We've been building AI-powered products, infrastructure, and workflows long before the hype. We'll help you use AI where it brings the most value to your project.
13+ years
of shipping production software
400+
engineers, including a dedicated AI team
React Native ExecuTorch
our open-source on-device AI library
We've been building AI-powered products long before they became mainstream; integrating AI into apps, researching LLMs, and making models run directly on users' devices. Along the way, we learned both what these models are capable of and where their limitations begin.
When AI coding tools arrived, of course, we embraced them too. But getting AI to generate code is the easy part; the real challenge is building the workflows, validation, and tooling that actually makes engineering teams more productive. That's exactly why we built tools like Argent – to solve problems we faced ourselves.
Today, we help teams get real value from AI-assisted development. So, whether you need production-ready AI features, better engineering workflows, or custom tools and pipelines, we'll get you there faster.
Run AI directly on your users' devices, without sending data to the cloud. We've spent years making local AI practical on mobile, like running LLMs, ASR, or computer vision models in React Native apps. We'll help you choose the right models, optimize inference, reduce memory usage, and deliver AI experiences that are private, responsive, infinitely scalable, and available even without an internet connection.
Getting AI to work in a demo is easy, but making it reliable for thousands of users is the hard part. We know how to build AI features that stay accurate, predictable, and trustworthy by combining techniques like model fine-tuning and quantization, evaluations and benchmarks, RAG and context management, prompt engineering and agentic pipelines – backed by the testing and monitoring needed to keep everything reliable in production.
A quick AI prototype can prove an idea, but turning it into a product people can rely on is a different story. If your app started with Claude Code, Cursor, Lovable, or an internal proof of concept, we can turn it into production-ready software. We'll build a scalable architecture, automated testing, CI/CD, monitoring, and the engineering practices your team needs to keep shipping instead of fighting technical debt.
Everyone knows how to use Claude Code, Codex, or OpenCode. The challenge is turning AI into a real productivity multiplier instead of another source of technical debt. We help engineering teams build the workflows around AI – validation pipelines, custom tools, agentic automation, and development practices that let developers ship faster while keeping quality high. If your team is using AI every day but not seeing the productivity gains you expected, we can help.
Reduce token usage both in day-to-day agentic coding workflows and in deployed production systems. We help identify where tokens are being wasted and optimize prompts, context, workflows, and model usage to reduce costs while preserving the quality of the results.
Make agentic workflows more reliable, faster, and easier to understand. We help debug reliability issues, improve end-to-end latency, and build benchmarking and visibility into agent behavior so workflows can be measured and systematically improved.
Open base models will take you a long way, but sometimes your product needs something they simply can't do. We work at the model level as well as the app level: fine-tuning LLMs on your data and for your specific task, and training new models from the ground up when that's what the problem calls for. Then we integrate the models into custom pipelines built around your product, so they do the job inside a dedicated system.
We build AI-powered workflows and agents that automate repetitive tasks, streamline decisions, and improve accuracy, so your team can focus on what matters most. Let's build solutions that fit the way your business works.
Run AI directly on your users' devices, without sending data to the cloud. We've spent years making local AI practical on mobile, like running LLMs, ASR, or computer vision models in React Native apps. We'll help you choose the right models, optimize inference, reduce memory usage, and deliver AI experiences that are private, responsive, infinitely scalable, and available even without an internet connection.
Getting AI to work in a demo is easy, but making it reliable for thousands of users is the hard part. We know how to build AI features that stay accurate, predictable, and trustworthy by combining techniques like model fine-tuning and quantization, evaluations and benchmarks, RAG and context management, prompt engineering and agentic pipelines – backed by the testing and monitoring needed to keep everything reliable in production.
A quick AI prototype can prove an idea, but turning it into a product people can rely on is a different story. If your app started with Claude Code, Cursor, Lovable, or an internal proof of concept, we can turn it into production-ready software. We'll build a scalable architecture, automated testing, CI/CD, monitoring, and the engineering practices your team needs to keep shipping instead of fighting technical debt.
Everyone knows how to use Claude Code, Codex, or OpenCode. The challenge is turning AI into a real productivity multiplier instead of another source of technical debt. We help engineering teams build the workflows around AI – validation pipelines, custom tools, agentic automation, and development practices that let developers ship faster while keeping quality high. If your team is using AI every day but not seeing the productivity gains you expected, we can help.
Reduce token usage both in day-to-day agentic coding workflows and in deployed production systems. We help identify where tokens are being wasted and optimize prompts, context, workflows, and model usage to reduce costs while preserving the quality of the results.
Make agentic workflows more reliable, faster, and easier to understand. We help debug reliability issues, improve end-to-end latency, and build benchmarking and visibility into agent behavior so workflows can be measured and systematically improved.
Open base models will take you a long way, but sometimes your product needs something they simply can't do. We work at the model level as well as the app level: fine-tuning LLMs on your data and for your specific task, and training new models from the ground up when that's what the problem calls for. Then we integrate the models into custom pipelines built around your product, so they do the job inside a dedicated system.
We build AI-powered workflows and agents that automate repetitive tasks, streamline decisions, and improve accuracy, so your team can focus on what matters most. Let's build solutions that fit the way your business works.
Run AI directly on your users' devices, without sending data to the cloud. We've spent years making local AI practical on mobile, like running LLMs, ASR, or computer vision models in React Native apps. We'll help you choose the right models, optimize inference, reduce memory usage, and deliver AI experiences that are private, responsive, infinitely scalable, and available even without an internet connection.
Getting AI to work in a demo is easy, but making it reliable for thousands of users is the hard part. We know how to build AI features that stay accurate, predictable, and trustworthy by combining techniques like model fine-tuning and quantization, evaluations and benchmarks, RAG and context management, prompt engineering and agentic pipelines – backed by the testing and monitoring needed to keep everything reliable in production.
A quick AI prototype can prove an idea, but turning it into a product people can rely on is a different story. If your app started with Claude Code, Cursor, Lovable, or an internal proof of concept, we can turn it into production-ready software. We'll build a scalable architecture, automated testing, CI/CD, monitoring, and the engineering practices your team needs to keep shipping instead of fighting technical debt.
Everyone knows how to use Claude Code, Codex, or OpenCode. The challenge is turning AI into a real productivity multiplier instead of another source of technical debt. We help engineering teams build the workflows around AI – validation pipelines, custom tools, agentic automation, and development practices that let developers ship faster while keeping quality high. If your team is using AI every day but not seeing the productivity gains you expected, we can help.
Reduce token usage both in day-to-day agentic coding workflows and in deployed production systems. We help identify where tokens are being wasted and optimize prompts, context, workflows, and model usage to reduce costs while preserving the quality of the results.
Make agentic workflows more reliable, faster, and easier to understand. We help debug reliability issues, improve end-to-end latency, and build benchmarking and visibility into agent behavior so workflows can be measured and systematically improved.
Open base models will take you a long way, but sometimes your product needs something they simply can't do. We work at the model level as well as the app level: fine-tuning LLMs on your data and for your specific task, and training new models from the ground up when that's what the problem calls for. Then we integrate the models into custom pipelines built around your product, so they do the job inside a dedicated system.
We build AI-powered workflows and agents that automate repetitive tasks, streamline decisions, and improve accuracy, so your team can focus on what matters most. Let's build solutions that fit the way your business works.
Run AI directly on your users' devices, without sending data to the cloud. We've spent years making local AI practical on mobile, like running LLMs, ASR, or computer vision models in React Native apps. We'll help you choose the right models, optimize inference, reduce memory usage, and deliver AI experiences that are private, responsive, infinitely scalable, and available even without an internet connection.
Getting AI to work in a demo is easy, but making it reliable for thousands of users is the hard part. We know how to build AI features that stay accurate, predictable, and trustworthy by combining techniques like model fine-tuning and quantization, evaluations and benchmarks, RAG and context management, prompt engineering and agentic pipelines – backed by the testing and monitoring needed to keep everything reliable in production.
A quick AI prototype can prove an idea, but turning it into a product people can rely on is a different story. If your app started with Claude Code, Cursor, Lovable, or an internal proof of concept, we can turn it into production-ready software. We'll build a scalable architecture, automated testing, CI/CD, monitoring, and the engineering practices your team needs to keep shipping instead of fighting technical debt.
Everyone knows how to use Claude Code, Codex, or OpenCode. The challenge is turning AI into a real productivity multiplier instead of another source of technical debt. We help engineering teams build the workflows around AI – validation pipelines, custom tools, agentic automation, and development practices that let developers ship faster while keeping quality high. If your team is using AI every day but not seeing the productivity gains you expected, we can help.
Reduce token usage both in day-to-day agentic coding workflows and in deployed production systems. We help identify where tokens are being wasted and optimize prompts, context, workflows, and model usage to reduce costs while preserving the quality of the results.
Make agentic workflows more reliable, faster, and easier to understand. We help debug reliability issues, improve end-to-end latency, and build benchmarking and visibility into agent behavior so workflows can be measured and systematically improved.
Open base models will take you a long way, but sometimes your product needs something they simply can't do. We work at the model level as well as the app level: fine-tuning LLMs on your data and for your specific task, and training new models from the ground up when that's what the problem calls for. Then we integrate the models into custom pipelines built around your product, so they do the job inside a dedicated system.
We build AI-powered workflows and agents that automate repetitive tasks, streamline decisions, and improve accuracy, so your team can focus on what matters most. Let's build solutions that fit the way your business works.
Run AI directly on your users' devices, without sending data to the cloud. We've spent years making local AI practical on mobile, like running LLMs, ASR, or computer vision models in React Native apps. We'll help you choose the right models, optimize inference, reduce memory usage, and deliver AI experiences that are private, responsive, infinitely scalable, and available even without an internet connection.
Getting AI to work in a demo is easy, but making it reliable for thousands of users is the hard part. We know how to build AI features that stay accurate, predictable, and trustworthy by combining techniques like model fine-tuning and quantization, evaluations and benchmarks, RAG and context management, prompt engineering and agentic pipelines – backed by the testing and monitoring needed to keep everything reliable in production.
A quick AI prototype can prove an idea, but turning it into a product people can rely on is a different story. If your app started with Claude Code, Cursor, Lovable, or an internal proof of concept, we can turn it into production-ready software. We'll build a scalable architecture, automated testing, CI/CD, monitoring, and the engineering practices your team needs to keep shipping instead of fighting technical debt.
Everyone knows how to use Claude Code, Codex, or OpenCode. The challenge is turning AI into a real productivity multiplier instead of another source of technical debt. We help engineering teams build the workflows around AI – validation pipelines, custom tools, agentic automation, and development practices that let developers ship faster while keeping quality high. If your team is using AI every day but not seeing the productivity gains you expected, we can help.
Reduce token usage both in day-to-day agentic coding workflows and in deployed production systems. We help identify where tokens are being wasted and optimize prompts, context, workflows, and model usage to reduce costs while preserving the quality of the results.
Make agentic workflows more reliable, faster, and easier to understand. We help debug reliability issues, improve end-to-end latency, and build benchmarking and visibility into agent behavior so workflows can be measured and systematically improved.
Open base models will take you a long way, but sometimes your product needs something they simply can't do. We work at the model level as well as the app level: fine-tuning LLMs on your data and for your specific task, and training new models from the ground up when that's what the problem calls for. Then we integrate the models into custom pipelines built around your product, so they do the job inside a dedicated system.
We build AI-powered workflows and agents that automate repetitive tasks, streamline decisions, and improve accuracy, so your team can focus on what matters most. Let's build solutions that fit the way your business works.
Run AI directly on your users' devices, without sending data to the cloud. We've spent years making local AI practical on mobile, like running LLMs, ASR, or computer vision models in React Native apps. We'll help you choose the right models, optimize inference, reduce memory usage, and deliver AI experiences that are private, responsive, infinitely scalable, and available even without an internet connection.
Getting AI to work in a demo is easy, but making it reliable for thousands of users is the hard part. We know how to build AI features that stay accurate, predictable, and trustworthy by combining techniques like model fine-tuning and quantization, evaluations and benchmarks, RAG and context management, prompt engineering and agentic pipelines – backed by the testing and monitoring needed to keep everything reliable in production.
A quick AI prototype can prove an idea, but turning it into a product people can rely on is a different story. If your app started with Claude Code, Cursor, Lovable, or an internal proof of concept, we can turn it into production-ready software. We'll build a scalable architecture, automated testing, CI/CD, monitoring, and the engineering practices your team needs to keep shipping instead of fighting technical debt.
Everyone knows how to use Claude Code, Codex, or OpenCode. The challenge is turning AI into a real productivity multiplier instead of another source of technical debt. We help engineering teams build the workflows around AI – validation pipelines, custom tools, agentic automation, and development practices that let developers ship faster while keeping quality high. If your team is using AI every day but not seeing the productivity gains you expected, we can help.
Reduce token usage both in day-to-day agentic coding workflows and in deployed production systems. We help identify where tokens are being wasted and optimize prompts, context, workflows, and model usage to reduce costs while preserving the quality of the results.
Make agentic workflows more reliable, faster, and easier to understand. We help debug reliability issues, improve end-to-end latency, and build benchmarking and visibility into agent behavior so workflows can be measured and systematically improved.
Open base models will take you a long way, but sometimes your product needs something they simply can't do. We work at the model level as well as the app level: fine-tuning LLMs on your data and for your specific task, and training new models from the ground up when that's what the problem calls for. Then we integrate the models into custom pipelines built around your product, so they do the job inside a dedicated system.
We build AI-powered workflows and agents that automate repetitive tasks, streamline decisions, and improve accuracy, so your team can focus on what matters most. Let's build solutions that fit the way your business works.
Run AI directly on your users' devices, without sending data to the cloud. We've spent years making local AI practical on mobile, like running LLMs, ASR, or computer vision models in React Native apps. We'll help you choose the right models, optimize inference, reduce memory usage, and deliver AI experiences that are private, responsive, infinitely scalable, and available even without an internet connection.
Getting AI to work in a demo is easy, but making it reliable for thousands of users is the hard part. We know how to build AI features that stay accurate, predictable, and trustworthy by combining techniques like model fine-tuning and quantization, evaluations and benchmarks, RAG and context management, prompt engineering and agentic pipelines – backed by the testing and monitoring needed to keep everything reliable in production.
A quick AI prototype can prove an idea, but turning it into a product people can rely on is a different story. If your app started with Claude Code, Cursor, Lovable, or an internal proof of concept, we can turn it into production-ready software. We'll build a scalable architecture, automated testing, CI/CD, monitoring, and the engineering practices your team needs to keep shipping instead of fighting technical debt.
Everyone knows how to use Claude Code, Codex, or OpenCode. The challenge is turning AI into a real productivity multiplier instead of another source of technical debt. We help engineering teams build the workflows around AI – validation pipelines, custom tools, agentic automation, and development practices that let developers ship faster while keeping quality high. If your team is using AI every day but not seeing the productivity gains you expected, we can help.
Reduce token usage both in day-to-day agentic coding workflows and in deployed production systems. We help identify where tokens are being wasted and optimize prompts, context, workflows, and model usage to reduce costs while preserving the quality of the results.
Make agentic workflows more reliable, faster, and easier to understand. We help debug reliability issues, improve end-to-end latency, and build benchmarking and visibility into agent behavior so workflows can be measured and systematically improved.
Open base models will take you a long way, but sometimes your product needs something they simply can't do. We work at the model level as well as the app level: fine-tuning LLMs on your data and for your specific task, and training new models from the ground up when that's what the problem calls for. Then we integrate the models into custom pipelines built around your product, so they do the job inside a dedicated system.
We build AI-powered workflows and agents that automate repetitive tasks, streamline decisions, and improve accuracy, so your team can focus on what matters most. Let's build solutions that fit the way your business works.
Run AI directly on your users' devices, without sending data to the cloud. We've spent years making local AI practical on mobile, like running LLMs, ASR, or computer vision models in React Native apps. We'll help you choose the right models, optimize inference, reduce memory usage, and deliver AI experiences that are private, responsive, infinitely scalable, and available even without an internet connection.
Getting AI to work in a demo is easy, but making it reliable for thousands of users is the hard part. We know how to build AI features that stay accurate, predictable, and trustworthy by combining techniques like model fine-tuning and quantization, evaluations and benchmarks, RAG and context management, prompt engineering and agentic pipelines – backed by the testing and monitoring needed to keep everything reliable in production.
A quick AI prototype can prove an idea, but turning it into a product people can rely on is a different story. If your app started with Claude Code, Cursor, Lovable, or an internal proof of concept, we can turn it into production-ready software. We'll build a scalable architecture, automated testing, CI/CD, monitoring, and the engineering practices your team needs to keep shipping instead of fighting technical debt.
Everyone knows how to use Claude Code, Codex, or OpenCode. The challenge is turning AI into a real productivity multiplier instead of another source of technical debt. We help engineering teams build the workflows around AI – validation pipelines, custom tools, agentic automation, and development practices that let developers ship faster while keeping quality high. If your team is using AI every day but not seeing the productivity gains you expected, we can help.
Reduce token usage both in day-to-day agentic coding workflows and in deployed production systems. We help identify where tokens are being wasted and optimize prompts, context, workflows, and model usage to reduce costs while preserving the quality of the results.
Make agentic workflows more reliable, faster, and easier to understand. We help debug reliability issues, improve end-to-end latency, and build benchmarking and visibility into agent behavior so workflows can be measured and systematically improved.
Open base models will take you a long way, but sometimes your product needs something they simply can't do. We work at the model level as well as the app level: fine-tuning LLMs on your data and for your specific task, and training new models from the ground up when that's what the problem calls for. Then we integrate the models into custom pipelines built around your product, so they do the job inside a dedicated system.
We build AI-powered workflows and agents that automate repetitive tasks, streamline decisions, and improve accuracy, so your team can focus on what matters most. Let's build solutions that fit the way your business works.
Run AI directly on your users' devices, without sending data to the cloud. We've spent years making local AI practical on mobile, like running LLMs, ASR, or computer vision models in React Native apps. We'll help you choose the right models, optimize inference, reduce memory usage, and deliver AI experiences that are private, responsive, infinitely scalable, and available even without an internet connection.
Getting AI to work in a demo is easy, but making it reliable for thousands of users is the hard part. We know how to build AI features that stay accurate, predictable, and trustworthy by combining techniques like model fine-tuning and quantization, evaluations and benchmarks, RAG and context management, prompt engineering and agentic pipelines – backed by the testing and monitoring needed to keep everything reliable in production.
A quick AI prototype can prove an idea, but turning it into a product people can rely on is a different story. If your app started with Claude Code, Cursor, Lovable, or an internal proof of concept, we can turn it into production-ready software. We'll build a scalable architecture, automated testing, CI/CD, monitoring, and the engineering practices your team needs to keep shipping instead of fighting technical debt.
Everyone knows how to use Claude Code, Codex, or OpenCode. The challenge is turning AI into a real productivity multiplier instead of another source of technical debt. We help engineering teams build the workflows around AI – validation pipelines, custom tools, agentic automation, and development practices that let developers ship faster while keeping quality high. If your team is using AI every day but not seeing the productivity gains you expected, we can help.
Reduce token usage both in day-to-day agentic coding workflows and in deployed production systems. We help identify where tokens are being wasted and optimize prompts, context, workflows, and model usage to reduce costs while preserving the quality of the results.
Make agentic workflows more reliable, faster, and easier to understand. We help debug reliability issues, improve end-to-end latency, and build benchmarking and visibility into agent behavior so workflows can be measured and systematically improved.
Open base models will take you a long way, but sometimes your product needs something they simply can't do. We work at the model level as well as the app level: fine-tuning LLMs on your data and for your specific task, and training new models from the ground up when that's what the problem calls for. Then we integrate the models into custom pipelines built around your product, so they do the job inside a dedicated system.
We build AI-powered workflows and agents that automate repetitive tasks, streamline decisions, and improve accuracy, so your team can focus on what matters most. Let's build solutions that fit the way your business works.
Private Mind is an AI assistant that runs entirely on your device. It's a real-world demonstration of how we build private, responsive AI experiences powered by local language, speech, and vision models.
We built key parts of the AI-powered app builder for Vibecode, where anyone can create mobile and web apps using natural language. Starting from an early prototype, we turned it into a product featured by Anthropic as a flagship Claude-powered app.
Before on-device AI became popular, we were already shipping it in real products: we built React Native ExecuTorch, a library for running AI models directly on the device, and Private Mind, an on-device AI assistant.
Being so well connected in the React Native community and supporting key open source projects, we know Software Mansion is always up on the latest developments.
Stephen Burgess
Engineering Manager, Chime
We've created Argent, an agentic toolkit that controls, debugs, and profiles iOS and Android applications. And users love it, stating it's the most impactful dev tool that improved their workflows.
Private Mind is an AI assistant that runs entirely on your device. It's a real-world demonstration of how we build private, responsive AI experiences powered by local language, speech, and vision models.
Before on-device AI became popular, we were already shipping it in real products: we built React Native ExecuTorch, a library for running AI models directly on the device, and Private Mind, an on-device AI assistant.
We built key parts of the AI-powered app builder for Vibecode, where anyone can create mobile and web apps using natural language. Starting from an early prototype, we turned it into a product featured by Anthropic as a flagship Claude-powered app.
Being so well connected in the React Native community and supporting key open source projects, we know Software Mansion is always up on the latest developments.
Stephen Burgess
Engineering Manager, Chime
We've created Argent, an agentic toolkit that controls, debugs, and profiles iOS and Android applications. And users love it, stating it's the most impactful dev tool that improved their workflows.
Tell us what you're building, where you think we could help, or what challenges you're running into. We'll help you figure out the right approach.
