AI for sensorindustrialmedicalfinancialIoToperationalmachinemarketsensor data.

InertialAI builds foundation models that connect time-series, sensor streams, and language context in one representation, so teams can search, classify, forecast, and explain operational data.

Explore the API Read Chronicle

API->

OpenAI-compatible embeddings for signal and language retrieval.

Research->

Foundation-model work for operational, sensor, and market data.

Docs->

Quickstarts, API references, examples, and integration guides.

A compact API for multimodal signal search.

Embed real-world signals and their context through an OpenAI-compatible API. Docs

EmbeddingForecastsoon

python

from openai import OpenAI

client = OpenAI(api_key="iai_...", base_url="https://inertialai.com/api/v1")

def embed(time_series, text=""):
    resp = client.embeddings.create(
        model="chronicle-embed-alpha",
        input={"time_series": time_series, "text": text},
    )
    return resp.data[0].embedding

query_emb = embed(time_series=[0.42, 1.87, 0.95, 2.31, 0.18])

sensor_embs = [
    embed(time_series=r["values"], text=r["label"])
    for r in sensor_log
]

ranked = sorted(sensor_embs, key=lambda e: cosine_sim(e, query_emb), reverse=True)
results = ranked[:5]

Research

Foundation-model work for signals, context, and language.

Preprint2026

Chronicle: A Multimodal Foundation Model for Joint Language and Time Series Understanding

Coming Soon

Cloud availability

Models where production teams already deploy

We are preparing InertialAI models for the major cloud model marketplaces, giving teams a governed path to evaluate and deploy through their existing cloud accounts.