Signal data is messy
Audio, sensor, motion, and clinical streams arrive noisy, fragmented, irregular, and deeply domain-specific.
Time-series AI for physical-world data
Signal intelligence for the real world.
Signal AI for noisy audio, sensor, clinical, industrial, and IoT streams, taking validated models all the way to server and edge deployment.
The problem
Time-series AI is still assembled by hand.
Teams still stitch together notebooks, DSP scripts, model experiments, explainability checks, and edge deployment by hand. The result is slow iteration, fragile reproducibility, and models that struggle to survive outside the lab.
The workflow is fragmented
Exploration, DSP, modeling, evaluation, and tracking live across notebooks, scripts, and specialist handoffs.
Deployment is the bottleneck
Reproducing the winning pipeline and preparing it for server or edge environments can take as much work as the modeling itself.
How we work
One guided lifecycle for real-world signals.
A validated methodology for the full time-series AI lifecycle, from exploration and signal processing through validation and server or edge export.
- 01
Data
Bring signal datasets into a versioned, reproducible workflow.
- 02
Explore
Profile patterns, gaps, distributions, and domain-specific signal behavior.
- 03
Process
Apply DSP, transforms, augmentation, and feature extraction with intent.
- 04
Model
Search model and pipeline options against task-specific KPIs.
- 05
Trust
Evaluate robustness, uncertainty, and explainability before deployment.
- 06
Deploy
Prepare the winning pipeline for server or edge environments.
Use cases
Validated across real-world time-series domains.
Our platform is already being used and validated across energy, health, human activity, and environmental signal domains.
01
Energy Analysis
Intelligent analysis of energy-related time-series data, including smart meters and power signals, for energy disaggregation, behavioral shifts, and anomaly detection.
02
Digital Health
Analysis and processing of diverse vital signs from wearables, medical devices, and sensors for novel digital health applications.
03
Human Activity
Recognition of human activity based on multimodal sensor streams from diverse smart wearable technologies.
04
Environmental Data
Analysis of massive, heterogeneous environmental data from diverse sources, including fixed stations, crowdsourcing, and social media streams.
Differentiation
Built for signals that have to leave the notebook.
Temporalis focuses where generic MLOps and analytics tools stop short: physical-world signals, model trust, and deployment constraints.
- Time-series-native workflows for audio, sensor, motion, and multivariate signals.
- Signal processing built into the AI lifecycle, not bolted on later.
- Experiment search across DSP, features, models, and deployment constraints.
- Robustness, uncertainty, and explainability before production decisions.
- Edge-ready export as a core outcome, not an afterthought.
- AI-assisted pipeline planning that guides DSP and modeling choices across the lifecycle.
Design partners
Building AI for noisy, regulated, or edge-deployed signals?
We're opening early technical briefings with teams working on clinical audio, IoT, industrial sensing, environmental monitoring, and other real-world time-series problems.
Early briefings are intended for teams evaluating time-series AI workflows, edge deployment, or applied research translation.