Operationalize customer data quality across teams
Customer City turns data quality into a shared operating model: domain-owned Graph Spaces, cross-object rules, and an org-wide Confidence Score (1-100) for every customer record—grounded in the customer graph.
Start with one domain and one system of record. Expand when the score model is proven.
Data quality isn't an IT-only backlog item
Customer data breaks in the handoffs between teams: definitions drift, fields go stale, hierarchies diverge, and ownership becomes unclear. The result is inconsistent targeting, routing, forecasting, and customer engagement.
No shared definition of "good data"
Quality rules live in docs, spreadsheets, or tribal knowledge.
Quality signals lack context
Lists and dashboards don't show how records connect across objects.
Fixes don't scale across domains
Central teams become bottlenecks for domain-specific requirements.
A customer data quality platform built on a graph
We model Accounts, Contacts, Leads, Opportunities, and Activities as a connected customer graph—then compute a record-level Confidence Score using transparent, cross-object rules defined by the teams who use the data.
Graph-based customer context
Not just tables—relationships that reveal how issues propagate
Domain-owned stewardship
Each team defines what "good" means for their work
Cross-object rules + shared weighting
One Confidence Score per record, explainable by rule evidence
Detection to action workflows
Move from finding problems to fixing them at the source
Graph Spaces: domain-owned data stewardship
A Graph Space is a collaborative workspace where a team (RevOps, Sales Ops, Marketing Ops, CS Ops) selects the entities that matter, defines quality rules, and manages remediation—without forcing every request through a central backlog.
Select entities + relationships
Choose the nodes you care about (Account, Opp, Contact) and how they connect.
Define quality rules in context
Create single-object and cross-object rules (e.g., "Closed-won Opp requires Billing Country on Account").
Assign weighting (shared model)
Teams apply weight to rules so the score reflects organizational priorities.
Track Confidence Score + anomalies
Every record gets an explainable score with rule-level evidence.
Route remediation into real work
Create tasks/notes/events and coordinate fixes with the right owners.
One record. One score. Backed by transparent rules.
Confidence is computed from a shared rule model. Each rule has clear criteria, evidence, and weight—so teams can align on what "trusted" means and improve it intentionally.
Explainable scoring
See exactly which rules passed/failed and why.
Cross-team weighting
Multiple teams contribute to the same score model—without losing governance.
Cross-object rules
Measure real customer readiness across connected entities, not isolated fields.
Confidence is measurable and auditable—designed for enterprise review.
AI recommendations—grounded in your customer graph
Customer City uses AI to reduce the cost of stewardship: draft rules faster, explain low scores in plain language, and summarize anomalies with recommended next steps. Suggestions remain reviewable and traceable to underlying data.
Create rules from intent
Describe the requirement; AI proposes a rule structure and scope.
Explain "why the score is low"
Summarize failed rules, related entities, and likely root causes.
Triage anomalies
Group anomalies by impact, ownership, and downstream risk.
Turn findings into actions
Generate tasks/notes/events with suggested owners and due dates.
AI produces recommendations—not silent changes. You control what gets applied.
Verification, validation, and enrichment—built into remediation
Confidence scoring only matters if records are actually usable. Customer City validates contactability (email/phone) and standardizes addresses, then enriches missing fields using configurable sources. The result is a practical golden record: verified, enriched, and ready to operationalize.
Configure sources and controls per Graph Space, team, or field sensitivity.
Write-back to Salesforce, HubSpot, and more
Customer City doesn't just report data problems—it helps you fix them at the source. Approved updates sync back to your systems of record so downstream workflows operate on the same trusted data.
Keep systems clean. Reduce rework. Preserve auditability.
Purpose-built views for every stakeholder
Revenue Operations
- Weekly data reliability scorecard for GTM objects and relationships
- Coverage views: missing buying roles, orphaned opportunities, stalled handoffs
- Prioritized anomaly queue—what to fix first, and why
- Reduce disputes over "which number is right"
Business Systems / CRM Admin
- Controlled write-backs: approve, audit, rollback
- Merge/standardization workflows with blast-radius preview
- Clear ownership routing for anomalies
- Read-only by default—no black-box auto merge without policy thresholds
CIO / CTO / Data Leadership
- Governed layer that reduces GTM data risk without a multi-year MDM program
- Auditability and change control
- Clean, explainable context layer for AI initiatives
- Complements warehouse/MDM/CDP—focuses on GTM relationship truth
Enterprise governance, by default
Shared stewardship only works when access, change control, and accountability are built in.
Role-based access
Control who can view, edit rules, and approve changes per Graph Space
Approval workflows
Optional approval flows for score model changes by team
Audit-friendly history
Track rule edits, weighting changes, and score shifts over time
Configurable policies
Read-only or controlled write-back per integration
Prove value with one Graph Space
Run a focused pilot with a single domain and a defined dataset. Establish a baseline, deploy rules, and measure score movement as remediation happens.
What a pilot typically includes:
- One domain-owned Graph Space
- Initial rule set (single-object + cross-object)
- Confidence Score baseline + anomaly categorization
- Remediation workflow mapped to owners
- Before/after reporting on score and rule pass rate
You bring the domain expertise. We help you operationalize it.
Make customer data quality a shared operating model
Bring your GTM teams into the same confidence framework—so everyone can rely on the same customer reality.
Prefer a guided setup? We'll help you scope your first Graph Space.