01 · Forecast
Predictive Analytics
Forecast failures, dwell, congestion, demand, utilization and SLA risk. Models retrained continuously on your live operational ledger.
INNFINI's Decision Engine & ML layer fuses every event, rule and learned pattern in real time — then recommends or triggers the right action the moment it matters. Move from manual monitoring to intelligent, automated control.
Six stages. One continuous loop. Every event your operation produces gets ingested, understood, scored, decided on — and acted on — without leaving the platform.
RFID, IoT sensors, GPS, cameras, ERP, WMS, CRM, EAM, mobile apps, user activity. Ingested via API, MQTT, Kafka, DB & webhook.
Raw data normalized into meaningful events: asset entered zone, vehicle dwelling, temp spike, SLA approaching.
Business users define thresholds, escalation paths and SOPs — no code. Events evaluated against every active rule in <5ms.
Patterns, anomalies and predictions: failure forecasting, demand, dwell, congestion, incident probability, SLA risk.
The engine proposes the next-best-action — context-aware of location, asset, role, priority and history — with citations.
Closed-loop: create WO, alert, escalate, update ERP/WMS, log compliance, push to mobile. Outcome feeds back to the ledger.
Deterministic business logic where it matters. Machine learning where humans can't keep up. AI recommendations where context wins.
Forecast failures, dwell, congestion, demand, utilization and SLA risk. Models retrained continuously on your live operational ledger.
Unusual movements, unexpected sensor readings, off-pattern behavior, compliance gaps — surfaced the instant the signal goes off-baseline.
Every event ranked by severity, business impact, SLA pressure, location, asset criticality and role. The right thing first, every time.
Plain-language next-best-action with the why behind it. Citations to the underlying event, rule, model and historical outcome.
Trigger tasks, alerts, approvals, work orders and system updates. Closed-loop tracking from detection to resolution to learned outcome.
Every outcome — good, bad, ignored — feeds back. Decision quality improves on its own; rule suggestions surface as the system learns.
Reduce the time teams spend identifying, investigating and triaging operational issues.
Right context, right recommendation, right priority — surfaced before the operator asks.
Early-warning signs caught before they become major incidents, breaches or downtime.
Repetitive decision-making automated. Manual follow-up replaced by closed-loop execution.
From reactive operations to predictive, intelligent, automated control. The Decision Engine & ML layer empowers teams to detect issues earlier, respond faster, and continuously improve operational performance across every asset, person, workflow and enterprise system.
Not every decision deserves the same treatment. The engine classifies each one and applies the autonomy the class allows — automation is earned, not assumed.
| Class | Example | Autonomy | Reversible |
|---|---|---|---|
| Deterministic | Temperature above threshold → open a work order | Fully automatic | Yes |
| Scored | Vibration signature → predict bearing failure, pre-order the part | Automatic above a confidence floor | Yes |
| Judgement | Crowd density rising → divert flow and re-staff a gate | Recommended, operator approves | Partially |
| Consequential | Evacuate a zone, isolate a feeder, dispatch armed response | Always human, often co-signed | No |
Rules are authored in a no-code editor by operations staff, versioned like code, and tested against historical data before they ever touch production.
Conditions are built from graph objects and event streams in plain terms — no query language, no engineering ticket.
Every rule is replayed against months of historical events to show exactly how often it would have fired, and on what.
New rules run in shadow mode, producing recommendations nobody acts on, until their precision is proven.
Promotion is a reviewed change with an author, a diff and a one-click rollback. Rules are never edited live.
Twenty-two models in production means twenty-two things that can silently degrade. The engine treats model health as an operational surface with its own alarms.
| Concern | How it is handled |
|---|---|
| Drift detection | Input distributions and output confidence are monitored continuously. A drifting model raises an incident like any other asset fault. |
| Retraining cadence | Scheduled retraining on the live operational ledger, plus event-driven retraining when drift crosses a threshold. |
| Champion / challenger | A candidate model runs alongside production on the same traffic. Promotion requires it to win on precision and on false-positive cost. |
| Feature lineage | Every feature traces to the graph objects and sensors it derives from, so a bad sensor is diagnosable from a bad prediction. |
| Fallback behaviour | If a model is unavailable or unconfident, the engine falls back to deterministic rules rather than guessing or going silent. |
| Bias and fairness review | Models touching people — staffing, prioritisation, access — are reviewed for disparate impact before promotion and on a fixed cycle after. |
A missed fire and a false fire alarm are not equal errors. Thresholds are set from the operational cost of each error type, not from a generic accuracy target.
| Domain | False positive costs | False negative costs | Tuned toward |
|---|---|---|---|
| Life safety | An unnecessary evacuation | An undetected fire | Recall |
| Predictive maintenance | A part replaced early | An unplanned outage | Recall |
| Security alerting | Operator fatigue, ignored alarms | A missed intrusion | Balanced |
| Traffic diversion | Congestion moved onto the wrong street | A queue left to grow | Precision |
| Service dispatch | A wasted crew visit | A breached SLA | Precision |
The most important behaviour of an automated system is what it does when it is uncertain, degraded or disconnected.
| Condition | Behaviour |
|---|---|
| Confidence below floor | The recommendation is presented as uncertain, with the missing evidence named. It is never auto-executed. |
| Signals disagree | The conflict is surfaced explicitly rather than averaged away. An operator sees both readings and decides. |
| Upstream feed lost | Affected rules are marked degraded, not silently skipped. Operators are told which decisions they must now make manually. |
| Link to core lost | The edge runtime continues on cached rules with a reduced action set, buffers events, and reconciles on reconnect. |
| Model unavailable | Deterministic rules take over. Autonomy drops one level automatically until the model is healthy again. |
| Operator overrides repeatedly | A pattern of overrides is treated as signal. The rule is flagged for review rather than left to keep firing. |
A 60-minute architecture review with our solutions team. We map your sensors, your systems, and the rules where automation moves the needle.