Aelix Echo · Predictive Analytics
Forecast demand, failures, and risk
before the window closes.
Predictive maintenance for manufacturers, demand forecasting for retailers, claim-severity scoring for insurers. MAPE under 5% on operational time series, with drift monitoring on every model.
Agentic AI · Predictive agents · Design-partner preview
Forecasts you can defend. Anomalies the agent already worked.
A prediction without a defensible explanation is a guess on a dashboard. Aelix Agents extend predictive analytics with the gathering, the reasoning, and the drafting. They explain a forecast in plain language with the drivers cited, investigate an anomaly with the relevant signals already pulled, and stress-test a scenario against canonical shocks. Decision support, not autonomous action.
Anomaly Investigation Agent.
When a metric trips a threshold, the agent gathers the correlated signals, the recent changes, and the historical baseline. Drafts a what-changed-and-why narrative with every claim cited to a specific data point.
Forecast Drafting Agent.
Pairs a transparent statistical baseline with qualitative sensing. Drafts the forecast and explains the drivers in plain language. A planner reviews, edits, and applies. Every applied forecast is recorded.
Alert Triage Agent.
Sits on the alert stream, ranks alerts by likely impact, suppresses known-benign patterns within a tenant-set allowlist, and writes a verdict with rationale. High-severity or unknown alerts always escalate.
Scenario Stress Agent.
Runs scenario stress tests against canonical shock vectors (historical or constructed) and surfaces implied impact with the assumptions cited. Defensible early-warning analytics. Not a guarantee.
The trust architecture.
Deterministic engines decide, the agent explains.
Forecasts come from the statistical or ML engine that's audited and version-locked. The agent narrates the drivers, never overrides the math.
Citation or strip.
Every claim in every brief cites a specific data point, model output, or assumption. Uncited output is discarded before display.
Confidence calibrated, not inflated.
Confidence comes from the underlying model's measured calibration on held-out data, not from the language model. The agent never invents a number.
Read-only by default, action is gated.
The agent surfaces, ranks, and drafts. Actions on the data (suppress an alert, push a forecast to a downstream system, schedule a remediation) are dedicated, gated tools the platform intercepts.
What ships first
Phase 1 ships the Anomaly Investigation and Alert Triage agents in assistive mode. Auto-suppress runs only within a tenant-set allowlist of known-benign patterns. Forecast and stress agents ship as decision support. We never publish a measured-accuracy claim that the underlying model has not validated on a held-out cohort.
The thesis
A forecast is useful only if a decision-maker can act on it before the window closes, and trust the math that produced it.
Most analytics programs ship dashboards. We ship decisions: models wired into the workflow that needs them, with confidence intervals an operator can read, drift signals the data team can see, and a clear answer to the question every executive eventually asks, why did the model say that?
What we model
Forecasting that respects your domain, not generic time-series.
Demand, throughput, claims, churn, energy load, asset failure. We pick the model class that fits the shape of the data and the lead-time the decision actually needs, not the one that won the last Kaggle competition.
Predictive maintenance and asset health, wired to the work.
IoT telemetry, vibration, temperature, and process data, fused into asset-level risk scores that surface in the maintenance system your team already opens every morning.
Risk scoring and decision intelligence with a paper trail.
Credit, fraud, claim severity, operational risk. Every score comes with the features that drove it, the population it was calibrated on, and the audit trail the regulator will ask for.
Explainability that survives a conversation, not a notebook cell.
Counterfactuals, feature attributions, and confidence bands rendered in the interface, so the operator sees why the model said what it said before they're asked to act on it.
MLOps that doesn't quietly stop being right.
Monitoring, drift detection, scheduled retraining, and rollback. The model goes into production with the same care the codebase gets, and the same telemetry the SRE team already trusts.
A glimpse of the work
Demand · next 30 days
Forecast surface · demand model
Outcomes
< 5%
MAPE on operational demand forecasts
14d
Lead time on asset-failure predictions
Months → wks
Model deployment turnaround
Measured across Aelix Echo pilot deployments, 2025-2026
Where the models run
Five domains, the decision we predict for, and the score we ship at.
How we work
A short discovery, an honest baseline, and a long, observed operate.
Discover
We sit with the decision-makers, find the window they have to land inside, and trace the data that would let a model honor it.
Model
Baseline, candidate, and explainer models, evaluated on the metric that matches the business cost of being wrong, not the one that's easy to plot.
Operationalize
The model goes into the workflow that needs it, with confidence intervals an operator can read and a rollback path the SRE team trusts.
Operate
Monitoring, drift detection, scheduled retraining, and the iteration that keeps the forecast worth trusting six quarters from now.
A note from the practice
A model that doesn't say how confident it is, isn't ready. A model that no one can question, isn't trusted. We ship the answer, the confidence, and the reason, because eventually someone is going to ask all three.
The Aelix Echo analytics practice
When the decision needs to be made before the data is in, we're here.
45-minute working session · One real decision, scoped live