Your marketing data,
one question away
Ask Claude about all your marketing data - campaign performance, pipeline attribution, budget pacing - and get governed, source-cited answers in the app your team already uses.
 | $XXX.XK | spend | $X.XXM | pipeline · 8.8x |
 | $XX.XK | spend | $XXXK | pipeline · 7.6x |
 | $XX.XK | spend | $XXXK | pipeline · 5.2x |
$ claude mcp add --transport http improvado \
https://mcp.improvado.io/v1
✓ connected · OAuth workspace token · 214ms handshake{ "mcpServers": { "improvado": {
"url": "https://mcp.improvado.io/v1",
"scopes": ["read:spend", "read:crm", "write:memory"],
"memory": "shared", "audit": "per-query" } } }
From question to answer in three steps
Connect once, ask in plain English, and Claude remembers what your team taught it - no setup project and no query language to learn.
Connect
Connect Improvado to Claude once - your admin sets it up, nothing for you to install.
Set up once at onboarding - nothing for your team to install or maintain.
Ask anything
Ask about spend, pipeline, CAC, attribution - Claude queries live, normalized data across every connected source.
led with $X.XXM in pipeline at 8.8x return · brought $XXXK at 7.6x.412 ms median answer · every number source-cited · saved to shared memory
It remembers
Your data model, naming conventions, and team context persist in your team's shared memory across every session and every teammate.
- Your namingset in onboarding
- Q3 targetsfrom the plan review
- Spend anomalieslearned last week
Saved to your team's shared memory.
See the technical setupThe exact commands, queries & scheduling - for your engineers and AI agents
# 1 - Show setup command
claude mcp add --transport http improvado \
https://mcp.improvado.io/v1
# 2 - See the underlying query
> which paid channels drove pipeline?
google_ads $1.31M · 8.8x linkedin $XXXK · 7.6x
# 3 - See what’s stored
memory: saved to shared memory
naming + context recalled · every sessionShow me what Claude does for reporting
Real workflows marketing teams run every day - each on your live, connected data.
| Search | $XXX.XK | 8.8x |
| ABM | $XX.XK | 7.6x |
| Prospecting | $XX.XK | 5.2x |
| Blended · weekly | $XXXK | 7.7x |
Google Ads · Search carries the highest efficiency at 8.8x ROAS - Claude recommends shifting weekly budget there first.
| Paid search | $X.XXM | XX% |
| Paid social | $XXXK | XX% |
| Organic + direct | $XXXK | XX% |
| Total modeled · 30d | $X.XXM | XXX% |
Paid social is compounding fastest at +XX% week over week - Claude flags the budget headroom before quarter close.
| Freshness | XX% | +2 pts |
| Completeness | XX% | +6 pts |
| Consistency | XX% | +3 pts |
| Overall · 8 weeks | 94/100 | +4 pts |
Completeness improved the most this cycle at +6 pts - the remaining gap traces to three legacy connectors.
| Naming conventions | XXX% | pass |
| Connector health | XX% | 2 alerts |
| Access audit log | XXX% | complete |
| Sources governed | 1,002 | all ws |
Two connector alerts remain open - Claude already drafted the fix ticket, keeping connector health at XX%.
Marketing data superpowers, inside every conversation
Everything Claude needs to be genuinely useful on marketing questions - clean data, real context, safe access.
1,000+ sources, one connection
Google Ads, Meta, HubSpot, Salesforce, TikTok and the rest of your stack, in one connection.
Persistent memory
Claude remembers your data model, naming and team context between sessions.
Secure by design
No raw credentials, scoped role-based access, and a full audit trail on every query.
Works where you work
Claude on web, desktop, and mobile - the same single connection everywhere.
One clean format
Every channel in one clean format - no messy per-platform differences to manage.
Real-time and historical
Live metrics and years of history through the same interface, always current.
Ask in plain English
Ask the way you'd ask a teammate. Claude reads your connected data and answers.
For engineers & AI agentsThe schema, field mappings, and the query behind each answer
# 1 - The blended-ROAS answer, as a query
improvado.query(
metric="roas",
channels=["google_ads","meta_ads","tiktok_ads"],
window="7d",
)
# 2 - Unified schema (excerpt)
# spend:number
# revenue:number
# roas:number
# channel:enum
# date:date
# updated_at:timestamp
# 3 - Access
# read-only
# workspace-scoped
# every query in the audit log
# zero raw credentialsMinutes to your first answer, not a data project
Connect once and Claude reads your live marketing numbers the same day - no integration backlog, no analyst queue, no keys pasted into a chat.
Ask on day one. The data-engineering project that used to sit between you and an answer is already done.
Every ad platform, your CRM, and your warehouse arrive as one clean set of numbers - not 500 exports to reconcile.
Governed access and one clean, consistent data source mean Claude's answer matches what your BI team would hand you.
See the full technical comparisonHow this compares to wiring up each platform yourself
| Dimension | Raw platform APIs | With Improvado MCP |
|---|---|---|
| Authentication | Separate OAuth per platform | One OAuth connection |
| Schema | Drifts per platform update | Unified schema, versioned |
| Memory | None between sessions | Persistent knowledge graph |
| Credentials | Keys pasted into prompts | Zero credentials in context |
| Rate limits | Per-API, hit blind | Governed by one layer |
| Time to first queryThe gap that compounds | Weeks of integration workweeks | Minutes, one MCP configminutes |
| Dependency | N direct integrations you maintain and control | One governed endpoint - a single vendor dependency, by design |
Based on Improvado onboarding data across 500+ connected ad, CRM, and analytics platforms
One governed layer between any agent and your entire stack
Switch AI tools whenever you want. Your data, your access rules, and everything your team has taught the system stay exactly where they are - nothing to reconnect, nothing relearned.
Switch agents. Keep your data. Keep your memory
Claude reaches your whole marketing stack through Improvado - same data, same memory, your credentials never touch the chat.
For engineers & AI agentsThe MCP contract, endpoints, and how memory persists across agents
# 1 - One connection, declared once
{
"mcpServers": {
"improvado": {
"url": "https://mcp.improvado.io/v1",
"auth": "oauth"
}
}
}
# 2 - Scopes - role-based, read-only by default
# read:spend
# read:crm
# read:attribution
# write:memory
# 3 - Shared memory - survives an agent switch
# persisted in the governed knowledge graph
# versioned
# every agent reads the same nodesAgent access your security team will actually approve
Improvado was built for enterprise marketing data from day one - agents get answers, never credentials.
SOC 2 Type II · HIPAA · GDPR/CCPA · SSO/SAML · every agent query logged read-only, scoped to the human's SSO role, zero raw credentials.
For engineers & AI agentsThe live audit trail and role-based scopes
| Workspace role | Data access |
|---|---|
| Analyst | Read: campaigns, spend |
| Manager | Read: campaigns, spend, budgets |
| Admin | Read/write: full workspace |
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.
Common questions
Straight answers on setup, security, and how Claude fits alongside other agents.
Does Claude work with Improvado today?
Yes - Claude supports MCP servers natively. Add the Improvado MCP endpoint to your config, authenticate, and every connected source becomes queryable.
Source: MCP config referenceWhich Claude models does this support?
Is my data safe?
What is MCP?
How long does setup take?
Does it work alongside other agents (Codex, custom agents)?
Let's talk about your measurement problem
Bring your own data - we'll show your pipeline, attribution, and the agent, live, in twenty minutes.