Founding story
Why we built Siftra and what we are solving first.
Built by people who paged themselves at 2am
Siftra started with a pattern we kept seeing across data teams: every new column, every schema change, every distribution shift needed another handwritten test to keep it honest. The data engineer was the single point of failure for the whole warehouse, and the gaps between hand-authored rules were the gaps where bad data reached dashboards, ML models, and customers. We tried the legacy observability tools — agents, sidecars, per-volume pricing — and the friction of adopting them was the same friction the gap was creating in the first place.
So we built the thing we wanted to use: a read-only credential, one click, and quality rules over every column in minutes — health scores, freshness checks, distribution-change alerts — without an agent or a sidecar or a deployment call. The first version ran for ourselves; the Wave 1 pilot is the first version other teams are running for theirs.
Mission
One sentence that decides every feature we ship.
Make data quality as automatic as the data itself.
Most data observability products stop at detection — a dashboard, an alert, a Slack ping. Coverage of the long tail of columns stays manual. We are building the alternative: a system that watches every column, decides if it is healthy, surfaces only what a human needs to act on, and writes the remediation itself when the fix is a routine one. The engineer’s job is to tune thresholds and own the high-value anomalies — not to write the 200th not-null test of the quarter.
Credibility signals
What already exists in production, not what we plan to ship.
Live in a Wave 1 cohort of six pilot warehouses
We are running the 90-day design partner pilot with a small cohort of teams whose production warehouses we profile, whose rules we tune alongside their data leads weekly, and whose case studies will land on /pricing as the pilot graduates to paid. Same numbers, same product, same onboarding path — the pilot price-lock just buys you direct access to the team for the 90 days.
Sample scale observed across pilot warehouses:
Try the same first-scan path on a sandbox warehouse: /app?demo=true. Pilot applications open at /design-partner.
The 6-tool benchmark is public, repeatable, and unsponsored
We ran every legacy data observability product — Monte Carlo, Databand, Soda, Great Expectations, Bigeye, and Siftra — on the same 14 GB Postgres dataset with the same seeded failure modes. Same four-axis scorecard: time to first rule, coverage breadth out of the box, pricing transparency, agent footprint. No sponsorship involvement, no NDA, no consulting-call gating to see the results.
The full writeup lives at /blog/data-quality-tools-benchmark-2026, and the pricing comparison that follows from it is at /pricing. Use the methodology to run your own side-by-side — most teams who pre-validated with their own data ended up rolling out Siftra within the same quarter they started evaluating.
Talk to the team, or just look at the price.
Wave 1 pilot applications get a 2-business-day reply. If you just want to compare the line item against the 6-tool benchmark, the pricing page is the shortest path.