FOR DATA TEAMS
Klaritics queries the same warehouse your dbt models live in. Same source of truth, same governance, same compute account. No reverse ETL, no second event copy, no new SLAs to babysit.
A PM asks why retention is 39% in dbt and 42% in Mixpanel. You explain - for the fourth time this quarter - that Mixpanel's session-merging logic differs from your warehouse's, that late-arriving events get bucketed differently, that the is_active_user model in dbt has a 7-day window while Mixpanel's "active" definition is 28-day. You're right. You've been right every previous time. You'll be right next quarter too.
Meanwhile, the dbt Labs 2026 State of Analytics Engineering report says 71% of data teams worry about incorrect data reaching stakeholders, 41% cite ambiguous data ownership as a recurring challenge, and warehouse spend is rising 57% YoY against just 36% budget growth. You're not the only one.
The gap between the warehouse (where the truth lives) and the analytics tool (where the PM looks) is the gap that creates these conversations. We built Klaritics to close it.
Monday - schema migration
Engineering renamed signed_up_at to account_created_at in the production deploy. In the old world, you'd update Mixpanel's tracking plan, wait for the next ingest cycle, hope nothing else broke. In Klaritics, the column rename propagates to the UI on the next query - schema is read directly from the warehouse, not from a vendor's mirror. Your morning standup mentions it once and moves on.
Wednesday - incident review
A bad deploy double-counted purchase events on Monday. By Wednesday afternoon, the fix is in and dbt is reprocessing the affected day. Because Klaritics queries events_resolved (the dbt-built view, not raw events), the corrected numbers show up in product dashboards the moment dbt's incremental model finishes. No separate "please re-ingest into Mixpanel" ticket. No multi-day discrepancy.
Friday - security review
A new compliance requirement says PII can't leave the company's AWS account. The security team checks Klaritics against the requirement: it's a self-hosted Helm deployment in your own EKS cluster, talks to Snowflake via a service user, and writes nothing to vendor-controlled storage. The review takes 20 minutes. Compare to the SOC-2 + DPA + sub-processor evaluation a SaaS analytics tool would trigger.
Two sources of truth: the warehouse and Mixpanel/Amplitude. The reconciliation conversation never ends.
One source: your warehouse. Klaritics queries it directly. dbt models become the canonical retention definitions.
Tracking plans live in Notion or Avo and drift from reality. Schema changes break dashboards silently.
Schema is introspected from the warehouse on every query. Renames and new columns appear immediately.
Reverse-ETL pipelines push computed cohorts back into the analytics tool. They fail at 3am.
No reverse ETL. Cohorts are SQL views in the warehouse. Klaritics reads them like any other table.
Vendor on your subprocessor list, on your DPA, on your annual security review.
Klaritics runs in your VPC. We're not on your subprocessor list because we don't process your data.
Warehouse spend grows; analytics SaaS spend grows; budget grows by less than either.
One license, plus a small bump in warehouse compute (typically 5-20%). No double-pay for the same events.
This is also unusual on a marketing page. You're going to invest something to use Klaritics. Worth knowing what.
You'll own the event schema. Klaritics reads your warehouse, so the quality of your event tables determines the quality of analytics. We document a canonical schema (long-narrow event table, event_time, user_id, event_name, properties JSON), but if your tables are fragmented across 12 sources or modeled in a way that prunes badly, the work to consolidate them lives with you. We help. We don't do it for you.
You'll run an identity-resolution model. Anonymous-to-known stitching matters in product analytics. Klaritics ships default dbt models for this, but they're yours to read, modify, replace, or delete. If your business has unusual identity needs (B2B account-level, multi-product cross-stitching, offline-to-online), expect to write SQL.
You'll right-size warehouse compute. Klaritics queries are aggregate-heavy and time-bounded; they prune well, but they cost something. Most teams allocate Klaritics its own warehouse - a Snowflake XSMALL, a Redshift Serverless workgroup, a small BigQuery slot reservation - with auto-suspend. Our docs cover the patterns, but you make the call.
You'll do the integration work. Klaritics ships as software, not SaaS. You install it (Helm, Docker Compose, or Linux package), run it, upgrade it on your schedule, and back it up. We help during onboarding; after that, it's yours to operate. For some teams that's a relief - no vendor outages affect your dashboards. For others, it's a tax. Decide which kind of team you are before you commit.
If you wanted to stop reading at "you'll own the event schema," that's a fair reaction. Klaritics rewards data teams who think of analytics as a data product. If your org doesn't have a data team, or your data team is a single overloaded analytics engineer, a SaaS tool may be the better fit until you scale up.
Klaritics doesn't replace anything in your data stack. It adds a query layer on top, designed for the funnel-and-retention shape of product analytics queries.
Klaritics is the rightmost component. Everything above it stays exactly the way you built it. We're a consumer of your warehouse, not a competing system.
Connect your warehouse in 8 minutes. See your first funnel in under an hour.
Klaritics is a serious enough piece of infrastructure that we'd rather you read the technical brief before you deploy. It covers the query model, identity resolution, the dbt integration patterns, and the per-warehouse performance characteristics. If after reading you want to talk before installing, an engineer (not an SDR) will walk through your stack with you.