Your marketing
agentic OS
The reporting layer for marketing teams that have outgrown their spreadsheets, their dashboards, and their patience.
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- One MCP
- One memory
- 1,400+ data sources
Marketing OS
/
Agent
Which Q3 campaigns wasted budget?
Thought for 38s
- Read Q3 spend from Google Ads, LinkedIn Ads and Meta Ads
- Joined HubSpot deals at stage 2+ with W-shaped attribution
- Ranked 8 campaigns by pipeline per dollar
- Checked headroom and drafted the move for approval
$61Kspent on Meta lookalikes for $38K of pipeline
Meta lookalikes0.6×Pause
Competitor search4.5×Add $30K
ABM retargeting8.4×SaturatedMove $30K over 6 weeks: +$40–80K expected net gain
Where should Q4 budget go?
4.6×▲ 0.3×
Pipeline per $ · Q3 vs Q2
Pipeline per $, by channel · Q3
6.5×
4.0×
1.5×
Recommended move
→
Move $30K from Meta lookalikes to competitor search
+$40–80Kexpected net gain
Experiments · 3 running · 2 concluded
+ New test
Meta lookalike holdout
No liftpipeline vs holdout
Demo request rate: 4 fields vs 7
+0.9ptnot significant yet
LinkedIn lead form: 3 questions vs 5
−8%SQL rate with 3
Brand search: bids −15%
−3%brand clicks
Competitor search: comparison page
–too early
Governance · 37 of 41 rules passing
Auto-block on
Naming: “Nov webinar promo”
Rename to LI_WEBINAR_NOV_US
Brand safety: 12 placements
LinkedIn Audience Network block list
Approval: $30K budget move
Meta lookalikes → competitor search
Write blocked: Google Ads bid cap
The watcher is read-only, so it drafted it
Agent activity · 46 queries todayLive
09:12Priya via Claude Q4 pipeline gapread
08:00Codex October pacingread
06:00Anomaly watcher Google bid capblocked
Workspace · marketing
Approval for changes
Any budget or bid change an agent drafts
Query log
Every agent query, kept 400 days
Slack alerts
#marketing-ops · anomalies and approvals
12 connected · 1,400+ in the catalog
Schema-mapped
Data pipelines
ETLELTReverseSync
12 live pipelines · unified into one model
Paid search Channel
Google Ads search · 3 campaigns
Metrics · Q3 2026
- Pipeline per $6.5×
- Spend$190K
- SQLs64
Definitions & docs
- SQLHubSpot stage
- AttributionW-shaped
- Naming convention
GADS_*
Every agent reads this page
Connected sources
+8
Trusted by marketing teams at
All customer stories →
Read the ASUS storyASUS · GLOBAL
Unified marketing across six regions into one analytics hub.
~90 hours/week saved · real-time reporting
Read ASUS’s story 
Read the illycaffe storyILLY · COFFEE & CPG
Automated marketing data across 140+ countries.
80–100 engineering hrs/week saved
Read illy’s story 
Read the AdRoll storyADROLL · MARTECH
Cross-channel attribution development time cut by 82%.
11 platforms integrated in 6 months
Read AdRoll’s story 
Read the SoftwareOne storySOFTWAREONE · IT & CLOUD
Centralized 80% of the MarTech stack out-of-the-box.
3× ROI during implementation
Read SoftwareOne’s story 
Read the University of San Francisco storyUNIVERSITY OF SAN FRANCISCO · EDUCATION
Marketing data unified across six schools.
Weekly reports became daily
Read USF’s story 
Read the AdCellerant storyADCELLERANT · MARTECH
New-source integration costs cut by 70%.
Was 6 months + $120K per platform
Read AdCellerant’s story 
Read the Booyah Advertising storyBOOYAH · AGENCY
Restored trust with 99.9% data accuracy.
Eliminated manual validation work
Read Booyah’s story 
Read the Eicoff storyEICOFF · AGENCY
Onboarded enterprise clients 15× faster.
No-code, cross-channel harmonization
Read Eicoff’s story 
Read the CV (Christian Vision) storyCV · NONPROFIT
Ran 400+ ad accounts with a two-person team.
8 data sources · no added headcount
Read CV’s story 
Read the Yodel Mobile storyYODEL MOBILE · APP MARKETING
Cut client reporting time by 80%, hours to minutes.
Unified pipeline automation
Read Yodel’s story 
Read the Chacka Marketing storyCHACKA MARKETING · AGENCY
Manual reporting time reduced by 90%.
One unified marketing data ecosystem
Read Chacka’s story Show me what Improvado does for…
Role
Industry
Ask anything. Watch it get visualized
Am I going to land the quarterly revenue number, where is per-channel new-customer efficiency decaying, and where does…
Live read · Reading your eCommerce signals as one connected story — here's what matters.
Pulled 12 sources for CMOs / VPs Marketing in eCommerce. Top signal: Meta Prospect Marginal ROAS 0.9x. Also tracking Projected revenue $9.41M. Built live on the right — no SQL, no tab-hopping.
Quarter Landing ForecastProjected EOQ vs $9.8M plan, day 64 of 91
Projected revenue
$9.41M
96% of plan
Gap to target
-$390K
recoverable
It doesn't stop at the dashboard
One prompt pulls every connected source, makes it analysis-ready, builds the live board, then pushes the next move back into your platforms.
Run it yourselfGet a board-ready performance overview
Meta Prospect Marginal ROAS
0.9x
-1.4x in 8 weeks
vs prior
The last $10K/week into Meta prospecting now returns 0.9x, below breakeven, the textbook saturated funnel behind the $38 to $52 CAC jump.
Payback by month
How it works
From a thousand tabs
to one command center
Marketing data lives in 1,400+ scattered platforms. Improvado pulls it in, maps it to one shared model, and serves it back as dashboards and AI answers. Instantly.
01 Connect
Plug into every marketing source you run
Data sources across paid, organic, CRM, analytics, retail media, and ecommerce.
1,400+
data sources
02 Unify
Map every channel to one shared model
Channels, campaigns, and metrics in a single structure, no SQL, no spreadsheet glue.
46K+
metrics unified
03 Decide
Act with confidence, in seconds
Prompt the agent to shift budget, audit creative, run attribution, or chase a pacing drop.
Seconds
to live dashboards
Enterprise-grade security
Built for non-negotiable enterprise compliance requirements
Why it matters
Security compliance is where most AI marketing deployments slow down. It's where we differentiate.
Role-based access
Per user & workspace
Full audit trail
Every query logged
SSO & SAML
Enterprise identity
GDPR & CCPA
EU data residency
Customer story
Improvado powers our data views every single day. It's a pulse check on business performance that enables our clients to make smarter, strategic decisions on budgeting, sales forecasting, and assess marketing's impact on their businesses.
$500K/yr
Savings in staff costs
15×
Faster client onboarding
<1 wk
Time to value
See it on your own data
Let's talk about your measurement problem
Bring your own data, we'll show your pipeline, attribution, and the agent, live, in twenty minutes.
Book a working session
A solutions engineer maps your stack and shows your own pipeline, no slideware.
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20-minute demo
On your own data
Live in two weeks
For engineers & AI agentsThe connection, the scopes, and the exact call behind this panel
- Instantly query any warehouse table via natural language
- Auto-generate governed SQL with lineage
- Drop agent tools into Claude / Cursor / Codex
- Full MCP + OpenAPI surface out of the box
- Zero-copy access to every marketing source
# one connection, declared once
mcp.connect("improvado")
# read-only, role-scoped by default
improvado.query(
metric="roas",
channels=["google_ads","meta_ads"],
window="q3", group_by="campaign",
)
# scope: read:spend · auth: oauth1,400+
Data sources
90 hrs
Saved per week
80%
Less overspend
75%
Faster launches