AI-native supply chain operating system
One operating layer for your entire supply chain.
Connect procurement, materials, warehouses, vendors, contracts, and field operations through one deterministic transactional core — with an AI layer that turns operational data into answers, predictions, and decisions.
Supply Chain Control Center
98.4%
124
18
$4.8M
3 suppliers show lead-time drift above tolerance for Q4 fiber demand.
- ADVA Optical
- Globex Electronics
- N.G. Electronics
- operational modules
- 6
- transactional core
- 1
- AI actions human-approved
- 100%
- audit trail
- Full
The problem
Your supply chain doesn’t have a visibility problem. It has a systems problem.
Every question worth asking — why is this late, what will we run short of, which supplier is slipping — needs data from four systems that don't share a schema, an owner, or a refresh cycle.
Today · 8 systems, no shared truth
ERP
Procurement
WMS
Spreadsheets
Vendor portals
Contracts
Email
Field apps
One deterministic transactional core
- Materials
- Warehouses
- Vendors
- Contracts
- Procurement
- Field ops
The platform
Six modules on one core — not six products stitched together.
Because they share a schema, a receipt posts inventory, closes the purchase order, and updates supplier performance in the same transaction. No nightly sync, no reconciliation job.
Procurement
Know what needs buying before the shortage becomes a problem — requisition to RFQ to purchase order to receipt, with multi-step approval built into the transaction, not bolted on after it.
Warehouses & Logistics
An append-only movement ledger, not a mutable stock counter — every receipt, issue, transfer and adjustment is a row you can audit, with live shipment tracking on top.
Materials
Categories, UOM, BOMs, kits, and serial-level traceability from goods-in to disposal.
Vendors
Onboarding that actually gates: compliance review, certifications, ratings, blacklisting.
Contracts
Template and clause library, versioned bodies, renewal windows, approval chains.
Field Operations
Jobs, assignment, live technician positions, check-in evidence, SLA performance.
AI intelligence
AI that understands how your operation actually works.
Not a chatbot bolted to a dashboard. A retrieval layer over your own transactional rows, contracts, and movement history — answering in the vocabulary your team already uses.
Which suppliers are likely to cause material shortages over the next 30 days?
3 suppliers require attention. Ranked by lead-time variance against open commitments for Q4 fiber build-out.
- ADVA Optical NetworkingHigh risk
Last 6 deliveries averaged 9.2 days late against a 21-day lead time.
- Globex ElectronicsMedium risk
Slippage concentrated in splice enclosures; other lines on time.
- N.G. ElectronicsMedium risk
Two partial receipts in the last quarter against contract terms.
Raise an RFQ for 400 patch cords against two alternate vendors to cover the ADVA shortfall.
AI recommends.
Your people decide.
The model can read everything the person asking is allowed to read, and propose anything. It cannot approve a purchase order, release stock, or change a contract. Those stay transactional, permissioned, and logged.
Cited, not asserted
Every answer names the records it came from — POs, movements, ratings, documents.
Permission-aware
Retrieval runs as the person asking. No answer includes a row their role cannot open.
Bounded to your data
It reads through audited tools, never a direct database connection or a training snapshot.
Architecture
Deterministic where it matters.
Intelligent where it helps.
Most 'AI for supply chain' products put a model in the write path and hope. Axle doesn't. The model is a layer above a core that already works without it — which is the only version an operations team can actually put into production.
- AI write paths to the database
- 0
- Tenant isolation — query + RLS
- 2×
- Mutations captured in audit log
- 100%
- Model provider, swappable
- Any
Transactional core
Postgres. Row-level tenant isolation under a restricted role. Append-only inventory ledger.
Rules & workflows
State machines per entity, multi-step approval chains, SLA timers, permission checks.
Tool layer
Every AI read and proposed write goes through audited, RBAC-checked tools. No direct DB access.
Intelligence layer
Retrieval, ranking, classification, forecasting, summarisation. Reads widely, writes nothing.
Human confirmation
Anything that mutates business data waits as a pending proposal until a permitted person accepts.
Execution & audit
The same validated code path a human action uses. Before/after state written to the audit log.
Real-time operations
The state of the operation, continuously.
Status is pushed over WebSocket as shipments transition and technicians move — not polled on a dashboard refresh, and not assembled by hand for a Monday report.
- Materials92%
- Warehouses97%
- Suppliers81%
- Shipments89%
18anomalies detected
4supplier risks
7delayed shipments
12inventory thresholds
Each signal links back to the rows that produced it — the movement, the purchase order, the tracking event — so an operator can act on it without leaving the platform.
Ask anything
Every operational question, in one place to ask it.
The same question that used to mean opening four systems and a spreadsheet resolves to a query — against rows for structured questions, against ingested documents for the rest.
- Deterministic patterns answer the common questions instantly, with no model call at all.
- Anything unrecognised falls back to classification, then runs a safe, parameterised query.
- Document questions retrieve passages and cite the file and version they came from.
Show all contracts expiring this quarter.
Which warehouse has the highest inventory variance?
Why is PO-2841 delayed?
Show suppliers with deteriorating delivery performance.
Which materials are at risk next month?
What are our termination terms with Prysmian?
Security & control
Enterprise control by design.
Not a compliance page — the actual mechanics. Here is everything that has to happen between the model suggesting something and a row changing in the database.
AI proposal
Raise RFQ · 400 patch cords
Human approval
Procurement manager
Transaction
RFQ-0413 created
Role-based access
A fixed permission catalog; roles compose it per organization. Checked at the route and again in the service.
Two-layer tenant isolation
Every query filters by organization, and Postgres row-level security enforces it again under a restricted role.
Permission-aware retrieval
AI retrieval runs with the asking user’s permissions — it cannot surface a record they could not open themselves.
Complete audit trail
Who, what, when, previous state, new state, and source — including every AI-proposed action and its decision.
Why Axle
The difference isn’t features. It’s where the truth lives.
Most stacks can produce any single number on this page. What they cannot do is produce all of them from the same source, at the same moment, with a trail back to the transaction.
Multiple disconnected systems
One operating layer
Reactive reporting, assembled by hand
Predictive intelligence, continuously
Generic AI with no operational context
AI grounded in your own rows and documents
Automation that acts on its own
Human-controlled execution, always
Stock as a mutable number
An append-only, auditable movement ledger
Answers that cannot be traced
Every answer cited back to a record
Your supply chain already generates the data.
Axle turns it into decisions.
See how one operating layer connects your entire operation — with a demo organization that is already full of materials, vendors, shipments, and open approvals.
Acquire Axle for your organization
Licensing, a private deployment on your own infrastructure, implementation, or the full platform — talk to the team directly.
support@niladritechsolutions.com