DataOps Platform That Brings Engineering Discipline to Data Teams.

Both, deliberately. The platform encodes DataOps practices in working software (CI/CD for pipelines, observability, runbooks, oncall, SLA tracking); we also offer DataOps maturity assessments, embedded coaching, and team transformation engagements. Most customers want both: the tool gives engineering teams primitives they can run themselves; the coaching provides external perspective, accelerated cultural change, and decision authority that a tool alone can't deliver. Pricing reflects the split: per-asset platform license, fixed-fee per coaching engagement. Customers who buy only the tool typically engage coaching when they hit cultural friction; customers who buy only coaching add the tool when they want to operationalize the practices between engagements.

Pipelines and dbt projects are versioned in Git (your existing repos), tested in ephemeral environments (separate dev/staging/prod with data subsetting), and deployed via promotion (dev → staging → prod with automated checks at each gate). Tests include schema validation, dbt test execution, anomaly detection on synthetic data, and integration tests against staging data. Failed tests block deploys; passed tests promote automatically. We integrate with GitHub Actions, GitLab CI, Jenkins, and other CI platforms — meaning you don't replace your existing CI, you extend it. For US teams without mature CI/CD practices, the platform provides defaults and templates that establish the practice without requiring DevOps expertise on the data team.

Sub-setting and synthetic data generation for safe non-prod testing. Sub-setting tools extract a representative slice of production data with referential integrity preserved; synthetic generation creates realistic-looking data that has zero PII or business-sensitive content. Both approaches are configurable per source — typically sub-setting for development environments (analysts need to feel real data shapes), synthetic for security-sensitive contexts (HIPAA, GDPR-protected data). Test data refresh is automated and audited. For regulated customers, the test data approach is auditable evidence of data protection in non-prod environments — a frequent gap in pre-DataOps shops.

Native GitHub Actions, GitLab CI, Jenkins, CircleCI, and other CI platforms — meaning you keep your existing CI infrastructure and add Logiciel-specific actions/jobs to it. We don't replace your CI; we extend it with data-specific testing, deployment, and validation steps. For teams using GitHub Actions, we publish reusable actions in the marketplace; for teams using GitLab CI, we publish reusable templates. The integration pattern means data engineers use the same CI workflow as software engineers, breaking down the cultural divide that's been a leading cause of data team isolation. For teams without mature CI practices yet, we provide reference templates.

Yes — maturity assessments and embedded coaching available. Maturity assessment is fixed-fee, 4-week engagement that benchmarks your team against industry-standard DataOps practices (CI/CD, observability, incident management, SLA discipline, automation, governance) and produces a 90-day uplift plan with named owners and measurable outcomes. Embedded coaching is typically 3-6 months with a US-based DataOps lead working alongside your team — pairing on incidents, leading retros, codifying runbooks, mentoring senior engineers. Coaching is most effective when paired with platform adoption; the tool reinforces the practices, the coaching builds the cultural muscle.

Concretely: all pipelines version-controlled in Git, code-reviewed in PRs, tested in ephemeral environments, deployed via promotion (dev → staging → prod). All datasets have observability (freshness, anomaly detection, schema drift) and lineage-aware alerting. All incidents have runbooks. Per-domain SLAs are measured and reported to business owners. Mean-time-to-detect (MTTD) is under 5 minutes for critical pipelines; mean-time-to-resolve (MTTR) is under 1 hour for high-severity issues. Pipeline change failure rate is under 5%. Cost is attributed to teams and budgeted. These metrics are measured continuously, not aspirationally — the difference between mature DataOps and DataOps theater.

Per active asset — DataOps capabilities (CI/CD, observability, incident management, SLA tracking) included in standard tier. Mid-market customers (5-30 data engineers, 200-500 assets) typically pay $40-90K ARR. Enterprise tiers ($200K+) add advanced governance, custom workflows, dedicated TAM. Coaching engagements are priced separately at fixed-fee per engagement, ranging from $50K (4-week assessment) to $400K (6-month embedded coaching). Pricing is transparent with workload-grounded TCO comparisons available at evaluation. Compare to building these capabilities in-house: DataOps platform engineering teams are typically 3-6 engineers ($900K-$1.8M annual cost), so the platform pays back quickly even before coaching value.