Enterprise Data Operations

Your Data Exceptions
Don't Resolve Themselves.
We Do.

VeriHubTree connects your existing systems, validates data against your rules, and deploys AI agents that investigate, explain, and resolve exceptions — autonomously.

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SOC 2 Architecture
Portable Architecture
Full Audit Lineage
veribubtree / exception-resolver — live
Connected
EXCEPTION SOURCE STATUS
EMP-2847: Payroll mismatch
HR ↔ GL — $14,280 delta
ERP ✓ Resolved
ACCT-0041: Invalid mapping
Chart of accounts — Rule VHT-009
GL ⟳ Resolving
STU-9134: Program conflict
SIS ↔ Special Programs
SIS ⊙ Investigating
VEND-0288: Duplicate record
CRM ↔ ERP — ref conflict
CRM ✓ Resolved
FIN-1109: Budget overage
Program 11 — $89K variance
BUD ⊙ Investigating
Agent activity — last 60s
› Reconciliation agent: cross-referenced EMP-2847 against GL entries
› RAG retrieved: Policy §4.2.1 — payroll period alignment
› Resolution applied: HR record corrected, audit trail written

Trusted by data engineering teams at

The Resolution Gap

Observability tools detect.
Governance tools catalog.
Neither resolves.

Your team spends 40–60% of its time triaging exceptions that existing tools cannot fix. VeriHubTree is the platform that closes the loop — from detection to verified resolution, with a complete audit trail.

Observability

Detects

Your current tools surface exceptions. That is where their work ends.

Pipeline anomaly detected
Data quality rule triggered
Root cause — not investigated
Source identified — no action
Resolution — manual, if at all
Governance

Catalogs

Governance platforms document problems. Tickets accumulate. Teams investigate manually.

Exception logged in catalog
Ticket created and assigned
Engineer investigates manually
Average time-to-resolution: 72 hrs
Audit trail — incomplete
VeriHubTree

Resolves

The complete cycle — detect through verified resolution — with AI agents grounded in your organizational knowledge.

Exception detected via deterministic rules
AI agent investigates root cause
RAG retrieves applicable policy
Plain-language explanation issued
Resolved — full audit trail written

"The problem was never detection. We had alerts. We had dashboards. What we didn't have was resolution — and that is what VeriHubTree delivered."

Chief Data Officer, Enterprise Financial Services

The Resolution Architecture

Five layers. One complete cycle.
From raw data to verified resolution.

VeriHubTree's architecture is deterministic where it must be, intelligent where it adds value, and portable to any infrastructure you already own.

01

Connect

The MCP connector layer links to your existing systems — ERP, CRM, data warehouse, flat-file imports, API feeds — without replacement or disruption.

MCP Connector Layer

02

Normalize

Ingested data is mapped to a unified canonical model. Source values are preserved alongside normalized values — every transformation is auditable.

Unified Data Model

03

Validate

Deterministic rules — versioned, cited, owner-assigned — run against the normalized model. Exceptions are created when rules fire, not buried in AI prompts.

Rules Engine

04

Investigate

Specialized AI agents — grounded in your organizational knowledge via RAG — investigate root cause, retrieve applicable policy, and generate plain-language explanations.

AI Agents + RAG

05

Resolve

Automated remediation applies the fix. Human judgment governs Class 3 decisions. Every action — agent and human — is captured in an immutable audit trail.

Workflow + Audit

1

Class 1 — Deterministic

Platform identifies and resolves automatically. Example: invalid account code.

2

Class 2 — Analytical

AI investigates, prioritizes, and recommends. Example: expenditure pattern deviation.

3

Class 3 — Judgment

Humans retain authority. AI assists, prepares evidence, and routes for approval.

The cost of the resolution gap

40 –60%

of data engineering team bandwidth consumed by manual exception triage each month

Industry analysis — CDO benchmarking surveys, 2024

72 hrs

average time-to-resolution for a data exception handled through manual triage workflows

DataOps benchmarking, Gartner Data & Analytics Summit 2024

$2.3M

average annual cost of poor data quality per enterprise — before downstream AI project failures

IBM Institute for Business Value — Cost of Data Quality Report

Operational domains

Resolution at scale, across every data domain.

Financial Operations

Financial Reconciliation

GL entries that don't reconcile with HR payroll records, budget overages with inconsistent program mappings, chart-of-accounts violations — VeriHubTree connects your ERP, HR system, and budget tools, applies your exact reconciliation rules, and deploys agents to investigate discrepancies before they reach your CFO or auditors.

Class 1 exceptions (invalid codes) are resolved automatically. Class 3 decisions (accounting judgment calls) are routed with full evidence packages to the right human authority.

Exception — EMP-2847

Type Payroll-GL mismatch
Source systems HR ↔ ERP General Ledger
Variance $14,280
Rule triggered VHT-FIN-012 (payroll period)
Agent finding Period cutoff mismatch — 2 days
Status Resolved
Auto-resolved in 3.2 hrs — audit trail written

Supply Chain Operations

Supply Chain Data Operations

Inventory records that diverge between warehouse management, procurement, and ERP systems create cascading exceptions that ripple into planning, finance, and compliance. VeriHubTree connects each system, detects discrepancies at the record level, and agents investigate whether the source is a sync delay, a duplicate entry, or a mapping error — then resolves it.

Your procurement and logistics teams stop spending mornings on data reconciliation spreadsheets and spend them on decisions instead.

Exception — INV-3341

Type Inventory count mismatch
Source systems WMS ↔ ERP ↔ Procurement
SKU SKU-7821-B — 340 unit delta
Root cause Partial receipt — sync lag
Resolution ERP record corrected
Resolved in 1.8 hrs — no human intervention

Customer Data Operations

Customer Data Integration

Customer records that differ between CRM, billing, support, and marketing platforms cause downstream failures in reporting, regulatory compliance, and AI model training. VeriHubTree normalizes customer entity records across systems, validates them against your data contract rules, and resolves conflicts — before they corrupt your customer 360 or trigger a compliance finding.

Deduplication, field-level conflict resolution, and consent flag reconciliation are handled by agents grounded in your data governance policies.

Exception — CUST-8820

Type Customer record conflict
Source systems CRM ↔ Billing ↔ Support
Conflict Email + address mismatch
Compliance flag GDPR consent record inconsistent
Status Routed for approval
Class 3 — evidence package prepared

Architecture

Built to run anywhere
you need it.

VeriHubTree is an independent, portable platform. Every component — source code, data schema, rules, agents, RAG configuration, and deployment manifests — is yours. No proprietary lock-in. No vendor dependency for your operational data infrastructure.

Portable source architecture

Complete GitHub repository. Every component is migratable to independent or customer-hosted infrastructure. No proprietary runtime required.

Environment-variable configuration

No hard-coded credentials, API keys, rules, or tenant configuration. Secrets managed via your existing infrastructure — AWS Secrets Manager, Azure Key Vault, or equivalent.

Tenant-isolated multi-tenancy

Full tenant boundary isolation across auth, data, RAG indexes, agent context, and audit logs. Designed for multi-tenant SaaS, private cloud, or single-tenant enterprise deployment.

MCP-standardized integrations

The connector layer follows Model Context Protocol conventions. Each integration is a documented, replaceable module — not a monolithic proprietary adapter baked into the core platform.

Supported deployment targets

Public SaaS
Private Cloud
Customer Cloud
On-Premises
AWS / Azure / GCP
Kubernetes

Platform architecture

/core — platform foundation
/connectors — MCP integration layer
/rules — deterministic rules engine
/rag — organizational knowledge
/agents — AI agent framework
/audit — immutable event log
/modules — industry-specific
/modules/financial-ops
/modules/supply-chain
/modules/customer-data
/modules/[your-domain] →
SOC 2 Architecture
Full Audit Lineage
RBAC + ABAC Ready

POC Program

Ready to see VeriHubTree
resolve your exceptions?

Our Proof of Concept engagement connects to a representative slice of your data environment — typically two to three source systems — and demonstrates the full resolution cycle with your actual data exceptions, your rules, and your organizational knowledge.

4–6 week structured POC — from connector setup through a live exception resolution demonstration with your data

Portable deliverable — you receive the source repository, schema, and configuration at POC completion, regardless of next steps

No production disruption — the platform is a read layer on top of your existing systems, not a replacement

Lighthouse pricing — early enterprise partners receive preferred rates and influence over the product roadmap

Request Your POC

Tell us your work email and we will reach out within one business day to schedule an initial technical conversation.

We respond within one business day. No marketing lists. No SDR cadence.

What happens next

1

Technical discovery call — understand your data environment and exception patterns

2

POC scoping — connector selection, rule set, and demonstration scenario design

3

Live resolution demonstration — exceptions detected, investigated, and resolved in your environment