Solo Analytics -- Know exactly what is working in your business - Vertical Agent Spec

One-line definition

An agent that pulls revenue, traffic, and conversion data from a solopreneur's connected tools weekly, identifies what actually changed and why, and delivers a prioritized action list before Monday morning.

The workflow it owns end-to-end

On a configured schedule (daily digest or weekly rollup), the agent pulls fresh data from connected sources: Stripe MRR and churn events, Google Analytics 4 sessions and goal completions, and optionally Shopify or Lemon Squeezy transaction logs.
It compares current period to prior period and to a rolling 90-day baseline, flagging metrics that moved more than one standard deviation in either direction.
For each flagged signal, it traces a probable cause chain: traffic source shift, pricing page bounce rate change, trial-to-paid conversion drop, or refund spike.
It produces a plain-language brief ranked by estimated revenue impact, not by data volume, so the operator reads the three things that matter rather than a dashboard full of equal-weight numbers.
It sends the brief via the operator's preferred channel (email, Slack, or Telegram) and archives it in a connected doc for longitudinal comparison.

What it knows that a generic LLM doesn't

What "healthy" looks like for a sub-$10k MRR indie product: trial conversion windows of 7 to 14 days are normal, monthly churn above 5% is structural, and a single viral Twitter thread can distort a week's data so badly it should be isolated rather than averaged.
Stripe's distinction between MRR movements from new business, expansion, contraction, and churn, and why conflating them produces bad decisions.
GA4's event model and the specific misconfigurations (missing scroll depth, broken form submission events) that are endemic in no-code Webflow and Framer builds used by this audience.
The difference between a traffic drop that is a Google algorithm penalty versus a seasonal pattern versus an expired Product Hunt front-page bump.
When a Shopify abandoned cart rate spike is a payment gateway issue versus a price anchor problem versus a mobile checkout UX regression.
Which metrics solopreneurs habitually over-index on (page views, Twitter impressions) versus the ones that predict whether they eat next month (trial activation rate, second-month retention).

What it explicitly declines

Making any changes to connected accounts, including pausing campaigns, adjusting pricing, or issuing refunds, even when instructed to.
Providing tax, legal, or accounting interpretation of revenue figures, including whether a metric pattern constitutes reportable income or a deductible loss.
Projecting future revenue with a specific number. It will describe a trend and its historical trajectory; it will not produce a forecast the operator might treat as a commitment.
Diagnosing product-market fit. It can tell you what the data says. It cannot tell you whether to keep building.

Tools and integrations required

Stripe API (revenue events, subscription lifecycle, refund data)
Google Analytics 4 Data API (sessions, conversions, acquisition source breakdown)
Shopify Admin API or Lemon Squeezy API (transaction and refund data, optional, vertical-dependent)
Notion or Google Docs (brief archival and longitudinal record)
Slack, email (SendGrid or Postmark), or Telegram (brief delivery)
A lightweight schema store, probably Postgres or Supabase, to hold the operator's 90-day baseline and metric definitions so comparisons are consistent across runs

Trust escalation: when it pings a human

MRR drops more than 15 percent week over week: the agent posts the data and stops. It does not suggest causes until the operator confirms the data connector is not broken, because a misconfigured webhook looks identical to a real churn event.
A data source returns stale or malformed data: the agent flags the gap explicitly rather than silently omitting the source or interpolating. A brief built on partial data with no warning is worse than no brief.
The operator asks the agent to act on a finding rather than report it, for example "cancel all trials that haven't activated." The agent declines and surfaces the request for human review.
Any signal that could indicate fraud, such as a sudden spike in international transactions followed by a refund cluster, gets flagged to the operator before any summary framing is applied.

Pricing model

The SaaS seat model at $25 per month is probably the wrong shape here, given that Stripe's own dashboard, GA4, and Shopify Analytics together cover most of what this agent does at zero marginal cost. A more honest pricing structure is a per-brief model: $9 per delivered brief, billed only when the agent completes a full run with at least two connected sources returning valid data. An operator running weekly digests pays roughly $36 per month, which is already above the $25 SaaS anchor, so the value proposition needs to be the saved time (30 to 60 minutes of manual pulling) rather than the insight itself. Whether a solo founder at $8k MRR pays that consistently is the real question, and the honest answer is: some will, most won't, and the ones who do are probably already analytically inclined enough that they mostly want the automation, not the interpretation.

Differentiation from a generic LLM wrapper

The actual defensible piece is not the analysis. A founder can paste their Stripe CSV into Claude today and get a reasonable summary. The defensible piece is the connection layer and the memory: this agent holds a calibrated baseline for this specific business, knows that last March's traffic spike was a Product Hunt launch and should be excluded from the seasonal average, and runs on a schedule without requiring the operator to remember to check. The moat is not intelligence. It is context accumulation and zero-friction cadence. The honest risk is that this moat is shallow. Any sufficiently motivated founder can replicate it with a weekend of n8n workflows. The agent wins only if the setup cost of the DIY version, plus the ongoing maintenance when Stripe changes its API schema, exceeds the subscription cost by enough margin that the operator does not bother switching.