Connect ERP, CRM, data warehouses, document repositories, shared inboxes, APIs, and legacy systems into a governed AI delivery layer without replacing the systems your teams already use.
Every connection includes permission boundaries, field-level access controls, sync frequency policies, data lineage logging, and audit trails — so AI workflows produce reliable, governed outputs backed by authoritative source data.
Enterprise AI requires access to live data, operational context, and authoritative records. Our managed connectivity layer makes that possible—without disrupting existing systems.
SAP, Oracle, Microsoft Dynamics, NetSuite — connect financial, supply chain, and operational records
Salesforce, HubSpot, Microsoft Dynamics CRM — connect accounts, opportunities, and interaction history
Snowflake, BigQuery, Redshift, Databricks — connect analytics and reporting data lakes
SharePoint, Google Drive, Box, Dropbox — connect policies, procedures, contracts, and forms
Outlook, Gmail, support@, invoices@ — connect group mailbox content and attachments
Invoices, contracts, claims, purchase orders, compliance forms — extract and match structured data
REST, GraphQL, SOAP, webhooks — connect custom enterprise services and third-party platforms
Mainframes, AS/400, custom on-premises apps — connect through managed adapters and extraction
ServiceNow, Zendesk, Jira Service Management — connect tickets, SLAs, and resolution data
Approval queues, routing systems, case management — connect workflow state and history
Every connection is configured with permission boundaries, sync frequency policies, and audit trails before AI workflows can access it.
Before any data feeds an AI workflow, governance boundaries are established. Each connection is reviewed and configured so that AI only accesses what it should — and every access is logged, attributable, and auditable.
Define which specific fields AI can access — not entire tables or databases. Restrict PII, financial data, and sensitive fields by role and use case.
Exclude specific records, date ranges, or categories from AI access. Define exclusion rules that are enforced before any query or workflow execution.
Role-based and use-case-based access controls. Different AI workflows see different subsets of data — even from the same source system.
Every data element that feeds an AI output is traceable back to its source system, table, and field — with timestamped access records for audit.
Define how often each data source refreshes. Real-time, hourly, daily, or event-triggered sync — governed by data freshness requirements and system load.
Immutable logs of every data access, query, and AI workflow execution. Who accessed what, when, and for which workflow — retained for compliance review.
Every AI output cites its data sources. Users can verify which systems, records, and fields contributed to a response — essential for trust and auditability.
Automated monitoring for anomalies: data sync failures, permission violations, stale data, unexpected schema changes. Alerts routed to data owners and operations teams.
A concrete example of governed data connectivity in production — from invoice arrival through ERP reconciliation, exception handling, human review, and downstream system update with full audit trail.
Invoice PDF or email arrives in shared inbox or document queue. Data extracted: vendor, amounts, line items, PO reference, dates.
Extracted data matched against purchase orders and ERP records. Line-item validation, pricing verification, budget code assignment.
Mismatches, missing POs, pricing discrepancies, or duplicate invoices flagged for human review. Supporting evidence attached to exception ticket.
Designated reviewer approves, adjusts, or rejects. Downstream ERP record updated. Audit log created with full lineage from source to decision.
This is one concrete example of governed data connectivity in production. The same pattern — connect, extract, match, flag exceptions, route to human review, update downstream, log for audit — applies across claims processing, contract review, compliance reporting, and dozens of other enterprise workflows.
Schedule an AI use-case review to evaluate which systems to connect, what governance boundaries to apply, and which workflow delivers the highest measurable impact for your organization.
Use this checklist to prepare for a productive AI use-case review. You don't need perfect answers — the evaluation process helps you find them.
Identify the system of record for each data domain — ERP for financials, CRM for customer data, HRIS for employee records.
Name the business owner and technical owner for each source system. They'll be needed for access review and permission configuration.
Document which roles should access which data. Define what's restricted, what's available, and who approves access changes.
Assess data freshness — does the AI need real-time, hourly, daily, or batch-synced data? Is current data quality sufficient for reliable AI outputs?
Identify sensitive fields — PII, financial data, proprietary information — that must be blocked from AI access, regardless of use case.
Define the specific AI workflow — reconcile invoices, classify support tickets, retrieve policy answers, route approval requests.
Define success metrics — processing time reduction, error rate decrease, throughput increase, cost per transaction, user satisfaction.
Identify the human reviewers for low-confidence outputs, flagged exceptions, and edge cases. Define escalation paths and response SLAs.
Ready to evaluate your data connectivity readiness?
Bring your checklist answers to a use-case review. We'll help you fill gaps and identify the highest-value starting point.
Request AI Use-Case Review