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Ad-hoc questions
T1 · AUTONOMOUSPicks up "what was MRR last week by plan?" from #data-questions, writes SQL, runs on read-only role, replies with chart + query in 8-15 minutes.
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ROLE · R/04 · INSIGHT TRIBE
A digital Analyst that writes the SQL instead of just asking for it.
Connect your warehouse (Snowflake / BigQuery / Postgres / Redshift). Cyborg learns your schema, picks up ad-hoc questions in #data-questions, writes the query, runs it on a read-only role, returns the answer with the chart and the SQL. Plus weekly metric digest, dashboard upkeep, and "why did X drop" investigations — without the 3-day backlog.
FROM $599/month
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SCALES TO $1,599 (analytics lead)
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ONBOARDING 7 days
— 02 / WHAT IT DOES
— CAPABILITIES
From "kya scene hai" to a chart with the SQL.
Cyborg-Analyst is read-only on your warehouse, write on the BI tools (Metabase / Superset / Looker / Mode). It cannot mutate prod. It can answer pretty much any question you ask in natural language.
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Dashboard build & upkeep
T1 · AUTONOMOUSBuilds Metabase / Superset / Looker dashboards. Maintains existing ones (broken sources, deprecated metrics, stale filters). Owns dashboard hygiene.
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Cohort & funnel analysis
T1 · AUTONOMOUSRetention curves, signup funnels, activation cohorts, feature-adoption analysis. Standard outputs in #data-weekly.
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Anomaly detection
T1 · AUTONOMOUSWatches your top-30 metrics. When something drifts >2 stddev from baseline, opens an investigation thread automatically with first-cuts of "why."
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"Why did X drop?" investigations
T2 · GUIDEDSlice by every dimension (channel, geo, plan, version, device). Identify the segment driving the change. Posts findings + counter-checks before declaring a cause.
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Weekly metric digest
T1 · AUTONOMOUSMonday 09:00: 1-page summary of MRR / DAU / activation / churn / LTV / CAC. WoW change, trend, alerts. Posted to #data-weekly + email.
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A/B test analysis
T2 · GUIDEDReads experiment config, computes effect size + significance + power, calls winner / inconclusive / harm with proper guardrails. Counters the "p-hack" reflex.
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dbt model maintenance
T2 · GUIDEDAdds new dbt models in PR (not direct push), updates schema docs, fixes broken tests, refactors stale models. Reviewed by your data lead.
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Data quality monitoring
T1 · AUTONOMOUSRow-count checks, freshness checks, null-rate alerts, schema-drift alerts. Surfaces broken pipelines before stakeholders notice the dashboards are wrong.
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Stakeholder readouts
T2 · GUIDEDDrafts board-deck slides, investor-update charts, internal QBR sections. Founder approves before send.
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Defines & documents metrics
T1 · AUTONOMOUSMaintains the metric dictionary (one source of truth for "what is MRR"). Resolves "why does this dashboard say X but that one says Y" disputes by tracing definitions.
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Forecasting (light)
T3 · APPROVEDRevenue / DAU / signup forecasts using prophet / ARIMA / your existing model. Always presented with confidence intervals + assumptions; founder signs off before external use.
TRUST LEVELS ·
T1 Cyborg posts, you can audit later
T2 Cyborg drafts, human reviews before sending
T3 Human approves before action runs
T4 Cyborg never acts alone
— 03 / STACK
— FLUENCY
Warehouses, BI, transform layers it speaks.
Native connectors. SQL dialect-aware (Snowflake's QUALIFY, BigQuery's STRUCT, Postgres window functions — whatever your warehouse needs).
Warehouses — READ-ONLY
- Snowflake★★★★★
- BigQuery★★★★★
- Postgres★★★★★
- Redshift★★★★☆
- Databricks SQL★★★★☆
- DuckDB / MotherDuck★★★★☆
- ClickHouse★★★★☆
BI & viz — READ-WRITE
- Metabase★★★★★
- Superset★★★★★
- Looker / Looker Studio★★★★★
- Mode Analytics★★★★☆
- Tableau (read-only of dashboards)★★★★☆
- Power BI★★★★☆
- Hex / Deepnote (notebooks)★★★★★
Transform layer — PR-ONLY
- dbt Core / Cloud★★★★★
- SQLMesh★★★★☆
- Dataform★★★★☆
- Airbyte / Fivetran (config + check)★★★☆☆
- Airflow / Dagster (read DAG state)★★★☆☆
Languages — FLUENT
- SQL (all major dialects)★★★★★
- Python (pandas, polars, scipy)★★★★★
- R (when models prefer it)★★★☆☆
- Jupyter / Hex notebooks★★★★★
- JS (Observable / D3 charts)★★★★☆
- Vega-Lite / Plotly★★★★★
— 04 / DAY IN LIFE
— TUESDAY · 09:00 → 18:00 IST
A real Tuesday. B2C SaaS, Snowflake + Metabase.
Anonymised activity log. Every query verifiable in their Snowflake query history (look for the cyborg-cda-004 session tag).
- 09:00
Monday-morning weekly digest follow-ups
3 founders replied with follow-up questions on yesterday's digest. Cyborg answers all 3 in-thread within 25 min: chart + SQL + caveat.
- 09:32
Anomaly alert — signup conversion
Auto-detect: signup conversion
−14% WoWon mobile-web. Opens investigation thread in#data-investigations. First slice: country breakdown. - 10:48
Investigation: drop isolated to BR mobile-web
14 dimension cuts run. Drop is 100% on Brazil mobile-web traffic. iOS app + Android app + desktop unaffected. Posts findings + 3 hypotheses for engineering to validate.
- 11:20
Ad-hoc Q from PM
Kavya asks: "of users who hit the export-csv flow last month, how many returned in 7 days?" Cyborg answers in 9 min: 41.3% (n=2,847), with cohort table.
- 12:15
Dashboard repair
"Activation v3" Metabase dashboard broken since yesterday's
fct_usersschema change. Cyborg updates 4 questions, re-deploys. Pings#data-team. - 13:00
Lunch
Pauses scheduled queries. Long-running ones complete in background.
- 14:00
dbt PR — new model
Opens PR:
fct_export_events. Includes schema YAML, 4 tests (not_null, unique, accepted_values, freshness), description. Tagged data-lead for review. - 15:30
A/B test analysis
Marketing's "new pricing page copy" experiment ended Sun. Cyborg pulls data: lift +3.1% signup, p=0.071, n=18,400 per arm. Calls "inconclusive, recommend extend or call no-effect."
- 16:48
Founder Q (urgent)
"How many DE customers do we have, by ARR band?" 6 min later: chart, table, SQL link, with note "DE is GDPR-flagged, so no PII in this output."
- 17:30
BR investigation update
Eng confirms hypothesis #2: Stripe.js 3DS flow timed out for ~30% of BR mobile carriers since Mon deploy. Hotfix deploying. Cyborg notes "monitor recovery in 24h."
- 18:00
EOD log
Posts: 11 questions answered, 1 investigation closed, 1 dashboard fixed, 1 dbt PR opened, 1 A/B test called, 412 queries this week.
— 05 / SAMPLE DELIVERY
— REAL "WHY DID X DROP" INVESTIGATION · ANONYMISED
An actual investigation closed in 4 hours.
From auto-detected anomaly to root-cause confirmed. Real session, real SQL, names removed.
INV-2026-05-184
Signup conversion −14% WoW (mobile-web)
Trigger
Daily anomaly scan flagged: signup-completion rate fell from 11.8% (28-day baseline) to 10.2% on Mon, then 9.4% Tue — a 2.6σ deviation. Mobile-web only; iOS / Android / desktop steady.
SIGNUP CONVERSION · DAILY · LAST 14 DAYS
baseline 11.8%
actual
Investigation steps (timeline)
- 09:32 — Anomaly detected. Auto-opened thread.
- 09:48 — Cut by
device_type: drop isolated to mobile-web. App + desktop steady. - 10:14 — Cut by
geo: drop concentrated in BR (~85% of total drop). - 10:32 — Cut by
browser: BR Chrome mobile + BR Safari mobile both affected. Not browser-specific. - 10:48 — Posted findings + 3 hypotheses (Stripe 3DS / WhatsApp OTP / BR carrier latency) to
#data-investigations. - 14:20 — Engineering confirmed hypothesis #1 from session-replay: Stripe 3DS flow timing out at challenge step on BR carriers.
- 17:30 — Hotfix deployed (extended 3DS timeout 30s → 60s for BR).
- 17:48 — Recovery confirmed: signup completion back to 11.4% in last 30 min on BR mobile-web. Investigation closed.
Root cause
Mon's checkout-v3 deploy upgraded Stripe.js from 3.x to 4.x. The new version honours stricter 3DS timeouts (30s default vs 60s previously). On slow BR mobile carriers, the 3DS challenge often takes >30s, causing the auth to fail silently. Users see "card declined" and abandon.
The query that found it
SNOWFLAKE · analytics_db · ANALYST_RO role
14.2s · 184k rows scanned
-- signup conversion by device + geo, last 14 days vs 28d baseline WITH daily AS ( SELECT DATE_TRUNC('day', started_at) AS d, device_type, country_code, COUNT(*) AS starts, SUM(CASE WHEN completed_at IS NOT NULL THEN 1 ELSE 0 END) AS completes FROM fct_signup_attempts WHERE started_at >= CURRENT_DATE - 42 GROUP BY 1, 2, 3 ) SELECT d, device_type, country_code, starts, completes, ROUND(completes / NULLIF(starts, 0) * 100, 2) AS conv_pct FROM daily WHERE device_type = 'mobile_web' AND d >= CURRENT_DATE - 14 ORDER BY d DESC, starts DESC LIMIT 200;
rohan-eng
14:20 IST
Confirmed from session-replay. It's Stripe.js 4.x — we shipped Mon. 3DS challenge timing out on TIM/Vivo carriers. Patching now. Good catch.
cyborg-cda-004 17:48 IST
Recovery confirmed: BR mobile-web back to 11.4% conversion in last 30 min (vs 11.8% baseline). Closing investigation. Adding 3DS-timeout to monitored metrics so this doesn't take a week to catch next time. Postmortem doc draft in notion/postmortems/inv-184.
— 06 / TOOLS IT TOUCHES
— INTEGRATIONS
Read warehouse. Write dashboards. Never touch prod.
Always its own dedicated read-only role on the warehouse (ANALYST_RO). Always tagged session so every query is traceable to Cyborg.
— 07 / PRICING
— FLAT MONTHLY · NO USAGE METER
3 tiers. Pick your data scope.
Same model. Tier defines warehouse rows scanned, dashboard count, and forecasting authority.
$599/month
Ad-hoc questions, dashboard upkeep, weekly digest. Single warehouse + 1 BI tool.
- ~150 queries / week
- 1 warehouse, 1 BI tool
- Ad-hoc questions + weekly digest
- Basic dashboards
- Slack response (24h)
$999/month
Full ownership of analytics. Investigations, dbt PRs, A/B test analysis. Most teams start here.
- Unlimited queries
- Multiple warehouses + all BI
- "Why did X drop" investigations
- dbt model PRs
- A/B test analysis
- Anomaly detection on top-30 metrics
- Slack response (4h)
$1,599/month
Cross-product. Forecasting. Board-deck data. Owns metric dictionary + governance.
- Multi-product / multi-tenant
- Forecasting (T3 sign-off)
- Board / investor deliverables
- Metric governance
- QBR / OKR data sections
- Slack response (1h)
- Dedicated A'nil sync (monthly)
14-DAY EVALUATION · 30-DAY MONEY-BACK · MONTH-TO-MONTH · CANCEL ANYTIME · See full pricing →
— 08 / ONBOARDING
— DAY 1 → DAY 7
Brief to first answered question in 3 days.
Analyst ramps quickest because the warehouse is already structured. Day 4 onward is real work.
- DAY 0
You sign
Contract + DPA. Your data lead provisions
ANALYST_ROrole. - DAY 1
Kickoff (60 min)
Founder + your data lead. Walk through warehouse, key tables, metric definitions, "questions that come up most."
- DAY 2
Schema ingestion
Cyborg reads
information_schema,dbtmanifest, last 90 days of query history. Writes "what I understood" doc back, asks for clarification on 5-10 ambiguous tables. - DAY 3
Shadow questions
Cyborg picks 5 already-answered questions from history, re-answers them privately, compares to ground truth. Calibrates style.
- DAY 4
First public question
Goes live in
#data-questions. Data lead reviews each answer for first 2 days, then progressively releases. - DAY 5-7
Trust calibration → autonomous
By Day 7, ad-hoc + dashboards + weekly digest run autonomously. Investigations + dbt PRs stay reviewed for first 30 days.
— 09 / FAQ
— DATA-LEAD QUESTIONS
Sawaal jo har data lead poochta hai.
Can it write production data? Drop tables? Mutate?
No. The connector role is read-only by design (ANALYST_RO on Snowflake, roles/bigquery.dataViewer on BigQuery, analyst_ro on Postgres). The only "write" Cyborg can do is on the BI tool layer (dashboards, queries, collections) and on dbt via pull request. It cannot push to your dbt repo, run a model, or mutate any warehouse data. We can show you the IAM trail.
What about hallucinated SQL? Wrong joins?
Three guards: (1) the query plan is validated against your dbt manifest before execution — if it references a column that doesn't exist, it never runs. (2) Cyborg posts the SQL alongside every answer, so anyone can check the join logic. (3) Every answer carries a confidence score; below 70%, it routes to your data lead instead of posting.
How does it handle PII / GDPR / sensitive columns?
Cyborg respects your existing column-level security: if your ANALYST_RO role can't see PII, neither can Cyborg (no bypass). For aggregated outputs only, we add a default k-anonymity floor (no group with <5 users in any cell). For GDPR-flagged geos, PII is masked even in aggregates. See security.
Will it spam my Slack with anomaly alerts?
No — that's calibrated during onboarding. Default thresholds are conservative (2σ + minimum sample size + minimum effect size). You can tighten further in the kickoff. Average customer gets 2-4 anomaly threads per week, not per day.
Will it be expensive on my warehouse bill?
Cyborg's queries respect the same warehouse-cost guards your team uses (max scanned bytes, query timeout, cluster sizing). Plus it caches identical questions across the team — if 3 PMs ask "what was MRR last week?" within an hour, it runs once. Customers typically see warehouse spend go up 5-10% in exchange for 3-5x more questions answered.
Does it understand my business context?
It reads your dbt model descriptions, your metric dictionary, and your historical question/answer pairs. If your context is undocumented (lots of "active user" definitions floating around), Day 7 outputs will reflect that ambiguity. Onboarding includes 60-90 min of definition cleanup with your lead, which usually saves the team weeks of "which number is right" later.
Can it replace my analyst?
It absorbs the ad-hoc question queue (typically 60-70% of an analyst's time). The strategic work — choosing what to measure, designing experiments, telling the story to the founder, deciding pricing — stays human. Most teams use Cyborg-Analyst so their senior analyst is freed for that strategic work, not to fire anyone.