Partner

Partner problem

Your experts should not rebuild execution every engagement.

Today, delivery teams translate client reality into scripts, mappings, controls, workflows, and exception logic. That work should compound.

Before Nap

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Migration scripts written by hand

Per client. Per ERP. Per data model.

Workflows configured from scratch

Approval matrix, exception routing, controls — every engagement.

Production issues escalated to teams

Schema drift, missing fields, transient failures — escalation tickets, not encoded fixes.

Months per deployment. Knowledge that does not compound.

WITH NAP

One Blueprint. Every engagement.

Migration scripts encoded as Blueprint patterns

42 prior deployments → reused on engagement 43.

Workflows generated from operating model

Approval matrix, exceptions, controls — derived, not configured.

Production issues resolved by the control plane

Self-monitoring. Failures encoded as patterns. No escalation tickets.

Live in days. Knowledge that compounds across every client.

Time per development

2-3 Months

2 Days

Engineers required

Delivery team of 8-12

1 In Production

Knowledge Reuse

Start from scratch

42 Deployments encoded

Margin per engagement

Consumed by delivery

10-15x Better

The system doesn't start from configuration.

Inside your delivery.Not alongside it.

You advise. NAP executes. The client sees one delivery motion.

Your client

Enterprise

Advisory

Your firm

SI / Partner

Delegates

CFO Operating Blueprint

NAP execution layer

Delivers

Running

Production

Three commercial outcomes.

Every partner conversation comes down to the same three questions: Do I deliver faster? Do I make more money? Do I win more deals? NAP's answer to all three is the same execution system.

Speed

From months to weeks.

Deliver before competitors finish scoping.

Enforce

Execution becomes software.

Same revenue. Fewer delivery hours.

Win Rate

Faster time-to-value wins deals.

“Days, not months” changes the buying decision.

Live in production today

Not a concept. A running system.

Execution work previously performed by delivery teams is now encoded in NAP.

Migration

Customer data moves into your system

Without building one-off pipelines. 208 failure patterns already observed in production across 22 ERP environments — including multi-entity mapping, historical period alignment, and intercompany structures

Integration

Your product works with the full stack

ERP, procurement, billing, reporting — without relying on external implementation teams. 208 failure patterns pre-encoded across 22 ERP systems. Live in days, including mismatched schemas, field-level inconsistencies, and ERP-specific API behavior.

Workflow execution

Your logic runs in production

Not just inside your UI. 72 CFO workflows already encoded from real deployments — active from day one, covering AP matching logic, revenue schedules, approval chains, and close processes.

Production operations

Issues resolved within the system

Not escalated to services. Resolving failures like invoice mismatches, posting errors, and sync drift automatically — encoded so they never repeat.

Manufacturing client

Reading

AP workflow

3-way match + bill of lading gate

Entities

6 subsidiaries · individual sign-off gates

Edge cases

79 AP variations encoded

Mirrored

SaaS company

Revenue recognition

AP workflow

Subscription period + ASC 606 split

Entities

ARM schedules across 1,200 recognitions

Edge cases

Recognition date drift encoded

Mirrored

Marketplace platform

Vendor + contractor

AP workflow

Vendor/contractor 1099 split at invoice

Approval chains

Budget-linked, conditional per commodity

Edge cases

Platform fee netting before AP posting

Mirrored

How execution works

We mirror how the CFO stack actually runs. Then we execute it.

Every CFO environment is different. Workflows vary by industry. Data structures evolve over time. Edge cases define how systems actually behave — not how they were designed. NAP reads how your stack is configured — not how it was designed. It mirrors the workflows, data relationships, and system interactions that are actually running. Then it applies what it learned from every prior environment. Then executes against that reality.