--- title: modelcat description: Modelcat ensures you can optimize your models for AI edge devices image: https://www.ai-techsales.com/hubfs/modelcat.jpg --- The AiT Stack  /  AI Model Development # AI that builds the AI your silicon was *made to run*. ModelCat is an autonomous model builder for edge, embedded and IoT devices. Give it your data, your target chip and your limits on speed, memory and power — it architects, trains, optimizes and hardware-validates the model, then hands it back ready to deploy. [Book a 30-minute intro →](https://www.ai-techsales.com/contact?hsLang=en) [Visit modelcat.ai ↗](https://www.modelcat.ai) ArchitectureDesign & IPVerification YieldOrchestrationAI Model Development The bottleneck ## Everyone has a model. Almost no one can ship it on time. The perception model is the part that demos well. Getting it optimized, quantized and re-targeted onto the actual NPU, DSP or MCU — inside a real power and latency budget — is where months disappear. And the moment the silicon underneath changes, much of that work is done again from scratch. The old way · DIY custom models 12–24 mo data → device - High risk the model misses its size, speed or power target - Depends on scarce “unicorn” ML + embedded engineers - Rebuilt by hand for every new chip and every new generation The ModelCat way · AI builds it for you ≤ 3 days data → ready-to-run model - You set the constraints — accuracy, memory, power — and hold them - Usable by developers, systems engineers and product owners - Re-target to new silicon by regenerating, not rewriting A model is portable. The work of fitting it to a chip is not — until you can generate that work instead of grinding it out by hand. How it works · AI in the loop ## Trained on thousands of architectures, so it can build the best-fit one for you. ModelCat learned from thousands of model architectures, training methods and real outcomes. You describe the job; it searches that space, builds candidates, and measures them on real hardware — then keeps learning as new architectures land. #### Upload your data Bring labeled data, or start from an included open-source dataset. #### Pick your target Choose from a wide, growing range of supported chips — or ask for yours. #### Set constraints Size, speed and power — or let ModelCat decide what fits best. #### Submit the job ModelCat architects, trains and optimizes candidate models for you. #### Review the set Get a set of optimized models with precision measurements you can trust. #### Explore & deploy Drill into every attribute, pick one, export to TFLite or other formats. ↻  New chip, new sensor, new generation? **Regenerate and re-validate** — don’t rebuild. What you get ## An entire model team, without having to be an AI expert. ** ### Check data quality ModelCat inspects your dataset up front, so the model performs in deployment — not just on the bench. ** ### Control the attributes Set the execution speed, power draw and memory footprint your model must live within. ** ### Built to order Every model is architected from scratch to your spec — tested, and ready to deploy. ** ### Anchored to the real world A built-in hardware farm validates on physical silicon, so the numbers reflect real results. ** ### Retarget across chips Model Retargeting moves a proven model to new silicon fast — the same job, a different target. ** ### Continually learning New architectures and training methods are added over time, so you always have the best options. Where ModelCat fits the stack ## The stack builds the silicon. ModelCat turns it into shipped intelligence. Our [EDA 3.0](https://www.ai-techsales.com/what-is-EDA-3.0?hsLang=en) thesis runs from intent to yield — architecture, design, verification, manufacturing. ModelCat sits at the far end of that line: it takes finished silicon and makes it *useful*, generating the on-device model the chip was built to run. It pairs naturally with CraftifAI — one generates the model, the other generates the pipeline around it. ModelCat · the model ### Generate the model from intent Declare the data, target and constraints; get a hardware-optimized model back, validated on real silicon and ready to deploy. CraftifAI PipeGen · the pipeline ### Generate the pipeline that runs it Capture, preprocessing, inference scheduling and post-processing, mapped to the target and re-targetable across compute platforms. + For a chipmaker, that combination is a design-win engine: it makes your silicon dramatically easier to adopt for on-device AI. It’s the same logic behind ModelCat’s work with leading semiconductor platforms — NXP — eIQ® Model CreatorAlif SemiconductorSilicon Labs Read the thinking ## Why we’re watching on-device AI — and what ModelCat is publishing. ### [The Watchtower Brief · Edge AI From Model to Motion: The Perception-Pipeline Bottleneck in Edge AI The model is the easy 10%. The system between it and the silicon is where the schedule goes — and why deployment, not training, is the next decade’s moat. Read on The Watchtower →](https://www.ai-techsales.com/ai-tech-sales-blog/from-model-to-motion-the-perception-pipeline-bottleneck-in-edge-ai?hsLang=en) ### [The Watchtower Brief · Portability One Spec, Every MCU: Rethinking IoT Firmware Why work that doesn’t transfer across silicon is the real tax — and why “regenerate, don’t rewrite” is the pattern that beats it. Read on The Watchtower →](https://www.ai-techsales.com/ai-tech-sales-blog/one-spec-every-mcu-rethinking-iot-firmware?hsLang=en) ### [From ModelCat · Partnership ModelCat launches eIQ® Model Creator for NXP devices ModelCat’s autonomous builder, brought directly into NXP’s eIQ toolchain to turbocharge AI model development on NXP silicon. Read on modelcat.ai ↗](https://www.modelcat.ai/blog/modelcat-tm-launches-eiq-r-model-creator-to-turbocharge-ai-model-development-for-nxp-devices) ### [From ModelCat · Platform Supported chips & the hardware farm See the growing list of targets ModelCat builds for, and the physical hardware farm it calibrates every model against. Explore modelcat.ai ↗](https://www.modelcat.ai/supported-chips) Let’s talk ## On-device AI program stuck between the model and the silicon? That’s the conversation. Thirty minutes on where you’re headed and which of our technologies gets you there — no slides required. [Book a 30-minute intro →](https://www.ai-techsales.com/contact?hsLang=en)