Product · Data Quality Intelligence
Your data is lying to you. DataVault finds out where.
Turn your data into your most reliable asset.
Connect your sources in minutes, read-only. DataVault profiles automatically, validates rules, finds duplicates and drift — and proposes the fix, with ready-to-run SQL and estimated savings in euros.
DataVault
erp_demo · scan #1282/100
Quality score
AI Agent · Recommendation
Merge 214 fuzzy duplicates in customers (0.94 confidence).
CREATE TABLE customers_clean AS SELECT DISTINCT ON (email) …
The problem
Four problems you can't see. Until they cost money.
Manual quality checks don't scale. These four patterns repeat in almost every company with data in more than one place.
Multi-cloud sprawl
Three to five data platforms per company. No unified quality view. Manual checks, platform by platform.
Duplicates and inconsistencies
Inflated metrics, corrupted ML models, wasted storage. Manual fuzzy matching is impractical.
Silent schema drift
New columns, changed types, null spikes break pipelines without warning. Exposed PII discovered far too late.
Degrading ML models
Garbage in, garbage out. Manual root-cause analysis is slow, expensive — and always arrives after the damage.
72%
of companies struggle with disconnected or poor-quality data
>50%
of specialists' time goes to chasing errors instead of creating value
How it works
Discover. Validate. Optimize.
Not passive monitoring. DataVault finds the problems — and proposes the fix.
Discover
Auto-discovery and automatic profiling of every connected table. No column-by-column configuration.
- Per-column metrics: counts, nulls, distincts, min/max/mean/deviation
- Cardinality and anonymizable samples
- History per table: a quality time series, scan by scan
Validate
Rule engine, duplicates and drift running on every scan. Everything converges into a comparable score.
- 6 rule types: not null, uniqueness, pattern, range, freshness, minimum row count
- Exact (hash) and fuzzy (ML) duplicates, in clusters with confidence levels
- Schema and statistics drift against the previous scan
- 0–100 score per dataset, across 5 dimensions: completeness, uniqueness, validity, freshness, consistency
Optimize
The AI Agent analyzes schema, issues and usage patterns — and returns concrete fixes, not vague advice.
- Recommendations with generated SQL, ready to review and apply
- Savings estimate in € and % — storage, compute, hours
- Risk analysis per change: safe or breaking
- Reports per persona: technical, cost-benefit, executive digest
Flow
From zero to first report in three steps.
01
Connect
Read-only access via official vendor methods. Credentials encrypted before they touch disk. Under 15 minutes to the first scan.
02
Analyze
Manual or scheduled scans — daily, weekly or monthly. Profiling, rules, duplicates and drift on every run. Alerts by severity.
03
Optimize
Actionable suggestions with SQL and estimated impact. Reports per persona. You fix, the score climbs — and the history proves it.
Who it's for
One platform, three readings.
Each stakeholder sees what matters to them. Nobody wastes time translating dashboards.
Efficient pipelines, less manual checking
Data Engineer
- Score per table and issue browser with drill-down
- Duplicate clusters: review, merge or ignore
- Schema and statistics drift alerts
- Refactoring reports with generated SQL
Target: −60 to −70% manual verification
Cost-benefit and operational efficiency
Data / Product Manager
- Score trend, open vs resolved issues
- Estimated hours saved
- Cost of poor quality per dataset
- Recommendation adoption
Target: prioritize with numbers, not opinions
Visible risk, financial efficiency
Executive (CFO/CTO)
- Total estimated financial impact, in euros
- Potential savings of 15–25% on storage and compute
- Critical incident digest: severity and exposure
- Recommended action for every incident
Target: visible risk, measurable ROI
Real proof
We connected to a public source. Zero preparation.
The OpenAQ open archive — air quality measurements from real sensors, in a public S3 bucket — connected anonymously, no account, no credentials. DataVault discovered the files, ran the scan and returned its verdict in half a minute.
The same source ships in the app's public sources gallery — connect it on your free account and see the same result.
Try it with this source30
files discovered as datasets
32 s
full scan duration
30
issues found and explained
What showed up — genuine real-world sensor dirt:
- —Sentinel values (-999) mixed with valid measurements
- —Negative pollutant concentrations — physically impossible
- —Daily files with gaps and inconsistent columns
What it does today
The full loop: detect, explain, fix, prove.
Anomalies without rules
Volume, nulls and cardinality monitored against history (median+MAD) — zero configuration. False-positive feedback mutes the metric.
Root-cause diagnosis
One click and the agent correlates profiles, drift, failed scans and lineage to explain the probable cause — with verification steps.
Fixes in SQL and PySpark
Every suggestion ships a safe diagnostic + breaking fix: ALTER TABLE, CHECK, dedup — in SQL and the PySpark equivalent for Spark/Databricks.
Medallion awareness
Bronze, Silver and Gold detected by name — the fix strategy adapts: quarantine in Bronze, cleansing in Silver, contracts in Gold.
Lineage and impact
Table dependencies inferred automatically: see what a problem hits downstream before it reaches the board's dashboard.
AI readiness
AI-readiness score per dataset — quality, stability, privacy (PII) and freshness — before feeding RAG or models.
Alerts where you work
Slack, Teams, email and webhooks with per-channel minimum severity + weekly executive digest.
Contracts and CI gate
ODCS export (Linux Foundation) per dataset and pipeline validation: divergent schema or below-threshold score fails the deploy.
Data FinOps
Cost per dataset and waste in €/month on the executive view — quality stops being abstract.
Agents via MCP
Built-in MCP server: Claude (or any agent) queries datasets, issues and costs and requests diagnoses — under your permissions.
Documents for RAG
Manuals and contracts in GCS/S3 analysed before indexing: PDFs with no text layer, duplicate versions of the same document, personal data and files nobody has touched in years.
AI Act compliance
An Article 10 dossier clause by clause, with the evidence we measure in your data and the gaps still to be declared. Ready to attach to the technical documentation.
Cost crossed with quality
Compute spend read from BigQuery, Databricks or Snowflake next to the score: how much of what you pay runs over data the measurement says is wrong.
Fixes under approval
Every fix goes through proposal, approval and application on your side — and the next scan confirms whether it worked. We never write to your sources.
Connectors
Connects to what you already run.
Warehouses, operational databases, files and business applications. Always read-only, always through official methods.
Why it's different
It detects — and proposes the fix.
Enterprise tools tell you there's a problem, months after kickoff. Open-source is free until you count the engineering hours. DataVault does the two things that matter: it finds and it fixes.
| Dimension | Enterprise (Collibra, Informatica) | Open-source DIY (Great Expectations, Soda) | DataVault |
|---|---|---|---|
| Time-to-value | 16–24+ weeks of implementation | Weeks of setup, ongoing maintenance | First scan in under 15 minutes |
| Fixing | Detects and reports. Fixing stays with you | Tests fail. Someone investigates and fixes by hand | Proposes the fix: generated SQL, € savings, risk analyzed |
| Real cost | Enterprise licenses + implementation consulting | Hidden engineering cost: writing and maintaining tests | From €500/mo, no implementation project |
| Multi-source view | Per module, paid separately | One test repository per source | Warehouses, operational DBs and SaaS in a single view |
Security
Your data stays where it is.
Read-only access
DataVault never writes, moves or changes data in your sources. It reads, analyzes, reports.
Official vendor methods
Official drivers and APIs for every platform. No scraping, no shortcuts.
Encrypted credentials
Encrypted with Fernet before being persisted. Never shown in logs.
Controlled samples
Your data never leaves your account. Samples used by the AI are limited and anonymizable.
Pricing
Simple pricing. No implementation project.
Free
€0
- One data source
- 25 datasets · 30 scans/month
- Full quality engine + sample AI suggestions
- No card required
Team
€490/month
- 5 sources · 500 datasets
- Unlimited users
- AI suggestions included (plan cap)
- ODCS contracts + CI gate
- API tokens and webhooks
Business
€1,190/month
- 15 sources · 2,000 datasets
- AI Act compliance (art. 10)
- Cost × quality with native telemetry
- Document analysis for RAG
- 4× AI cap and 2-year history
Enterprise
Custom
- Custom volumes and sources
- Private deployment
- Reinforced DPA and dedicated SLA
- Assisted onboarding
Launch pricing, annual billing (monthly +20%). The gap between DIY open source and enterprise (€30k+/yr) is where we live — on purpose.
See DataVault working on your data.
A 30-minute demo, with your data or our demo ERP. You leave knowing what it would find in your account.