Reasoning-native forecasting

See the shape of
what happens next.

AetherLogic pairs causal reasoning with production-grade forecasting, deployed inside your own cloud environment — so your team acts on signal, not noise, without your data ever leaving your boundary.

Get started See how it works
34%Avg. error reduction
2.1M+Predictions / month
99.95%Uptime
FORECAST.STREAM — LIVE
Demand · Q3 Northeast
94% conf.▲ 12%
Churn · Enterprise tier
91% conf.▼ 3%
Anomaly · Payments API
flagged 00:42:11
Inventory · West DCs
88% conf.▲ 6%
Built for Logistics & supply chain Financial services Healthcare & life sciences Retail & e-commerce Energy & utilities
The gap

Most forecasting tools stop at correlation

They tell you what's about to happen. They almost never tell you why — which means every forecast is a guess your team has to defend on faith.

The old way

Black-box scores with no rationale
Retrained quarterly, stale in between
Data exported to a third-party cloud
Anomalies discovered after the fact

The AetherLogic way

Every prediction ships with plain-language reasoning
Continuous retraining as new data lands
Runs inside your own environment, always
Anomalies flagged in real time, before impact
Process

From raw data to a decision you can act on

Four stages, running continuously — not a one-time model handoff.

01 · CONNECT

Point us at your data

Warehouse, event stream, or data lake — connected read-only, on your terms.

02 · REASON

Trace the causes

Causal models separate what's actually driving a metric from what's just correlated.

03 · FORECAST

Generate the prediction

Ensemble models produce a forward-looking number with a confidence band, not a guess.

04 · ACT

Ship it to your team

Alerts, dashboards, or a direct API call — wherever the decision actually gets made.

Why AetherLogic

Built for teams that have to
justify every prediction

Precision and transparency aren't a tradeoff here — every forecast comes with the reasoning behind it.

Causal reasoning engine

Multi-step inference that traces cause, not just correlation, so every forecast comes with a "why" your stakeholders can act on.

Forecasting at scale

Time-series, anomaly detection, and scenario simulation running continuously across millions of series in parallel.

Explainable by default

Every prediction ships with a confidence interval and a plain-language rationale — nothing is a black box.

Private, isolated deployment

Runs inside your own cloud environment, in your own isolated network, so customer data never leaves your account boundary.

Estimate your impact

What could better forecasts be worth?

A rough estimate based on the average error reduction teams see in their first quarter.

Estimated monthly savings
$10,200
Estimate = decisions × avg. cost × current miss rate × 34% typical error reduction. For directional planning only — request a pilot for numbers specific to your data.
Model library

Predictive models, tuned for the real world

Specialized architectures fine-tuned for forecasting and decision support, not generic benchmarks.

Forecasting

Temporal Fusion

Multi-horizon forecasting with attention over exogenous variables.

Causal

Causal Impact

Measures the effect of interventions with Bayesian structural time-series.

Reasoning

Neural Reasoner

Graph-based reasoning for complex relational predictions.

Monitoring

Anomaly Oracle

Real-time detection of outliers and regime changes in streaming data.

Ensemble

Ensemble Stack

Blend of tree-based, deep, and probabilistic models for robust output.

Spatial

Geospatial Predictor

Location-aware forecasting for logistics, retail, and climate.

Case study

How LogiChain cut forecast error by a third in one quarter

LogiChain's demand planning team was reforecasting weekly with a black-box model nobody fully trusted. AetherLogic replaced it with a causal model that explains every shift in demand — and retrains continuously instead of quarterly.

"We can now justify every prediction to stakeholders. That's the part that actually changed how we work."
Priya Sharma — VP Data, LogiChain
34%Forecast error reduction
6 weeksFrom pilot to production
94%Avg. prediction confidence
Trust

A security posture built for enterprise procurement

Designed so your security team's questionnaire is a formality, not a blocker.

Encryption everywhere

AES-256 at rest and TLS 1.3 in transit across every service boundary.

Isolated by design

A dedicated, isolated network per customer, with least-privilege access scoped to a single tenant.

Full audit trail

Every inference is logged into an immutable, versioned archive you can hand to an auditor.

Data residency control

Data and models stay inside your own account and chosen region — never copied out.

Compliance roadmap: SOC 2 Type II audit in progress · GDPR-ready data handling · HIPAA-eligible architecture available on request.
Pricing

Straightforward plans, no seat-counting

Priced on forecast volume, not headcount. All plans start with a paid pilot.

Starter

$2,400 / month

For a single team validating one forecasting use case.

  • Up to 10,000 forecasts / month
  • 2 predictive models
  • Email support
  • Standard security posture
Request a pilot
Most common

Growth

$7,800 / month

For teams running forecasting across several business units.

  • Up to 150,000 forecasts / month
  • Full model library
  • Dedicated Slack channel
  • Isolated deployment
  • Quarterly model reviews
Request a pilot

Enterprise

Custom

For regulated, multi-region, or high-volume deployments.

  • Unlimited forecast volume
  • Custom model development
  • Dedicated solutions engineer
  • Custom compliance review
Talk to us
The team

Researchers and engineers who ship

A small team obsessed with trustworthy, explainable AI in production.

Portrait of Dr. Elena Voss

Dr. Musa Yakubu

CHIEF AI SCIENTIST

Ex-DeepMind, PhD in probabilistic machine learning.

Portrait of Marcus Chen

Martha Emeka

HEAD OF ENGINEERING

Distributed systems & MLOps, previously at Stripe.

Portrait of Dr. Sarah Kwon

Dr. Sarah Kiwi

RESEARCH LEAD

Causal inference & time-series, PhD from MIT.

Portrait of James Okafor

James Okonkwo

PRODUCT & STRATEGY

AI product leader with three prior exits.

Team photography via Unsplash — placeholder portraits, not yet AetherLogic's actual team. Swap in real photos before publishing.
Feedback

What early teams say

Real feedback from teams running AetherLogic in production.

★★★★★
The explainability features changed how we work with regulators. We can now justify every prediction, not just the outcome.
Dauglas
CTO, FinCore Analytics
★★★★★
Deployment inside our own environment was painless, and the ensemble model outperformed our previous system on precision.
Dr. Amira
Director of AI, HealthPredict
★★★★★
The pilot paid for itself in the first month. Rolling it out to two more business units next quarter.
Tomas
Head of Ops, BrightGrid Energy
Questions

Frequently asked

The questions security and data teams usually ask first.

Most forecasting tools stop at a number. AetherLogic's causal layer traces what's actually driving that number, so your team gets a rationale it can act on and defend, not just a score.

Inside your own cloud environment, in an isolated network scoped to your account. Nothing is copied to a shared or third-party environment.

Six weeks on average, from data connection to a measurable lift on one forecasting problem you choose.

Yes — most customers run fully isolated cloud deployments, and hybrid or on-prem is available for regulated environments. Ask us about your specific constraints.

You'll have a measured error-reduction number specific to your data. From there it's a standard subscription — no re-implementation required.

Get started

Start with a six-week pilot

Bring one forecasting problem. We'll deploy inside your own environment and show measurable lift before you commit to anything.

Request a pilot
Get in touch

Let's build something intelligent

Reach out for a demo, a pilot, or just to talk through your forecasting problem.

hello@aetherlogic.ai
+2349117178075
Wadata Market Makurdi Benue State, Nigeria