Connect. Match. Clean. Contextualize.

How Tidyr Works

Your data is scattered, siloed, messy, and missing context. Tidyr fixes each problem in sequence—then hands Claude a unified intelligence layer via the Model Context Protocol. No data team required.

Step 01Solves: Inaccessible

Connect everything

Your data lives in 5–15 different tools. Someone asks a question and you open HubSpot, then Xero, then Stripe, then Intercom—piecing together an answer manually. There’s no front door.

Tidyr connects to your tools with pre-built integrations. Over 100 connectors covering CRM, finance, billing, and support. Setup takes less than 5 minutes per source. No engineering, no CSV uploads, no API keys to manage.

CRM
HubSpot, Salesforce, Pipedrive
Finance
Xero, QuickBooks, NetSuite
Billing
Stripe, Chargebee, Recurly
Support
Intercom, Zendesk, Freshdesk

Plus Shopify, Monday, Notion, Google Sheets, and 100+ more

1,000+
supported data sources
<5 min
to connect your first source
Zero code
no engineers needed
Step 02Solves: Siloed + Messy

Match & unify across systems

“Acme Corp” in HubSpot. “ACME Corporation” in Xero. “acme-corp-inc” in Stripe. Same company, three names. Your systems are islands—contacts don’t link to invoices, subscriptions don’t connect to support tickets.

Tidyr resolves entities using three complementary AI methods, then links records across every system into a single unified graph. You stay in control—every match goes through a review queue where you approve, reject, or override.

Method 1

Deterministic

Exact rules on shared identifiers. Email addresses, phone numbers, domain names, tax IDs. Fast and certain.

HubSpot: sarah@acme.com
Xero: sarah@acme.com
Match: exact email
Method 2

Fuzzy

String similarity for near-matches. Catches typos, abbreviations, and formatting differences.

HubSpot: “Acme Corp”
Xero: “ACME Corporation”
Match: 92% similarity
Method 3

AI Embedding

Semantic understanding via vector embeddings. Catches what rules and string comparison miss.

CRM: “IBM”
Billing: “International Business Machines”
Match: semantic similarity

The result: one unified record

CRM
Sarah Chen · sarah@acme.com · VP Engineering
linked
Finance
INV-2024-0847 · $4,200 · Acme Corporation
linked
Billing
sub_1N4x7k · $350/mo · Enterprise Plan
linked
Support
#18294 · “API rate limit question” · Open
4 systems · 1 customer · complete picture
Step 03Solves: Incomplete

Clean, enrich, fill the gaps

Unified data is only useful if it’s accurate. Missing fields, stale records, inconsistent formats—these erode trust in every report and every AI answer.

Tidyr continuously monitors data quality, flags anomalies, and enriches records to fill gaps. Standardize formats, detect missing fields, and layer in external data so your intelligence layer is always complete.

Data Quality

Automated monitoring catches issues before they reach your dashboards. Missing emails, invalid formats, stale records, duplicate entries.

⚠ 23 contacts missing email
⚠ 8 companies missing industry
✓ Phone format standardized

Enrichment

Fill in missing company data, industry classifications, employee counts, and domains. Turn sparse records into complete profiles.

Acme Corp → +industry: SaaS
Acme Corp → +employees: 150
Acme Corp → +domain: acme.com

Standardization

Normalize formats across every system. Consistent naming, addresses, phone numbers, currencies. No more “US” vs “United States” vs “USA.”

“US” → United States
“+1 (555) 123-4567” → +15551234567
“$4,200.00 USD” → 4200.00

Continuous quality scoring

Every record gets a quality score. Tidyr tracks completeness, freshness, consistency, and accuracy over time. You see exactly where your data stands and what needs attention—before it becomes a problem in your reports.

Step 04Solves: No context

Add your business context

This is the step most platforms skip. Clean, connected data is necessary but not sufficient. Claude still doesn’t know what “churn” means to YOUR business, or how YOU segment customers, or which metrics YOUR board cares about.

Tidyr lets you layer your business logic on top of unified data. Define your metrics, your segments, your operational rules. This is what transforms generic AI answers into intelligence that sounds like it came from someone who actually works at your company.

Metric Definitions

Churn
No active subscription AND no invoice paid in 90 days
ARR
Monthly recurring × 12, excluding one-time fees
At-risk
Overdue invoice + open support ticket + declining usage

Customer Segments

Enterprise
ARR > $20K, dedicated CSM, annual contract
Mid-Market
ARR $5K–$20K, quarterly business review
SMB
ARR < $5K, self-serve, monthly billing

When Claude answers “What’s our churn rate?” it uses YOUR definition—not a generic one. That’s the difference between a chatbot and a business intelligence layer.

The result

Claude with real intelligence

Connected, mapped, cleaned, and contextualized. Tidyr serves your unified data to Claude via the Model Context Protocol. Claude doesn’t just get data—it gets your business understanding.

✶ Connected via Model Context Protocol (MCP)

Which enterprise customers are at risk of churning this quarter?

3 enterprise accounts match your at-risk criteria (overdue invoice + open ticket + declining usage): Acme Corp ($42K ARR, 2 overdue invoices, 3 open tickets), Globex ($28K ARR, 1 overdue invoice, declining logins), and Initech ($22K ARR, support escalation + 45-day payment delay). Total ARR at risk: $92K.

Reconcile our HubSpot pipeline with actual Xero revenue for Q1.

HubSpot shows $312K in closed-won deals for Q1. Xero shows $298K in recognized revenue. The $14K gap comes from 2 deals: one invoiced but payment deferred to Q2 ($9K, Nexus Ltd), and one with a billing address mismatch that delayed invoicing ($5K, Atlas Inc). Both are resolved in your unified records.

What does our customer health look like by segment?

Enterprise (12 accounts, $284K ARR): 9 healthy, 3 at-risk. Mid-Market (34 accounts, $156K ARR): 28 healthy, 4 at-risk, 2 churned in March. SMB (201 accounts, $89K ARR): 178 healthy, 15 at-risk, 8 churned. Overall health score: 84%. Biggest concern: Enterprise at-risk accounts represent 17% of your total ARR.

These aren’t hypothetical. These are the kinds of answers Claude gives when it has unified data with your business context. Cross-system, reconciled, using your definitions.

Ready to get started?

Connect your first source in under 5 minutes. No code, no consultants, no multi-month project.

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