Physical
AI
SiMa.ai confidential • February 2026

Scaling Physical AI

A browser-based executive presentation built from the attached deck, highlighting SiMa.ai’s platform positioning, hybrid AI architecture, software stack, and deployment story.

Company snapshot

Who SiMa.ai says it is

The deck positions the company around deep machine-learning talent, substantial financing, and active mass production shipments to customers.

Expertise
0

Best ML minds

The deck cites more than 180 industry-leading machine-learning experts.

Funding
$355M

Backed by major investors

Named investors include Fidelity, Maverick, Dell Tech, Point72, MSD, and Adage.

Commercial scale
100+

Customers shipping

The presentation emphasizes mass-production status and shipments to more than 100 customers.

Leadership

Leadership team emphasis

KR

Krishna Rangasayee

CEO, Founder

30+ years in semiconductors, including Xilinx, Cypress, Altera, and Groq, with public and private board experience.

HK

Harald Kroger

President, Automotive & Sales

30+ years in business and automotive leadership, including Bosch and Mercedes-Benz, with Tesla and Rivian board experience.

GH

Gopal Hegde

SVP Engineering

30+ years across startups and incumbents including Cisco, Cavium, and Intel, with a stated record of delivering products and generating $100M+ revenue.

AH

Azfar Hasib

Head of People & Culture

30+ years in HR and people leadership across public companies and startups including Byton, Cavium, and Virage Logic.

Product message

Era of Physical AI is here

◫

Modalix

In production, with the deck describing it as a second time A0 silicon to production milestone.

▣

SoM + dev kit

Available now, and framed as a pin-compatible replacement for the industry GPU leader.

✦

Software flexibility

The stack is presented as supporting CV, CNN, LLM, and LMM workloads with seamless multimodal compute.

The slide sequence pushes a single commercial thesis: hardware is already shipping, form factors are ready, and software breadth reduces adoption friction.

Reasoning narrative

Why reasoning changes Physical AI

Before

Computer vision

Rules-based workflows and deterministic pipelines anchored early edge AI deployments.

Before

Transformers + statistics

The deck presents them as useful but still insufficient for richer decision-making in dynamic machine environments.

Now

LLMs enable reasoning

SiMa.ai frames multimodal LLM capability as the shift from machines doing things to machines reasoning about things.

Adaptability to changes and faster time to market.
Higher accuracy in challenging conditions.
Less training effort and reduced need for in-house ML expertise.
Lower development complexity for agentic machine deployments.
Architecture thesis

Hybrid ML architectures dominate

Classical CV

Deterministic tracking

Rules, geometry, and tracking remain essential for fast, explainable perception tasks.

Deep Vision

CNN / ViT perception

Learned perception layers handle richer recognition, scene understanding, and object-level interpretation.

Reasoning

LMM / GenAI control

Multi-modal understanding and instruction-following add higher-level context for machine behavior.

A standout technical claim in the deck is more than 10x better latency for complex tracking, plus sensor fusion and multiple models operating in parallel for safety.

Platform framing

One platform for Physical AI

The platform story is organized around model build and deployment, with emphasis on low power, multiple form factors, and scalable model coverage.

Model
Build
Deploy
Scale
Under 15W

The deck highlights low-power deployment across multiple form factors.

Easy deployment

Modalix, Edgematic, SoM, and LLiMa are grouped as deployment enablers.

Performance and power

Throughput and multi-chip communication are positioned as core differentiators.

Scale

Coverage spans broad model support, MLOps, RAG, and MCP integration.

Pipeline coverage

Entire LMM application on one chip

Multi-modal sensor capture
Image preprocessing
Vision encoder
LLM
Use case logic
Speech to text
Text to speech
Competing stack
Image preprocessing
Vision encoder
LLM
Use case logic
STT
TTS

The core competitive claim is simple: SiMa.ai says its MLSoC Modalix accelerates the full multimodal pipeline, while competitors only cover selected stages.

Software stack

Modularized Docker architecture

ML Model

Parse, optimize, quantize, compile.

MPK Toolset

GStreamer, ML networks, I/O and compute plugins.

Device Manager

Code and data verification, packing, security.

Evaluate & Monitor

Debug, tune, and optimize deployed systems.

Deploy

Scale to multiple devices with packaging support.

①
Input
Customer model arrives as secure ONNX input with datasets and calibration assets.
②
Two-docker split
ModelSDK and MPK Toolset are modularized, with combined size noted at about 20GB.
③
Operational tooling
KPIs, logs, GDB, Log Cat, and deployment options are integrated into the workflow story.
User experience

Palette Edgematic workflow

App 1

Multi-model GenAI

Pre-installed CNN and LLM evaluation.

App 2

Object detection

Pipeline-based evaluation and stream selection.

App 3

People tracking

Upload videos and test live stream scenarios.

App 4

Pose estimation

Edge-ready demos for immediate experimentation.

App 5

Multi-camera

Examples show broader deployment patterns.

App 6

Deployment UI

Run applications after file selection and setup.

●
Easily evaluate pre-installed CNNs and LLMs.
●
Choose a pipeline and simply download the file.
●
Upload your own videos and create streams.
●
Deploy and run applications from the interface.
Executive close

Commercial story in one view

This online version preserves the original pitch direction: SiMa.ai combines low-power silicon, multimodal reasoning, full-pipeline acceleration, and modular deployment software to argue for scalable Physical AI adoption.

15W

Power target emphasized for practical deployments.

10x+

Latency improvement claim for complex tracking workloads.

ONE

Single-platform and single-chip narrative anchors the deck.

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