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 — from validated models 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 to 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.
Proof
Validated in the real world.
Our methodology is already deployed — across respiratory audio, IoT symptom tracking, and environmental sensing.
~400KB
Edge pipeline footprint
<50ms
Cough inference latency
3
Industry & EU deployments
3
Peer-reviewed publications
Validated signal work
Field work before platform polish.
Temporalis is grounded in deployments where signal quality, model trust, and edge constraints were part of the work from day one.
01
Respiratory audio AI
A deployed methodology for smartphone-ready cough and lung-sound analysis on noisy, real-world audio.
02
IoT symptom tracking
Time-series gesture recognition for field workflows around allergic-rhinitis symptom observation.
03
Environmental sensing
Signal intelligence across school sensors, wearables, and environmental biomarkers.
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.