In-situ architecture  ·  Early access now open

Ask any operational question against your live warehouse. Bayanate generates and runs the SQL, traces the root cause, and delivers a structured answer — without moving a single row.

BIRD-SQL benchmark  71.4% execution acc. · Avg. SQL compile  < 1.8s · Rows moved  0
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BAYANATE · AUTONOMOUS DISCOVERY · IN-SITU
// autonomous trace · prod_db · payment_events
SELECT plan_tier, signup_channel,
  count(*) FILTER (WHERE status='failed') AS failures,
  round(avg(amount_usd)::numeric,2) AS avg_amount
FROM prod_db.public.payment_events
WHERE created_at >= NOW() - INTERVAL '7 days'
GROUP BY plan_tier, signup_channel
HAVING count(*) FILTER (WHERE status='failed') > 5;

⚠ anomaly: Pro tier failed payments ▲ 6.8% (vs 1.2% avg)
⚡ root cause: Tabby webhook latency 03:14Z
downstream: $31,400 GMV at risk · 38 accounts
✓ trace complete · 1.6s · 0 rows moved
// schema drift monitor · dw.fct_sessions · daily
SELECT column_name, data_type, is_nullable
FROM information_schema.columns
WHERE table_name = 'fct_sessions'
AND table_schema = 'analytics'
ORDER BY ordinal_position;

⚡ drift detected: session_duration_ms
expected: BIGINT · actual: DOUBLE PRECISION
⚠ 3 downstream models affected: fct_engagement,
fct_activation, dim_user_segments
✓ remediation path compiled · 2.1s · 0 rows moved
// expansion monitor · snowflake · crm_prod
SELECT account_tier, cohort_month,
  sum(expansion_mrr_usd) AS expansion_mrr,
  lag(sum(expansion_mrr_usd),1) OVER
    (PARTITION BY account_tier ORDER BY cohort_month)
FROM crm_prod.public.subscription_events
GROUP BY 1,2 ORDER BY 2 DESC LIMIT 12;

⚠ SMB expansion flat 3 consecutive months
correlated: activation_rate < 0.42 threshold
⚡ trigger: onboarding change deployed 2026-03-12
✓ cohort analysis complete · 1.4s · 0 rows moved
warehouse: prod_db · schedule: 5m · engine: native ● LIVE
Postgres · ClickHouse · BigQuery · Snowflake Runs inside your VPC — no data leaves
01Discovery Layer

Insights that find you.
No query required.

The moment you connect a data source, Bayanate builds a semantic map of your warehouse and derives automated monitors for every domain your team owns. No prompt. No dashboard. No waiting.

Active Monitors
+ 6 more domains
Bayanate Alert Lead Data Engineer just now
Automated Root-Cause Summary
Triggerdaily_revenue_rollup anomaly detected Tableprod_db.public.payment_events Schemapayment_amount DECIMAL(12,2), status VARCHAR(32) FindingFailed payment rate 6.8% (vs 1.2% 30d avg) Spike began2026-06-01 22:10 UTC Correlated colpayment_provider = 'tabby'
Impact

Estimated $31,400 GMV blocked in last 12h across 38 accounts. Tabby API timeout threshold breach → payment_intents stuck at 'pending' → revenue_rollup double-counting.

Action Plan

Set Tabby timeout to 8s (was 30s). Re-run daily_revenue_rollup --date 2026-06-01. Monitor payment_events.status hourly for 24h.

Automated Root-Cause Summary
Triggerengine_latency_monitor breach Clusterprod-clickhouse-01, shard-4 Metricp95 latency 7,103ms (threshold 1,200ms) Slow queries38 in 5-min window Window2026-06-02 03:47–04:31 UTC Root tableevents_partitioned_v3 (merge op)
Impact

Background merge consumed 87% I/O on shard-4. 3 dbt models timed out: fct_sessions, fct_revenue_events, dim_users. Dashboard refresh delayed 44 minutes.

Action Plan

Move nightly ETL INSERT batch to 02:00 UTC. Set merge_tree_max_bytes_to_merge_at_once = 10GB. Re-run 3 affected dbt models.

Automated Root-Cause Summary
Triggeractivation_rate_monitor threshold breach Datasetbigquery_prod.analytics.user_events MetricWeek-2 activation 31% (vs 48% 90d avg) Affected cohortSMB signups May 12 – Jun 1 (n=342) Correlationonboarding_flow = 'v3_streamlined' MRR at risk$18,200 (expansion blocked)
Impact

Onboarding v3 removed the guided connection step on day 3. 58% of at-risk accounts never completed their first integration. Expansion likelihood drops to 12% without activation.

Action Plan

Re-introduce guided step for SMB tier. Trigger in-app nudge for 342 at-risk accounts. A/B test v3 vs v2 for next 500 signups — measure day-14 activation delta.

Automated Root-Cause Summary
Triggerexpansion_mrr_monitor stall detected Datasetsnowflake_crm.public.subscription_events MetricSMB expansion flat 3 consecutive months SegmentSMB · Mid-Market expansion delta Correlationseat_count stagnant post-upgrade prompt removal Revenue delta-$24,600 vs Q2 expansion pace
Impact

Upgrade prompt removed in March product sprint eliminated the primary expansion trigger for SMB. 67 accounts eligible to expand — none received the prompt in the last 90 days.

Action Plan

Re-enable seat-limit prompt at 80% seat utilization. Sequence 67 eligible accounts into expansion campaign. Restore upgrade CTA in the in-app header for SMB tier.

No one wrote a query. No one opened a dashboard.

02Artifact Generation

From chat to
production-grade assets.

One request drops a structured, citable deliverable directly into your workflow. Not a screenshot. Not a CSV export. A professional document built from your actual data layer.

PDF Briefing ↓ executive_summary.pdf
Executive Summary — two-paragraph plain-language narrative, calibrated for non-technical stakeholders. No jargon. No raw numbers without context.
Trend Lines — 30/60/90-day metric series per KPI, annotated at every inflection point with the event or deploy that caused it.
Data Lineage — every figure cites the exact table.column, query hash, and run timestamp. Audit-ready from day one.
Structured for exec review. Drops into board decks as-is.
Excel Export ↓ mrr_analysis.xlsx
Populated rows — real values pulled directly from your warehouse at query time. No placeholder data. No sample exports.
Active formulas — SUMIF, pivot tables, cohort arrays — live and recalculating. Finance can extend the model without rebuilding it.
Linked definitions — a metric glossary tab tied to your semantic layer definitions so every term matches your source of truth.
Hand it to Finance. They can edit it. You don't need to explain it.
Slide Deck ↓ board_update.pptx
Five-slide structure — Exec summary, KPI snapshot, root cause, downstream impact, action items. No filler slides. Every claim sourced.
Charts in your palette — color tokens derived from your brand config. No generic blue bars. Looks like it came from your design team.
Presentation-ready — slide notes include speaker talking points referencing the underlying data. Zero editing required before the meeting.
Present it in the same meeting where you discovered the issue.
03Persistent Views

Pin a question once.
Always see the answer.

One conversational request spins up a permanent, real-time metric view — pinned to Slack, your internal wiki, or the Bayanate dashboard. The data refreshes on your warehouse schedule. The question never needs to be asked again.

Pinned by @omar  · "Track expansion MRR by segment weekly"
LIVE
Expansion MRR WoW
$8,420
▲ 3.2% vs last week
Churn Rate (mo.)
2.1%
▼ 0.4pp — improving
Net New MRR (mo.)
$34,100
▲ 11% vs prior mo.
ARR at Risk
$12,880
⚠ 3 accounts flagged
Cohort Trend · Last 8 Weeks
SMB
$4,200 avg expansion / acct
Mid-Market
$9,100 avg expansion / acct
Enterprise
$22,400 avg expansion / acct
System Watchlist
dbt model: mrr_expansion_weekly
last run: 06:00 UTC
OK
postgres: prod_db.subscription_events
freshness: 4 min ago
OK
stripe webhook: pending events > 120s
detected: 2 events
WARN

Stays live in Slack, Notion, or any embedded iframe. Refreshes on your warehouse schedule.

04Automated Trace

Set a guardrail.
Let Bayanate trace the root cause.

Specify a tracking boundary in natural language. Bayanate compiles a native SQL monitor against your exact schema, runs it on your warehouse schedule, and fires a full post-mortem the moment a threshold is breached.

TRACE LOG  · engine_latency_monitor · cluster: prod-clickhouse-01
T+00m The Guardrail

A user specifies a tracking boundary in natural language.

@bayanate trace engine latency anomalies by cluster
T+01m In-Situ Compilation

Bayanate reads your live schema — no replication, no ETL — and compiles a production-grade SQL monitor. Runs every 5 minutes, natively inside your warehouse.

Reading: clickhouse_prod.system.query_log clickhouse_prod.system.metric_log SELECT cluster, toStartOfFiveMinute(event_time) AS window_start, avg(query_duration_ms) AS avg_latency_ms, quantile(0.95)(query_duration_ms) AS p95_latency_ms, countIf(query_duration_ms > 3000) AS slow_query_count FROM system.query_log WHERE event_time >= now() - INTERVAL 1 HOUR AND type = 'QueryFinish' GROUP BY cluster, window_start HAVING avg_latency_ms > 1200 -- guardrail threshold ORDER BY window_start DESC Schedule: every 5m · warehouse: clickhouse_prod · rows read: ~42k · rows moved: 0
T+47m Breach Detected
⚡
Threshold breached · 2026-06-02 03:47 UTC
cluster: prod-clickhouse-01 avg_latency_ms: 2,841ms (threshold: 1,200ms) p95_latency_ms: 7,103ms slow_query_count: 38 queries in 5-min window
T+49m Resolution — Post-Mortem
Incident Summary
p95 latency on prod-clickhouse-01 exceeded 7s for 14 consecutive 5-min windows. Elevated from 03:47 UTC, self-resolved at 04:31 UTC.
Confirmed Root Cause
Background merge on events_partitioned_v3 consumed 87% I/O on shard-4. Triggered by a 340M-row INSERT from dbt_prod.stg_events nightly ETL.
Downstream Impact
3 dbt models timed out: fct_sessions, fct_revenue_events, dim_users. Dashboard refresh delayed 44 min. 2 alert pipelines skipped 04:00 UTC window.
Action Plan
1. Move ETL batch to 02:00 UTC
2. Set merge I/O cap to 10GB
3. Re-run 3 affected dbt models
4. Tighten monitor threshold to p95 > 2,000ms
● TRACE COMPLETE elapsed 2.1s 0 rows moved out of warehouse affected: prod-clickhouse-01, shard-4, 3 dbt models
05Integrations

Direct connection.
Zero gravity.

Bayanate speaks the native query dialect of your data layer. No middleware, no replication, no ETL pipeline to maintain. Connect a read-only credential and the agent begins mapping your schema within seconds.

Data Warehouses
Snowflake BigQuery Amazon Redshift ClickHouse Databricks Apache Iceberg
Production Databases
PostgreSQL MySQL CockroachDB PlanetScale Supabase
Pipeline & Transformation
dbt Core Apache Airflow Prefect Dagster Fivetran
Cloud Observability
AWS CloudWatch Datadog Metrics API Google Cloud Monitoring Azure Monitor
⬡
Custom MCP Server Support
Any data source that exposes a Model Context Protocol (MCP) server connects to Bayanate out of the box. Proprietary internal APIs, in-house metrics stores, custom lakehouses — if you can expose it via MCP, Bayanate can query it natively. Open standard. No vendor lock-in.
06Get Started

See the agent live
on your stack.

Connect a read-only credential. Bayanate maps your schema, runs your first trace, and has a live monitor running — before you finish your coffee.

How it works:
✓
Connect a read-only credential — SELECT only, nothing leaves your infra
✓
Ask your three hardest operational questions — live, against your schema
✓
Walk away with a working trace and one live monitor
Zero risk. No deployment required. Cancel any time.
1.8s
Avg Compilation Speed
Schema read to production-grade SQL. Natively inside your warehouse.
0
Rows Moved Out of Your Warehouse
Every query runs natively in-situ. Your data never leaves your perimeter.

Ready to ask your first operational question?

Bayanate connects to your stack in minutes. The first trace runs before the onboarding call ends.