Systems of record remain essential.
ERP, accounting and ecommerce systems are where operational truth lives. Grabb is not trying to replace them.
The Grabb operating layer
Your systems know what happened.Grabb determines what matters next.
Grabb connects operational data, creates a consistent commercial model of your business, detects meaningful changes, prioritizes feasible actions and helps execute them through the systems you already use.
Built above your existing systems · No ERP replacement
Illustrative example · John's MarketFollowing one signal through the stack
Illustrative example · John's Market
01 — Connect
Start with operational truth.
The useful signals already exist inside the systems where your business operates. Grabb starts by connecting those systems rather than creating another place your team has to maintain.
QuickBooks
John's Market Inc.
Shopify
Order #9921 · Johns Mkt
ERP
CUST-1042 · PO #4421
Customer PO
JOHNS MARKET / PO-4421
Canonical transaction
Invoice #84723
John's Market · SKU-HBC-12oz
$3,242
- Connectors
- Incremental sync
- Source mapping
- Transaction ingestion
Don't ask people to re-enter the business into another system.
Illustrative example · John's Market
02 — Canonicalize
Different systems.One commercial language.
Connecting systems is not enough. Different platforms represent customers, products, SKUs, orders, inventory, suppliers and reps in different ways. Grabb creates a consistent commercial representation across sources.
Grabb Commerce Layer
The Commerce Layer normalizes business entities across systems so customer, product and order behavior can be understood consistently.
Customer entity
QuickBooks
John's Market Inc.
Shopify
Johns Mkt
ERP
CUST-1042
Customer PO
JOHNS MARKET
Canonical
Customer · John's Market
Product entity
ERP SKU
HBC-12oz-CASE
Customer SKU
JM-COFFEE-12
Ecommerce SKU
house-blend-12oz
Pack / UOM
12 × 12oz case
Canonical
Product · SKU-HBC-12oz
House Blend Coffee
Connecting data is plumbing. Understanding that the records represent the same business entity is the harder problem.
Illustrative example · John's Market
03 — Understand
Turn transactionsinto behavior.
Grabb Signal Engine
Raw transactions tell you what happened. They do not tell you what changed enough to matter.
Invoice #84723 = $3,242 · John's Market
Cadence deviation · reorder overdue
John's Market · Normal buying behavior
Wk 1
Wk 5
Wk 9
Wk 13
Now
- Expected reorder
- Every 30 days
- Actual behavior
- Day 45 · no order
- Reorder cadence
- SKU velocity
- Customer momentum
- Product penetration
- Demand change
- Seasonality
- Fill rate
- Margin movement
- Account deviation
- Fulfillment gap
The goal isn't more metrics.It's knowing what changed enough to matter.
Illustrative example · John's Market
04 — Decide
Not every signaldeserves action.
Detection creates candidates. The harder problem is deciding what matters most, what is feasible, what has enough confidence and what should be ignored.
Grabb Action Model
The Action Model ranks possible actions against business context, expected impact, urgency and feasibility.
Candidate actions detected
- Call John's Market · reorder overdueACT
- Introduce SKU-MFB · Atlas FoodsGROW
- Fill rate slipping · Brand AFIX
- Summit Supply · volume decliningACT
- SKU velocity spike · Region EastWATCH
Priorities this week
- 1Call John's Market · reorder recovery
- 2Fill rate exception · Brand A
- 3Summit Supply · declining volume
- 4Introduce SKU-MFB · Atlas Foods
- 5Review demand spike · Region East
- 6Validate PO · Brand C
- 7Replenish SKU-204 · warehouse
Rules decide. AI explains.
Business rules, permissions and operational constraints should not depend on an LLM improvising an answer.
Deterministic checks
Deterministic where the business requires certainty.
- Account ownershipRep assigned · valid
- Reorder threshold15 days overdue · act
- Inventory availableSKU in stock
- Pricing constraintsWithin contract
- Approval requiredNot required
Language layer
Generative where it adds leverage.
Explain priority #1
John's Market typically reorders every 30 days. They're now at day 45 with no order—$3,242 recurring revenue at risk. Recommend calling today to confirm reorder timing.
Signals
Models detect meaningful business changes.
Decision logic
Deterministic logic ranks and validates what can actually be done.
Language
LLMs help explain, summarize and let users interact naturally.
The LLM is an interface to the intelligence. It is not the intelligence itself.
Candidate action→Policy→Approved / Modified / Blocked
Illustrative example
Complexity in. Priorities out.
100,000
Transactions
2,400
Signals
178
Meaningful changes
34
Possible actions
7
Priorities
John's Market · reorder signal → 1 of 7 priorities
Illustrative example · John's Market
05 — Act
Intelligence shouldn't stopat another dashboard.
The final destination is not a report. Once an action is approved, Grabb connects back into the operating workflow.
Priority #1 approved
Call John's Market · reorder recovery
CRM
CRM
Task created · assigned to rep
✉
Outreach draft prepared
ERP
ERP
Account flagged · reorder watch
Create taskPrepare outreachCreate / update CRM activityProcess customer order · Order AutomationPrepare purchase orderUpdate ERP workflowTrigger approvalInitiate connected workflow · Architecture supports
The destination is a completed action.
Illustrative example · John's Market
06 — Learn
The recommendation shouldn't be the end.
After an action happens, the system can observe the outcome—creating the foundation for improving future ranking, thresholds and recommendations.
Outcome feedback creates the foundation for improving future ranking, thresholds and recommendations.
Account reordered
John's Market placed order #9012 · 3 days later
Cadence restored · reorder model updated
- Account reordered
- Opportunity converted
- No response
- Demand normalized
- Fill-rate issue resolved
- Recommended SKU sold
- Action ignored
07 — Why we're building it this way
The operating stack is changing.
Humans cannot interpret everything anymore.
As customers, products and transactions multiply, attention becomes the constraint. Teams need exceptions and priorities—not another report.
AI should not improvise business rules.
Some decisions require deterministic logic, permissions and operational certainty. Generative AI should amplify understanding, not replace guardrails.
The future is an action layer above the operating stack.
A layer that understands the business, detects meaningful change, determines what matters, coordinates execution and observes the outcome.
That's the system we believegrowing product businesses will run on.
Grabb is building that layer.
Record the business.Then operate from it.
Systems of record
ERP · Accounting · CRM · Ecommerce
They store
- Customers
- Transactions
- Inventory
- Orders
- Activities
Grabb — System of action
Prioritized execution
Grabb determines
- What changed
- What matters
- What is at risk
- What opportunity exists
- What should happen next
- What can be automated
Keep the systems that run the business. Add the layer that helps the business decide.
Where Grabb applies this
Sales
Account behavior → priorities → growth actions
Demand
Velocity + seasonality → forecast → replenishment signal
Fulfillment
Ordered vs fulfilled → service risk → intervention
Order Automation
Incoming order → validation → ERP-ready transaction
Built to sit inside real operating environments.
Grabb is designed for production use alongside the systems that already run your business.
- Role-based permissions
- Human approval workflows
- Deterministic policy guardrails
- Clear system boundaries
- Audit trail for actions
- Controlled execution paths
Technical questions
Is Grabb just an LLM over our ERP data?
No. Grabb combines a Commerce Layer that normalizes entities across systems, a Signal Engine that detects meaningful behavioral changes, an Action Model that ranks what deserves attention, a Policy Layer with deterministic guardrails, and a language layer that helps users interact naturally. The LLM explains and interfaces—it does not replace the underlying intelligence.
Does Grabb replace our ERP?
No. Grabb connects to ERP, accounting, ecommerce and order systems you already use. Your ERP remains the system of record.
Why does Grabb canonicalize data?
Because different source systems represent customers, products and orders differently. Without a consistent commercial model, you cannot compare behavior across channels or detect meaningful change reliably.
How does Grabb decide what deserves attention?
Grabb learns expected behavior from transaction history, detects deviations through the Signal Engine, and ranks candidates through the Action Model using business context, impact, urgency and feasibility—without requiring teams to review every transaction.
Is every decision made by AI?
No. Deterministic logic and policy guardrails handle permissions, thresholds, feasibility and approvals. Generative AI helps explain signals and summarize context—it does not improvise business rules.
Can Grabb execute actions automatically?
Grabb supports connected workflows including order processing, task creation and CRM updates—often with human approval for high-impact actions. The architecture supports expanding automated execution as policies allow.
Does Grabb learn from outcomes?
Grabb is designed to observe action outcomes—whether an account reordered, an issue resolved or a recommendation was ignored—creating feedback for improving future ranking and thresholds over time.
What systems can Grabb connect to?
Grabb connects to QuickBooks, Shopify, Sage, Xero, Zoho, Salesforce, Amazon and your ERP—with incremental sync from accounting, ecommerce, orders and CRM sources. See the integrations page for current connector details.
From operating data to action
Your business is already producing the signals.
Grabb gives those signals structure, context and a path to action.