Governed Analytics
Your data tells a story. Make sure it's the right one.
BayesIQ delivers governed analytics — whether you need us hands-on or want the platform embedded in your workflow. Every finding reviewed. Every decision attributed.
How BayesIQ works
Every finding reviewed. Every decision attributed.
Hands-on consulting or the platform in your workflow — the same chain from raw output to approved deliverable, and every transition evidence-backed.
Three layers of truth
Every piece of information moves through three distinct layers. Nothing reaches operational state without explicit human acceptance. The layers are the invariant — they apply to every engagement, every pipeline, every decision.
- Raw Data
Raw Truth · Immutable evidence
No summaries, no interpretations. Just the record. Pipeline outputs, test results, call transcripts, emails — the ground truth your decisions rest on.
- Reviewed
Derived Interpretation · Structured output
AI-generated proposals, extracted tasks, priority assessments, and summaries. Clearly labeled as interpretation, not fact. Never auto-promoted to operational state.
- Approved
Governed State · Human-accepted truth
The only layer that drives dashboards, deadlines, and work state. Nothing reaches this layer without explicit human acceptance. This is your single source of governed truth.
Interactive · try it
Nothing reaches governed state without a human yes.
Click a source to send a record in. It lands in raw truth, gets interpreted, then waits for review. Only an approval makes it governed.
- 1–5
- send a source
- enter
- approve
- r
- reject
Two ways in. One standard of proof.
Bring us in to find what is broken and fix it — or run the governance layer yourself. Either way, nothing reaches a board deck without evidence behind it.
Reliability score
38 → 87
- F-01Criticalcheckout_completed fires on payment attempt, not payment confirmation — 23% funnel inflation.
- F-02Highuser_id null in 18% of mobile web page_view events.
Fix Your Metrics
Audit-first analytics. We find it, fix it, prove it.
For data team leads, VPs Analytics, and CFOs
Raw Data
evidence attached
Reviewed
decision attributed
Approved
transition backed
Govern Your AI Outputs
The governance layer that produces an auditable chain from raw AI output to approved deliverable.
For CTOs, compliance leads, and technical operators
+
Quality checks
%
Less debug time
Avg score after
score · before → after
Walk a live engagement
Five industries, real findings, board reports and governance workflows — end to end.
Fintech · SaaS · Hospital · Retail · Real Estate
Industries
Every industry breaks its own way
We know what breaks in each one because we have audited it. Pick yours to see the failure patterns and a representative finding.
Representative finding
Revenue metrics built on transaction pipelines that nobody has audited end-to-end.
Reported revenue
$1.2M
Actual revenue
$960K
$240K overstatement found. 1,200 dropped transaction records and a fee calculation error in the settlement pipeline.
Reliability score
Score moved from 52 to 84 out of 100.
Common failure patterns
- 01
Revenue metrics that don't match finance
Product analytics show one revenue number. Finance shows another. Metric validation recomputes KPIs from raw transaction data to show exactly where currency conversion logic, refund handling, or phantom events introduce the gap.
- 02
Payment event telemetry with gaps
Transaction events fire from multiple clients and payment processors. Required fields like currency, payment_method, or transaction_id are null 5-20% of the time. Schema profiling catches null spikes before they reach downstream aggregations.
- 03
Compliance reporting on unaudited pipelines
SAR filing counts, transaction monitoring volumes, and KYC completion rates are built from the same pipelines as product dashboards. A scored audit with severity-ranked findings shows exactly which pipeline issues affect compliance numbers.
ResultA mid-market payments processor found a $340K annual revenue discrepancy and 1,200 silently dropped transaction records.
Works with your stack · read-only
- Snowflake
- BigQuery
- Redshift
- dbt
- Looker
- Tableau
- Mode
- Metabase
- Streamlit
Case studies
See what an audit changes
Representative engagements from patterns we see across industries. Details are composites — your audit uses your actual data.
Audit + Plan · 4 weeks
SaaS — Product Analytics
38→
Reliability score after the engagement
The churn metric was overstated by 22% in Q3 board reporting. A planned pricing experiment was designed around incorrect activation numbers. Two analysts spent ~10 hours/week manually reconciling reports.
Full Implementation · 6 weeks
Fintech — Transaction Pipeline
52→
Reliability score after the engagement
Audit + Plan · 4 weeks
Healthcare — Clinical Analytics
44→
Reliability score after the engagement
How we work
A repeatable, six-stage pipeline
Every engagement follows the same sequence, so nothing gets missed and every finding traces back to source data.
- 01
Ingest
Connect to your warehouse and load datasets. Read-only access, no production changes.
- 02
Profile
Scan schemas, column types, null rates, cardinality, and distributions across every table.
- 03
Check
Run 40+ automated quality checks: uniqueness, referential integrity, accepted values, freshness, near-duplicate detection.
- 04
Validate
Recompute KPIs from source data and compare against production dashboards. Flag every metric that diverges.
- 05
Score
Assign a 0-100 reliability score based on check results, weighted by severity and business impact.
- 06
Generate
Produce the audit report, dbt project, metric contracts, and interactive dashboards from validated data.
Engagement tiers
Start with a one-week diagnostic
The sprint fee is 100% credited toward a full engagement.
1 week
Diagnostic Sprint
A fast, low-friction entry point. We connect to your warehouse, run our automated pipeline, and deliver a severity-ranked scorecard of the top issues in your data systems.
- Scored audit report (0-100)
- Top findings ranked by severity
- Remediation priorities
- Executive summary + technical detail
4-6 weeks
Most popularFull Engagement
End-to-end: from warehouse connection to validated dashboards. We audit your data, formalize metric definitions, build the fix, and hand it off to your team.
- Everything in Diagnostic Sprint
- ASSUMPTIONS.md (data contracts)
- METRICS.md (canonical definitions)
- dbt project with 40+ tests
- Interactive Streamlit dashboards
- Drift detection and monitoring
Ongoing
Continuous Monitoring
After the engagement ends, automated monitoring keeps your metrics honest. Drift detection, freshness alerts, and quarterly reviews ensure regressions are caught early.
- Automated drift detection
- Freshness and volume alerting
- Quarterly audit reviews
- Priority support for new metrics