Axinity / Sentient SDK

A sentient layer over global commerce.

Axinity builds the Sentient SDK - one intelligence layer that perceives the entire social-commerce web, understands it as a single living ontology, and acts on it: from a single listing to a multinational expansion.

Get in touch

Commerce no longer happens in one place. It happens everywhere at once - across every platform, marketplace and storefront, in millions of videos, voices and scenes a day.No team can watch it. No dashboard can hold it. Every company sees a fragment and calls it the market.

We built the layer that sees it whole.

01 / Total perception

Every platform. Every modality. Every day.

Video, audio and scene classification across the whole social and commerce web - millions of pieces of content decomposed daily into objects, scenes, speech, sound, sentiment and intent.

Platform-agnostic by design: any network, any marketplace, any site resolves into one signal stream.

02 / The global ontology

One model of the commerce world.

Everything perception sees lands in one living ontology: products, brands, creators, audiences, trends, prices and places - connected across platforms, languages and markets.

Not a dashboard of metrics. A world model in which every signal has context. Perspective is what turns data into actionable insight.

03 / Beyond commerce

Wherever behavior leaves a signal.

The ontology does not stop at commerce - commerce is where it starts. Wherever human or machine behavior produces patterns, Sentient models it.

Video, audio, sensor streams: a dedicated engine for every signal class, all feeding the same ontology. Any domain where behavior emerges can be perceived, understood, predicted and acted on.

Vision engines

Scenes, objects, people and actions - extracted frame by frame.

Audio engines

Speech, sound and tone - understood across languages.

Sensor engines

Machine and environment telemetry - patterns beyond human senses.

04 / Every altitude of decision

From one listing to a new continent.

The same layer that reprices a listing models a market entry.

Dynamic listing

Storefronts and offers that adapt in real time - price, placement and presentation tuned continuously.

Campaign prediction

Entire campaigns simulated before they run - and the perfect people for each one drawn from the ontology, matched by what their content actually contains.

Expansion modelling

Multinational expansion planned as scenarios: which markets, which locations, in which sequence, with modelled outcomes.

05 / The whole loop

Plan. Predict. Execute. Follow. Report. Decide.

Intelligence that stops at insight leaves the hard part open. Ours stays in the loop - planning, executing, monitoring, reporting and deciding at every step: autonomous where allowed, guided where wanted.

Plan

Objectives become executable strategies.

Predict

Every strategy simulated before anything moves.

Execute

Decisions become actions across channels and systems.

Follow

Live monitoring of every running motion.

Report

Outcomes traced back to causes, not correlations.

Decide

Autonomous or guided - the loop closes and starts again.

06 / The Sentient SDK

The whole layer, in your environment.

Ontology, predictions and the decision loop - embeddable into any environment: storefronts, ERPs, data stacks, internal tools.

One SDK, platform-agnostic on both sides: what it reads and where it runs.

sentient.ts

import { Sentient } from "@axinity/sentient-sdk"; const sentient = new Sentient({ ontology: "global" }); const campaign = await sentient.predict.campaign({  brand: "acme",  market: "de-DE",  objective: "launch",}); const expansion = await sentient.model.expansion({  regions: ["eu-central", "apac"],  horizon: "24m",}); sentient.decide.on(campaign, { mode: "guided" });

07 / Deep system mechanics

Built at the metal.

This scale is not a prompt away. It is pipelines, schedulers, inference and storage systems engineered to the metal - throughput as a design principle.

We specialize in deep system mechanics, because a sentient layer is only as good as the machine underneath.

>Streaming-first pipelines - no batch windows, no blind spots

>Schedulers built for millions of classifications a day

>Inference placed where the data lives

>Every subsystem observable, replayable, auditable