AI Native Growth Engineers

The Problem

Building retargeting audiences and preparing enhanced conversion data for ad platforms is a manual, repetitive process. Someone queries the data, defines the segments, formats the export to each platform's specification, and uploads it. Most teams either skip it entirely or do it inconsistently, which means their ad platforms are optimizing on incomplete signals.

How It Works

1. Agent queries conversion events and user behavior data from BigQuery.
2. Identifies high-value converters for offline conversion import, and engaged non-converters for retargeting.
3. Generates properly formatted CSV files: enhanced conversions with hashed PII, plus audience CSV for Customer Match.
4. Summary of segment definitions and expected impact is produced alongside the exports.

Output

• enhanced_conversions.csv, ready for Google Ads offline conversion import
• retargeting_audience.csv, ready for Customer Match upload
• Segment definition summary with expected impact

Integrations

The Problem

Funnel analysis is manual and slow. Someone pulls the data, spots a drop-off, writes up a hypothesis, and creates a brief. By the time it reaches the testing queue, the data is stale and the context is lost. The gap between insight and action is where most optimization programs stall.

How It Works

1. Agent queries funnel data across landing page, pricing, signup, and conversion stages.
2. Identifies the biggest drop-off point and quantifies the opportunity in lost conversions.
3. Generates a structured A/B test hypothesis based on the specific drop-off pattern.
4. Writes a complete experiment brief with problem statement, hypothesis, control vs. variant, primary metric, and success criteria.
5. Creates a task in ClickUp with the experiment brief attached, ready for implementation.

Output

• Funnel analysis summary with drop-off percentages at each stage
• Structured hypothesis statement
• Complete experiment brief document
• ClickUp task with the brief attached and properly formatted

Integrations

The Problem

Most teams find out about traffic drops, conversion spikes, or revenue anomalies days after they happen, usually when someone checks a dashboard or a stakeholder asks a question. By then, the damage is done and the root cause is harder to trace. Continuous monitoring requires either dedicated headcount or brittle rule-based alerts that generate noise.

How It Works

1. Agent scans 30 days of daily metrics across sessions, conversions, revenue, and conversion rate.
2. Detects anomalous days using statistical thresholds (greater than 2 standard deviations from the mean).
3. Explains what changed, when it happened, and hypothesizes potential causes based on context.
4. Sends a formatted Slack alert with the metric, expected vs. actual values, timing, and suggested areas to investigate.
5. An n8n workflow is generated to run this check on a daily schedule without manual intervention.

Output

• Anomaly detection summary with statistical analysis
• Formatted Slack alert with context and recommended next steps
• n8n workflow JSON for automated daily monitoring

Integrations

The Problem

PLG teams know that certain behaviors during a trial correlate with conversion, but identifying those behaviors requires combining user profiles, feature usage events, and conversion data across multiple systems. The analysis takes days, the findings are often inconclusive, and by the time at-risk users are identified, many have already churned.

How It Works

1. Agent queries trial user data including signup dates, conversion status, feature usage events, session frequency, and engagement timing.
2. Identifies conversion predictors: which features converters use that non-converters don't, and how quickly converters engage.
3. Generates actionable recommendations: features to promote in onboarding, engagement thresholds for at-risk identification, and suggested nudges.
4. At-risk trial users are segmented and can be synced to HubSpot for targeted campaigns.

Output

• Conversion correlation analysis with features ranked by conversion lift
• "Aha moment" identification with supporting data
• Onboarding optimization recommendations
• At-risk user segment ready for HubSpot sync

Integrations

The Problem

Tracking issues and UTM inconsistencies are invisible until they corrupt your data. Missing events mean incomplete funnels. Inconsistent UTMs fragment your attribution. Fixing these problems manually means cross-referencing event specifications against live data and scanning hundreds of campaign URLs for naming errors.

How It Works

1. Agent receives the expected event specification and compares it against actual analytics data.
2. Reports missing events, unexpected events, and events with suspiciously low volume that suggest tracking issues.
3. Scans campaign URL data and GA4 traffic sources for UTM hygiene issues: inconsistent naming, typos, and missing parameters.
4. Generates cleanup recommendations with suggested standardized values and a corrected UTM mapping table.

Output

• Tracking QA report listing missing, unexpected, and low-volume events
• UTM audit report with inconsistencies flagged and fix suggestions
• Corrected UTM mapping table with standardized naming conventions

Integrations

The Problem

Getting a CRO review of a landing page typically means hiring a consultant, waiting for their availability, and receiving recommendations days or weeks later. Internal teams can spot obvious issues but lack the systematic framework to evaluate above-the-fold content, CTA placement, trust signals, form complexity, and mobile considerations in a structured way.

How It Works

1. Agent navigates to the target URL and captures a screenshot of the page.
2. Analyzes the page visually for above-the-fold content, value proposition clarity, CTA visibility, trust signals, and mobile considerations.
3. Generates prioritized CRO recommendations with specific suggestions for each identified issue.

Output

• Screenshot of the analyzed page
• Structured CRO audit with prioritized recommendations
• Specific, actionable suggestions tied to each identified issue

Integrations

The Problem

GA4 has a powerful API but no official CLI. Pulling reports, checking real-time data, or listing available dimensions means either navigating the GA4 interface manually or writing custom API scripts from scratch. Automating recurring reports or piping GA4 data into other tools requires building and maintaining custom integrations every time.

How It Works

1. Authenticate using an API key, environment variable, or interactive login.
2. Run commands to query properties, pull reports, check real-time data, or explore available dimensions and metrics.
3. Output in JSON, table, or CSV format for direct use in other tools, scripts, or AI agent workflows.

Output

• GA4 reports in JSON, table, or CSV format
• Real-time traffic and event data
• Property and dimension/metric inventories

Integrations

The Problem

Ahrefs is one of the most data-rich SEO platforms available, but accessing that data programmatically means working directly with their API. There's no CLI for quickly pulling backlink data, checking domain ratings, or running keyword research from the command line. This makes it difficult to automate SEO audits, pipe Ahrefs data into reporting workflows, or integrate with AI agents.

How It Works

1. Authenticate using an API key or environment variable.
2. Run commands to query backlinks, keywords, domain ratings, or site explorer data for any domain.
3. Output in JSON, table, or CSV format for integration with reporting tools, spreadsheets, or agent workflows.

Output

• Backlink profiles with referring domains and anchor text
• Keyword rankings and search volume data
• Domain rating and site explorer reports

Integrations

The Problem

Meta's Ads Manager is built for manual, point-and-click campaign management. Pulling performance data across multiple campaigns, exporting audience definitions, or reviewing ad set configurations at scale means clicking through dozens of screens. There's no official CLI, so automating reporting, auditing campaign structures, or feeding Meta data into AI workflows requires custom API integrations.

How It Works

1. Authenticate using an API key, environment variable, or interactive login.
2. Run commands to list accounts, manage campaigns and ad sets, pull performance insights, or query audience definitions.
3. Output in JSON, table, or CSV format for use in reporting, cross-platform analysis, or agent workflows.

Output

• Campaign and ad set performance reports in JSON, table, or CSV
• Audience definitions and targeting configurations
• Account-level insights and spend data

Integrations

The Problem

Mailchimp's interface works for one-off campaign management, but pulling performance data across campaigns, auditing list health, or exporting member data for analysis requires manual exports or custom API work. There's no CLI, which means automating email reporting, syncing list data with other tools, or integrating Mailchimp into AI workflows requires building from scratch.

How It Works

1. Authenticate using an API key or environment variable.
2. Run commands to manage lists and members, review campaigns and templates, pull performance reports, or check automation status.
3. Output in JSON, table, or CSV format for integration with reporting dashboards, CRM syncs, or agent workflows.

Output

• Campaign performance reports with open rates, click rates, and revenue attribution
• List and member data exports
• Automation status and template inventories

Integrations

The Problem

Buffer handles social scheduling well through its UI, but pulling performance data across profiles, auditing posting frequency, or integrating social analytics into broader reporting requires manual work. There's no CLI, so any automation or cross-platform analysis involving Buffer data needs custom API scripting.

How It Works

1. Authenticate using an API key or environment variable.
2. Run commands to manage profiles, schedule or review posts, and pull analytics data.
3. Output in JSON, table, or CSV format for use in cross-channel reporting or agent workflows.

Output

• Post performance analytics across profiles
• Scheduled post inventories
• Profile configuration and status data

Integrations

The Problem

Google Tag Manager's web interface is the only way most teams interact with their tagging infrastructure. Auditing tag configurations across containers, reviewing trigger logic, or comparing workspace versions means clicking through nested menus one item at a time. There's no CLI, which makes it impossible to automate tag audits, version comparisons, or integrate GTM management into CI/CD or AI agent workflows.

How It Works

1. Authenticate using an API key, environment variable, or interactive login.
2. Run commands to list and manage accounts, containers, workspaces, tags, triggers, variables, versions, and environments.
3. Output in JSON, table, or CSV format for use in tag audits, documentation, or agent workflows.

Output

• Tag, trigger, and variable inventories across containers
• Workspace and version comparisons
• Container configuration exports for documentation or audit

Integrations