An asset estate that thinks ahead.
assetziq intelligence learns from every scan, movement and approval. It recognizes what it has never seen, anticipates what is about to go wrong, and recommends the next step with its reasoning.
- Understands
- Anticipates
- Recommends
- Explains its reasoning
- A person always decides
Understand, anticipate, recommend.
Each one learns from data assetziq already holds about your assets.
Understands your estate
Learns what your devices, records and documents look like, so it can recognize an unknown vendor or model, spot the same asset under two names and read a supplier document.
- Recognition
- Classification
- Matching
Anticipates what comes next
Learns the patterns behind failures, stock movements and spend, so it can warn of a likely failure, a spare running short or a project heading over budget.
- Risk scoring
- Forecasting
- Anomaly detection
Recommends and explains
Proposes the next step on any record and shows its confidence and the evidence it used. Ask it why in plain language, then approve, change or dismiss.
- Next best action
- Reasoning
- Conversation
A loop that gets sharper with every decision.
The model does not stand still. Each confirmation or correction from your team becomes part of what it knows.
Observe
Scans, movements, approvals and documents are recorded as they happen.
Learn
Patterns are learned from your history, not from generic averages.
Anticipate
Risks, shortages and anomalies are scored before they surface.
Recommend
A next step is proposed, with confidence and reasoning.
Confirm
A person accepts or corrects it, and the model improves.
Predictions and automation across the product.
Forecasts for stock, warehouses, the register and more, then ten places AI can take work off your teams.
Predictive analytics: see the problem weeks before it arrives
SFP-10G-LR will fall below its minimum level.
Issues are rising faster than receipts at two warehouses. The forecast shows when on-hand stock crosses the minimum, early enough to reorder.
- Issue history
- Open reservations
- Receipts due
Recommended actionRaise a purchase request for the shortfall.
Builds on Warehouse InventoryThe central warehouse will reach 95% of bin capacity.
Put-away is outpacing issues. The forecast shows when storage runs out, so stock can be moved before receipts have nowhere to go.
- Put-away rate
- Receipts due
- Bin capacity
Recommended actionPlan a transfer to a regional warehouse.
Builds on Warehouse InventoryThe register will drift further from operations.
New devices are appearing faster than exceptions are being closed. The forecast shows where the match rate lands if nothing changes.
- Open exceptions
- New devices per scan
- Resolution rate
Recommended actionPrioritize the 16 open exceptions by value.
Builds on Fixed Asset RegisterA UPS battery bank is heading for failure.
Its age, status history and three similar units replaced nearby push its risk upward. The forecast shows when it crosses the level where replacement is cheaper than failure.
- Asset age
- Status history
- Replacements nearby
Recommended actionRaise a replacement request now.
Builds on Asset ManagementThe core upgrade will finish over budget.
Incurred and committed spend are tracking above plan. The forecast projects the final cost while there is still time to act.
- Incurred and accrued
- Committed orders
- Completion date
Recommended actionReview scope with the project owner.
Builds on CWIP AssetsThe invoice hold queue is about to double.
Price variances from one supplier and late receipts are both rising. The forecast shows the queue before it becomes a payment delay.
- Price variances
- Receipt delays
- Supplier pattern
Recommended actionChase the outstanding receipts and review the tolerance.
Builds on Procure-to-PayTen places AI can take work off your teams
Vendor and model recognition
Identify the vendor, model and type of an unknown device from what a scan returns, across many vendors, so new equipment is recognized without a hand-written rule.
Works with Asset DiscoveryOne device, seen twice
Recognize when network discovery and IT discovery have found the same device under different names, and suggest merging them into one record.
Works with Asset DiscoverySmart matching in reconciliation
When a serial number is missing or mistyped, suggest the most likely register record for a discovered device, with a confidence score for a person to confirm.
Works with Fixed Asset RegisterAnomaly detection in discovery
Spot changes that do not fit the pattern, such as a device that changes serial number or vendor between scans, and raise them for review first.
Works with Asset DiscoveryFailure risk ranking
Rank assets by how likely they are to need attention, using their age, status history and past replacements, so teams act before a failure.
Works with Asset ManagementSpare demand forecasting
Forecast which spares will be needed and where, from issue history and stock movements, and suggest minimum and maximum levels.
Works with Warehouse InventoryInvoice exception triage
Read why an invoice is on hold, compare it with similar past cases and suggest the next step, so the queue clears faster.
Works with Procure-to-PayDocument reading
Pull the order number, quantities and amounts from supplier invoices and delivery documents, ready for a person to check and post.
Works with Procure-to-PayAutomatic classification
Suggest the asset type, category and item code for newly discovered or imported records, learned from how your team classified earlier ones.
Works with Asset ManagementCapitalization readiness
Highlight capital work that looks complete but has not been capitalized, and work that is heading over budget, before the period closes.
Works with CWIP AssetsIntelligence in every area.
More of what AI does across the product.
Discovery
- Topology hints from neighbour data
- Diagnosis of failed scans and credentials
- The best time and frequency for each scan
Assets and tagging
- Duplicate record detection
- A data-quality score for each record
- End-of-life and replacement outlook
Finance and the register
- Forecast of the register match rate
- Net book value outlook by asset class
- Likelihood that an asset no longer exists
- Useful-life and cost allocation suggestions
Warehouse and stock
- Stock-out prediction by item and warehouse
- Capacity forecast for bins and locations
- Slow-moving and excess stock alerts
- Transfer suggestions between warehouses
Procurement
- Price checks against contracts and price lists
- Supplier delivery and quality insights
- Forecast of the invoice hold queue
- Approval queue ordered by urgency
Across the product
- Plain-language reports on request
- Period summaries of exceptions
- A suggested next step on any record
Questions each team will be able to ask.
Operations
- Which sites have devices that changed since the last scan?
- What is in transit for more than a week?
- Which assets are most likely to fail next?
Finance
- Which register entries are still not explained?
- What capital work is overdue for capitalization?
- Summarize this month's reconciliation exceptions.
Procurement and supply
- Which invoices are on hold, and why?
- Which spares will run short next quarter?
- What is still to be received on approved orders?
AI is only as good as the data behind it.
Predictions and answers are worth trusting only when the record is. That is what the product does today.
See the platform- Discovery keeps the inventory current
- Reconciliation shows where records disagree
- Approvals mean every change was authorized
- History gives the model the full story of each asset
Principles built into every AI feature.
Human review
The assistant recommends. A person decides and approves.
Explainable
Every answer links to the records it was drawn from.
Your data stays yours
No training across customers by default.
Monitored
Model quality is tracked so drift is visible over time.
AI questions, answered.
How is this different from a chatbot?
A chatbot waits for a question. This raises insights on its own, scores how confident it is, shows the evidence, and learns from whether you accept or correct it.
What data will the AI use?
The data assetziq already holds for you: asset history, discovery results, stock movements, reconciliation outcomes and procurement records.
Will AI make changes on its own?
No. It recommends and prepares. Changes still go through the same approvals as any other change.
Will our data be used to train models for others?
Not by default. The design keeps each customer's data separate.
Do we need AI to get value from assetziq?
No. Discovery, the lifecycle, procurement and the register all work without it. AI builds on the record they create.
See what AI can do for your assets.
Tell us the questions you most want answered, and we will show you.