AI operations copilots for lean startup teams
Start every morning with a clear brief of what requires your attention. OpsTower connects your analytics, databases, logs, and deploys — so you don't have to.
Built for founders and lean engineering teams who don't have time to investigate across five tools every morning.
Daily review window covered automatically
Faster path from symptom to likely root cause
Extra dashboards your team needs to babysit
Ops report snapshot
Signups up 18% week-over-week, activation conversion down 6% on the new onboarding flow. Recommend reviewing step three of the onboarding process before launch traffic compounds it.
Overnight 5xx burst grouped to one code change in auth made yesterday. Similar fingerprint found in Cloudflare logs and GitHub diff from 23:17 UTC.
The activation drop correlates with the onboarding A/B test launched Monday. Users on variant B complete step 3 at 38% vs 71% on control. Likely cause: new form validation introduced a silent error.
Two operating modes
Mode 01
Scheduled agents review the last 24 hours for you, then ship concise summaries before anyone starts hunting through dashboards.
Mode 02
Stand up specialist agents with their own system access and knowledge, then ask them to investigate whenever something looks off.
See what you get every morning
Sample daily reports showing what OpsTower generates for a coffee subscription SaaS startup. Every section — the health scorecard, the issue findings, the prioritized recommendations — is generated automatically from connected data sources. No templates, no manual input.
Product Analytics
Daily metrics, anomaly detection, and actionable recommendations for product, engineering, and marketing.
Wednesday, March 25, 2026
Tuesday's email campaign ("Spring Roast Drop") drove a record traffic day — 4,210 unique visitors, up 62% — but trial-to-paid conversion quietly dropped to 4.1%, the lowest in three weeks. The root cause: a Stripe webhook timeout introduced in Monday's deploy is silently failing 8% of checkout completions, so users think their subscription didn't go through and abandon. MRR still grew to $48,400 thanks to volume, but the team is leaving roughly $3,800/day on the table until the webhook issue is fixed.
| AREA | STATUS | SUMMARY |
|---|---|---|
| Acquisition (new visitors, signups) | HEALTHY | 4,210 unique visitors today — up 62% from yesterday (2,598) and up 41% vs. last Wednesday (2,980). The Spring Roast campaign is driving strong top-of-funnel traffic. |
| Engagement (sessions, feature usage) | HEALTHY | 5,840 sessions with an average duration of 4m 32s, up from 3m 48s yesterday. Flavor Quiz completions hit an all-time high of 680. Users are exploring the product. |
| Activation (trial starts, first order) | NEEDS_ATTENTION | 312 new trial signups (healthy), but only 64 converted to a paid subscription — a 4.1% rate vs. the 7-day average of 6.8%. The drop started Monday and is getting worse. |
| Revenue (MRR, transactions) | NEEDS_ATTENTION | MRR grew to $48,400 (+$620 net new today), but 23 checkout attempts failed silently — an estimated $3,800 in lost daily revenue. Without the webhook bug, today would have been the best revenue day this quarter. |
| METRIC | TODAY (MAR 25) | YESTERDAY (MAR 24) | 7-DAY AVG (MAR 19–25) | SAME DAY LAST WEEK (MAR 18) | DAY-OVER-DAY | VS. LAST WEDNESDAY |
|---|---|---|---|---|---|---|
| Unique Visitors | 4,210 | 2,598 | 2,870 | 2,980 | +62.0% | +41.3% |
| Total Sessions | 5,840 | 3,410 | 3,650 | 3,920 | +71.3% | +49.0% |
| New Signups | 312 | 198 | 215 | 224 | +57.6% | +39.3% |
| Trial-to-Paid Rate | 4.1% | 5.2% | 6.8% | 7.1% | −21.2% | −42.3% |
| Paid Conversions | 64 | 52 | 73 | 80 | +23.1% | −20.0% |
| Avg Session Duration | 4m 32s | 3m 48s | 3m 55s | 4m 05s | +19.3% | +11.0% |
| Flavor Quiz Completions | 680 | 390 | 420 | 445 | +74.4% | +52.8% |
| Gift Subscriptions Sent | 89 | 34 | 41 | 38 | +161.8% | +134.2% |
| Subscription Upgrades | 28 | 22 | 19 | 18 | +27.3% | +55.6% |
| Churned Subscriptions | 12 | 15 | 14 | 11 | −20.0% | +9.1% |
| MRR | $48,400 | $47,780 | $47,200 | $45,900 | +1.3% | +5.4% |
| Revenue Today | $4,480 | $3,640 | $3,290/day | $3,120 | +23.1% | +43.6% |
Systems Operations
System health, error tracking, deployment correlation, and prioritized issue resolution.
Report Date: Thursday, 2026-03-26
Generated: End of day | Coverage: 2026-03-20 → 2026-03-26
The platform is partially degraded with two high-severity issues persisting since Monday's payment-service deploy. The Stripe webhook timeout (checkout.session.completed handler exceeding Cloudflare's 30s CPU limit) is now confirmed as the root cause of the 8% checkout failure rate flagged in yesterday's analytics report — 68 subscriptions have been charged but never activated over the past 3 days. A Supabase connection pool exhaustion issue emerged overnight, causing intermittent 500s on the subscription API for ~12 minutes before self-recovering. The recommendation engine's Cloudflare Worker is hitting CPU time limits on 4.2% of requests, producing stale "You might also like" suggestions for affected users. On the positive side, the hotfix for the webhook race condition (commit b7e3a42) deployed yesterday is confirmed working — duplicate subscription events dropped to zero.
At a Glance:
| SYSTEM | STATUS |
|---|---|
| Checkout & Payments | Degraded — 8% of checkouts failing silently due to webhook timeout, 68 stuck subscriptions over 3 days |
| Subscription API | Recovered — Supabase pool exhaustion caused 12 min of 500s overnight; self-healed after idle connections were reaped |
| Recommendation Engine | Degraded — 4.2% of requests hitting Worker CPU limit, returning stale suggestions |
| Email Delivery (Resend) | Healthy — 100% delivery rate, avg latency 1.2s |
| Inventory Sync | Healthy — all warehouse feeds processing within SLA |
| Scheduled Jobs (Cron) | Healthy — all 6 cron triggers firing on schedule |
Why teams keep it open
Morning reports call out anomalies, trends, and metric movement before your standup turns into archaeology.
Logs and failures are grouped into digestible findings so people can respond without spelunking five tabs deep.
Ask natural-language questions across logs, analytics, and source code to understand what changed and where to look next.
How it works
Connect analytics, observability, source code, and the systems each specialist agent should understand.
Run agents on a schedule for automated reporting or kick them off in chat when a human needs an investigation.
OpsTower correlates the evidence and replies with findings, likely causes, and concrete next steps.
Who it helps
Leadership gets the scheduled view. Builders get the live investigation workflow.
From bug to solution in minutes
Once the daily reporting has done its job, chat mode takes over. A developer, QA engineer, or analyst can launch a debugger agent, let it correlate evidence across systems, and get a grounded explanation before the bug becomes everybody's afternoon hobby.
Bug report
Comes from support, QA, or production noise
Agent investigation
Queries systems and knowledge in parallel
Clear next step
Likely cause with evidence attached
A bug report comes in
Support, QA, or a teammate drops a symptom into chat instead of opening a dozen tabs and hoping for chemistry.
Debugger agent investigates
It checks logs, analytics, deploy context, and source code in parallel, then correlates the signals.
OpsTower explains the issue
You get the likely cause, supporting evidence, and a suggested next move instead of a shrug in Slack form.
Ship the fix faster
The team can verify the change and move on without burning half the day on manual debugging theater.
"Checkout failures started after the latest pricing rollout. Logs show malformed discount payloads, the deploy diff changed coupon parsing, and conversion dipped at the same minute. Revert the parser update or guard empty discount codes, then re-test the payment flow."
Connected systems
Start with automated reporting, expand into live investigation across logs, databases, and source code, and let the same agent layer answer both “what changed?” and “can you look into this right now?”
Analytics
PostHog, GA4, Amplitude, Mixpanel
Error Tracking
Sentry issues, stack traces, and trends
Payments
Stripe, Paddle, Chargebee, and Braintree subscriptions, revenue, and transactions
Logs
Axiom, AWS CloudWatch, Cloudflare Logs, Google Cloud Logging
Databases
ClickHouse, MongoDB, DynamoDB, Firestore, and more
Source Code
GitHub, Bitbucket, and GitLab context for deploys, diffs, and MRs
Ticketing
Linear and Jira for issue search, tracking, and auto-creation
Social
X (Twitter), LinkedIn, Facebook Pages, and Instagram engagement metrics
Advertising
Meta Ads and Google Ads campaign performance and spend analytics
App Stores
App Store Connect reviews, ratings, and app metadata
Customer Support
Intercom and Zendesk tickets, CSAT, conversations, and help center data
External APIs
Connect any REST API — internal services, third-party platforms, or partner feeds
Outputs
Email digests, tickets, and agent chat investigations
Issues found. Tickets filed. Before coffee.
OpsTower's systems operations reports don't just tell you what broke — they file the tickets for you. Every error, every regression, every code bug discovered overnight gets a Linear issue with the right team, priority, and evidence attached. Your engineering standup starts with a list of what to fix, not a debate about what happened.
06:30 — Morning ops report
3 issues found overnight. 2 classified as CODE_BUG, 1 OPERATIONAL.
"Auth token refresh fails silently after session timeout — users see blank dashboard. High priority, assigned to Backend team."
"Payment webhook retries exhausted for 12 subscription renewals — Stripe returns 400 on malformed payload. Medium priority."
All tickets linked in the report. Your team knows what to fix before standup.
