Building an AI Analyst That Actually Understands Your Store

Your Shopify sales dropped 30% last week. You open the analytics dashboard and see charts. Lots of charts. Sessions, conversion rates, funnel drop-offs. But no answers to the question you actually care about: what's broken and how do I fix it?

This is the gap between data and understanding. You have access to everything - every click, every purchase, every visitor - but no time to make sense of it. Traditional analytics gives you the raw materials but expects you to be the analyst.

This is the problem I set out to solve with Scryer. Not another dashboard to check. Not another chatbot waiting for you to ask the right questions. An AI analyst that understands your store and tells you what needs attention.

The Data Overwhelm Problem

Modern ecommerce analytics gives you more data than ever:

• Daily sales by product, by channel, by customer segment
• Conversion funnels showing drop-off at every step
• Session recordings of how people navigate your store
• Technical metrics like page speed and SEO scores
• Customer behavior patterns and cohort analysis
• Attribution data showing which marketing actually works

The problem isn't lack of data. It's having too much data and no time to analyze it.

A store owner's day is already full: fulfilling orders, managing inventory, creating content, running ads, answering customer support. Spending hours in analytics dashboards to figure out "why did sales drop?" isn't realistic.

What you actually need: someone to look at all that data, find what matters, and tell you "here's what's broken and here's how to fix it."

That's what an analyst does. That's what Scryer does.

Why AI Analytics Tools Fail Store Owners

Most AI analytics tools are glorified chatbots. They wait for you to ask questions, give generic advice that could apply to any store, and hallucinate recommendations that don't match your actual data.

The fundamental problem: they lack context.

A generic LLM doesn't understand the difference between "your tracking just started collecting data" and "your conversion rate is actually broken." It doesn't know that a brand new store with zero revenue isn't failing - it just hasn't launched yet. It can't distinguish between normal daily variance and a real performance issue.

For merchants, this means:

• Advice that doesn't apply to their specific situation
• False alarms about non-issues
• Missing real problems because they didn't know what to ask
• Still having to be an analytics expert to use the tool

The chatbot approach also misses something fundamental: you shouldn't have to ask. A real analyst doesn't wait for you to request a report. They proactively look at the data, spot issues, and bring them to your attention.

Why Daily Analysis? Why Not Real-Time?

Scryer runs once per day, analyzing the previous day's complete data. This is a deliberate architectural choice, not a limitation.

Real-time AI analysis is usually bullshit.

Most "AI analytics" tools are just GPT wrappers that generate responses on-demand. They're fast because they're shallow - they look at whatever data you show them and generate plausible-sounding advice without deep analysis.

Scryer does actual work:

• Pulls complete data from Shopify API (orders, products, customers, inventory)
• Processes session tracking data (every visitor, every page view, every interaction)
• Runs technical audits (crawling your site for SEO issues, measuring page speeds)
• Performs cohort clustering (grouping visitors by behavior patterns)
• Builds attribution models (which marketing actually drives sales)
• Generates comprehensive reports across multiple dimensions
• Feeds all of this through a multi-stage AI pipeline with verification

This takes time and costs money to compute.

Running this analysis costs up to several dollars per store per day in infrastructure and AI costs. It's not a quick API call to ChatGPT.

Daily analysis also makes sense for the business:

• E-commerce patterns emerge over days, not minutes. A one-hour dip in traffic isn't actionable. A three-day trend in cart abandonment is.
• Merchants don't need real-time alerts. You're not going to fix a page speed issue at 2am. You need to know about it when you're actually working.
• Complete data is more reliable than partial snapshots. Analyzing a full day's data (midnight to midnight in your timezone) gives cleaner patterns than real-time partial views.
• Historical context matters. Comparing complete days to complete days reveals trends that real-time analysis would miss.

The daily cadence means every morning, you know what happened yesterday and what needs attention today. Not instant, but actually useful.

The Architecture: Multi-Stage AI Pipeline

Scryer runs a daily analysis pipeline that processes your store data through several stages to ensure reliability and relevance.

Stage 1: Comprehensive Data Collection

Every day, Scryer pulls together a complete picture of your store:

• Sales & Revenue: Product performance, order patterns, revenue trends
• Customer Behavior: Purchase frequency, cart value, retention signals
• Conversion Funnel: Where visitors drop off, abandonment patterns
• Traffic Analysis: Visitor cohorts, channel effectiveness, behavioral patterns
• Technical Health: Page speed scores, SEO issues, site performance
• Competitive Context: Industry benchmarks for your store size and category

But raw data isn't enough. The system also captures context:

• Store maturity: Is this a test store, newly launched, or established business?
• Data quality: Real customer data or test orders?
• Tracking history: First report vs months of historical data?
• Business stage: Pre-launch, growth phase, or mature operation?

This context is critical. The same metric means different things for different stores. Zero revenue on a password-protected pre-launch store isn't a problem. Zero revenue on a live store that had sales last week is urgent.

Stage 2: AI Analysis with Domain Constraints

The data gets analyzed by an AI, but not without heavy constraints. The system is explicitly instructed on:

What to ignore:

• Session data on first reports (tracking needs time to collect)
• Normal business variance vs real issues
• Setup/configuration tasks (focus on business actions only)
• Problems the merchant can't control

What to focus on:

• Technical issues: performance, SEO, bugs
• Sales insights: patterns, trends, opportunities
• Growth opportunities: quick wins, optimizations
• Operational problems: inventory, fulfillment, customer experience
• Real anomalies: genuine spikes, drops, or unexpected patterns

How to frame findings:

• Match the store's stage (pre-launch guidance vs performance optimization)
• Provide specific examples (which product, which page, which file)
• Include business impact (estimated revenue effect, conversion impact)
• Give actionable next steps the owner can take today

These constraints prevent the AI from flagging non-problems. Without them, every first-time analysis would scream "no session data!" even though that's completely expected and normal.

Stage 3: Insight Lifecycle Management

Here's a key difference from one-shot AI tools: insights persist over time.

When an issue is identified, Scryer creates an insight that tracks it. If new evidence emerges, the insight gets updated with additional context. When the merchant fixes it, the insight is resolved with a note about what changed.

This solves a major problem with AI alerts: alarm fatigue. If the AI flags the same issue every single day, merchants learn to ignore it. If issues disappear without acknowledgment, there's no sense of progress.

The lifecycle approach means:

• Issues are tracked until actually resolved
• Related findings are grouped together (not 5 separate alerts for SEO problems)
• Historical context is preserved (what was tried, what worked)
• Merchants see progress as insights get resolved

The system decides which insights to create, update, or resolve based on:

• What changed since last report
• Which existing insights are still relevant
• Whether new findings are actually new or just rephrasing old issues

Between daily reports, merchants can also request a re-analysis of specific insights. This pulls current data and compares to the previous insight state to show where things stand right now. Useful when you've just fixed something and want to confirm it worked, or when you want to dive deeper into a particular issue without waiting for tomorrow's report.

Stage 4: Verification Loop

Here's the key to reliability: the AI checks its own work.

Before any insights are shown to the merchant, a verification step checks:

• No duplicate insights about the same underlying issue
• No invalid operations (updating insights that don't exist)
• No premature resolutions (closing issues that are still relevant)

If verification fails, the system loops back with the specific violations and tries again.

This catches common AI mistakes:

• Creating multiple insights for what's really one problem
• Resolving an issue just because it wasn't mentioned in the latest data

Only after passing verification do insights surface to the merchant.

Real Examples

Here's what Scryer actually surfaces:

What Makes This Actually Work

• Narrow scope. Scryer only does Shopify analytics. It doesn't try to be a general business intelligence tool. This allows deep domain expertise to be encoded as rules and constraints.
• Domain constraints over hoping the LLM understands. Rather than trusting the AI to "figure out" ecommerce, business logic is explicitly built into the system. The AI is told what matters and what doesn't for Shopify stores specifically.
• Multi-stage verification. Analysis happens, then verification checks that work, then copy gets rewritten for clarity. Each stage has a focused job. This catches mistakes before they reach merchants.
• Insight lifecycle, not one-shot alerts. Issues are tracked over time, updated with new evidence, and resolved when fixed. This maintains continuity and prevents the same alert from showing up every day.
• Continuous operation. Daily analysis means trends are caught early. The system has historical context to distinguish between "sales variance" and "something broke."
• Proactive intelligence. You shouldn't have to ask "what's wrong?" The analyst should tell you. Scryer runs automatically and surfaces what needs attention.
• Deep analysis, not quick responses. This isn't a GPT wrapper. Real computation happens: data processing, technical audits, cohort clustering, attribution modeling. It costs real money to run and takes time to do properly.

Try It Yourself

Scryer is live for Shopify stores at scryerapp.io.

Install it from the Shopify App Store. It connects automatically (via the Shopify Admin API and Web Pixel), and starts analyzing immediately. First insights appear within minutes.

7-day free trial, then $29/month for continuous daily analysis.

I'm actively working on this and looking for feedback from store owners. If you try it, I'd love to hear what works and what doesn't: support@scryerapp.io