Looking for a Windsor.ai alternative for AI coding agents?

    Windsor.ai is a marketing data pipeline (ad platforms → warehouse → attribution). Cogny is one MCP server your AI coding agent calls directly — no warehouse, no Zapier glue, one OAuth flow per source. $9/mo Solo.

    ❯ install the skillcompare plans →
    ❯

    cogny --windsor

    WHAT

    Windsor.ai is a marketing data integration platform with 325+ connectors that move data into your warehouse, with pre-built attribution models on top.

    Data PipelineOAuth into 325+ sources (ad platforms, CRM, e-commerce). Daily sync into your warehouse.
    Attribution LayerFirst-touch, last-touch, linear, Markov, and algorithmic attribution models computed in their stack.
    Warehouse RequiredYou land the data into BigQuery / Snowflake / Postgres, then query it via BI or an MCP server.
    ❯

    cogny --compare

    TABLE

    Cogny vs. Windsor.ai — feature-by-feature

    FeatureCognyWindsor.ai
    Core surfaceMCP server — called from your AI coding agentETL pipeline → warehouse → BI / MCP
    Warehouse required✓No✗Yes (BigQuery / Snowflake / Postgres)
    Entry price✓$9/mo Solo✗from ~$23/mo (Lite tier)
    Source connectors✓~15 (marketing focus)✓325+ (broad coverage)
    Attribution models✓Ad-hoc — agent writes the join in-session✓Pre-built: first-touch, last-touch, linear, Markov, algorithmic
    Setup time✓Minutes✗Hours to days
    Best for solo founder / small team✓Yes (Solo tier built for it)✗Possible, but warehouse overhead
    Best for enterprise data team✓Cloud tier; warehouse is optional✓Yes — designed for the warehouse layer
    AI-agent-native✓Yes — built for it✗Adopted via MCP server (BI tools first)
    Pre-built marketing skills✓Yes (SEO audit, CTR analysis, GEO tracking, etc.)✗No — build your own queries
    Maintenance✓Zero — Cogny manages the stack✗Warehouse + ETL + BI ownership
    Free starting tier✓15 calls/mo on signup✗Free trial, then paid
    ❯

    cogny --use-cases

    DEEP

    What it actually takes to answer common marketing-attribution questions, with each tool

    Attribute Stripe revenue to ad campaigns
    With Windsor.ai
    1. 1.Set up data warehouse (BigQuery, Snowflake, or Postgres)
    2. 2.Connect Windsor.ai to Stripe + Google Ads + Meta Ads
    3. 3.Wait for first daily sync (typically 4–24 hrs)
    4. 4.Model the join in dbt or SQL (utm → session → customer → subscription)
    5. 5.Pick an attribution model (last-touch / Markov / algorithmic)
    6. 6.Build a BI dashboard or query layer to actually ask questions
    7. 7.Connect an MCP/BI client so an agent can query it
    Time: 1–2 weeks
    Skills: SQL, dbt, warehouse admin, attribution modeling
    With Cogny
    1. ✓Connect Stripe + Google Ads + Meta Ads via OAuth
    2. ✓Ask the agent: "which campaigns drove last month's Stripe revenue?"
    3. ✓Agent writes the join + answers in the chat
    Time: Same day
    Skills: None — natural language
    Cross-channel ROAS analysis
    With Windsor.ai
    1. 1.Land Google Ads, Meta Ads, LinkedIn Ads, GA4 in warehouse
    2. 2.Reconcile naming differences across platforms
    3. 3.Model conversions back to spend
    4. 4.Build dashboards or Looker Studio reports
    5. 5.Schedule weekly refresh + alerting
    Time: 1–2 weeks setup, ongoing maintenance
    Skills: Warehouse SQL, attribution, BI tooling
    With Cogny
    1. ✓Connect ad accounts (OAuth, ~2 min each)
    2. ✓Ask: "show ROAS by channel for the last 30 days"
    3. ✓Agent pulls live numbers from the MCP and answers
    Time: Same hour
    Skills: None
    Newsletter signup → blog post attribution (GA4 + Mailchimp)
    With Windsor.ai
    1. 1.Connect both sources to Windsor.ai
    2. 2.Land to warehouse
    3. 3.Model the join (GA4 page_view → newsletter_signup → mailchimp contact)
    4. 4.Build query layer
    Time: 3–5 days
    Skills: Warehouse SQL, dbt
    With Cogny
    1. ✓Connect GA4 + Mailchimp via OAuth
    2. ✓Ask: "which blog posts drove the most newsletter signups?"
    3. ✓Agent reasons over both data sources in-session
    Time: 15 minutes
    Skills: None
    Ad-hoc question: "why did CPL spike last week?"
    With Windsor.ai
    1. 1.Open BI tool
    2. 2.Build new query against warehouse
    3. 3.Pivot by channel, campaign, ad group
    4. 4.Cross-reference with Google Ads change history (separate report)
    Time: 15–60 minutes per question
    Skills: SQL fluency + BI tool
    With Cogny
    1. ✓Ask the agent in plain English
    2. ✓Agent queries live MCP data, finds the cause
    3. ✓Follow-up questions stay in the chat
    Time: Under 5 minutes
    Skills: None
    Set up the system for a solo founder with no warehouse
    With Windsor.ai
    1. 1.Spin up BigQuery or Postgres
    2. 2.Subscribe to Windsor.ai (typically $23/mo+ entry plans)
    3. 3.OAuth every source
    4. 4.Sit through initial sync
    5. 5.Decide on a BI tool (Looker Studio / Metabase) or learn SQL
    Time: 2–5 days
    Skills: GCP/AWS basics, SQL, BI tooling
    With Cogny
    1. ✓Install the Cogny skill in your coding agent
    2. ✓CLI signs you up using your git email
    3. ✓First analysis runs in the same chat
    Time: 5 minutes
    Skills: None — just install
    ❯

    cogny --which

    FIT
    Choose Windsor.ai if...
    ✓You already operate BigQuery / Snowflake / Postgres for analytics
    ✓You need formal multi-touch attribution models (Markov, Shapley) with audit trails
    ✓Your analytics team writes SQL — agents are not your primary consumption layer
    ✓You need to land 100+ marketing sources (Windsor has 325+ connectors)
    ✓You have data-engineering capacity to own the warehouse + ETL layer
    Choose Cogny if...
    ✓You're a solo founder or small team without a data warehouse
    ✓Your AI coding agent (Claude Code, Codex, Cursor) is your primary analytics surface
    ✓You want answers in natural language, not in SQL
    ✓You'd rather install one MCP server than maintain a warehouse + BI stack
    ✓Your spend is $20K–$500K/mo and you'd rather pay $9 than $200+ in warehouse + ETL overhead
    ✓You value pre-built marketing skills (SEO audit, CTR fixes, attribution questions) over a generic ETL pipeline

    ❯ get started

    Install the skill. Your agent does the rest.