customers report 60-80% reduction in 'is anyone seeing this?' threads.
Connect your warehouse and we auto-profile the top 100 datasets within 24 hours, establishing baselines and starting anomaly detection immediately. The first surfaced anomaly typically arrives within 48-72 hours - often catching an issue the team hadn't noticed (stale data in a dashboard, a quietly-broken pipeline, schema drift in a critical fact table). Week 1 is baseline stabilization and tuning; weeks 2-4 are routing configuration and stakeholder onboarding (per-domain SLA dashboards). By day 30, most teams report 60-80% reduction in 'is the data right?' Slack threads and have a quantified baseline against which to measure ongoing improvement.
Yes - and Airflow tasks, Spark jobs, BI dashboards (Looker, Tableau, Mode, Sigma, ThoughtSpot), reverse-ETL flows (Salesforce, HubSpot, Marketo writes), and AI feature pipelines. Coverage extends end-to-end from source ingestion through warehouse transformations through BI consumption, giving you a unified view of the data journey. dbt tests run with full visibility (which tests passed, which failed, which were skipped); dbt model freshness is monitored automatically; column-level lineage extends from dbt models out to upstream sources and downstream BI. About 80% of our customers run dbt and we treat it as a first-class citizen.
Anomaly detection is per-dataset trained on 30-60 days of historical patterns to minimize false positives. Most teams report fewer than 2 false positives per week per 1,000 monitored datasets after the first week of stabilization. The first 7 days produce more alerts as baselines establish; we provide a tuning period with an implementation engineer to suppress known-noisy patterns and adjust sensitivity per dataset. After tuning, signal-to-noise is typically 5-10x better than rule-only systems because the platform learns your patterns rather than alerting on every rule violation. For datasets with legitimate volatility (marketing campaigns, seasonal patterns), we apply context-aware baselines that respect known cycles.
Yes - anything you can express in SQL becomes a monitored check, with full configurability for severity, alerting cadence, routing, and escalation. Custom checks are versioned in Git alongside your data models (treated as code, not UI configuration that nobody reviews), reviewed in PRs, and tested in ephemeral environments before production. Common custom check patterns: business-rule reconciliation (revenue match across systems), distribution monitoring on critical KPIs, cross-table consistency at scale, and domain-specific quality rules co-authored with stewards. Custom checks coexist with platform-managed anomaly detection - you don't choose between rules and ML; we run both with unified routing.
Yes - up to 25 datasets monitored free, forever, with no credit card and no time limit. The free tier includes anomaly detection, freshness monitoring, schema drift, lineage-aware routing, and Slack alerting on those 25 datasets - full capability, just bounded scope. About 30% of free-tier users convert to paid within 6 months, typically when their dataset footprint outgrows 25 or when they want enterprise governance and SSO. The other 70% stay free, which is fine - the goal is making data observability accessible to teams that can't budget enterprise tooling but still need their pipelines to work. Free tier is genuinely useful, not a crippled trial.
Connect your warehouse in 5 minutes. We'll auto-baseline your top 25 datasets and start anomaly detection - free, forever.
