Signal data is messy
Audio, sensor, motion, and clinical streams arrive noisy, fragmented, irregular, and deeply domain-specific.
Time-series intelligence for the real world.
Time-series AI and Agentic AI collaborate effectively to properly process and analyze noisy financial, audio, sensorial, clinical, industrial, and IoT streams, taking AI pipelines all the way to production environments, server-side or at the edge.
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.
Who We Are
A holistic platform for time-series AI, from experimentation to production.
Temporalis is a start-up that takes time-series data from raw signal to production AI, on one in-house platform for signal processing (DSP), machine learning, and neural networks.
Choose the right DSP
Select the signal-processing steps and AI models that fit each dataset.
Explore the hyperspace
Set batches of hyperparameters for processing and modeling, and converge fast on an optimal solution.
Explain every decision
Explainable AI (XAI) and Conversational AI guide the search toward the best pipeline.
Export to production
End-to-end AI pipelines are exported in a format ready for delivery.
How we work
One guided AI lifecycle for real-world time-series.
A validated methodology for the full time-series AI lifecycle, from exploration and signal processing through validation and server or edge deployment, using one coherent platform, with simple clicks or chat.
- 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.
Energy Analysis
Intelligent analysis of energy-related time-series data, including smart meters and power signals, for energy disaggregation, behavioral shifts, and anomaly detection.
Digital Health
Analysis and processing of diverse vital signs from wearables, medical devices, and sensors for novel digital health applications.
Human Activity
Recognition of human activity based on multimodal sensor streams from diverse smart wearable technologies.
Environmental Data
Analysis of massive, heterogeneous environmental data from diverse sources, including fixed stations, crowdsourcing, and social media streams.
Differentiation
Built for time-series data that have to leave the notebook.
Temporalis focuses where generic MLOps and analytics tools stop short: physical-world time-series, AI model trust, and deployment constraints.
- Time-series-native workflows for audio, sensor, motion, and multivariate time-series data.
- Signal processing built into the AI lifecycle, not bolted on later.
- Experiment search across DSP, feature extraction, models, and deployment constraints.
- Robustness, trustworthiness, and explainability before production decisions.
- AI-pipeline export for server or edge environment as a core outcome, not an afterthought.
- AI-assisted pipeline planning that guides DSP and AI modeling choices across the lifecycle.
Design partners
Building AI for noisy, regulated, server-side or edge-deployed time-series?
We're opening early technical briefings with teams working on real-world scenarios that engage financial, clinical, audio, IoT, industrial, environmental, and other time-series data.
Early briefings are intended for teams evaluating time-series AI workflows, edge deployment, or applied research translation.