AI engineering for systems that can't afford to fail.

Built by the team that's done it 150+ times since 2019.

Your next big idea is in good company

Partner AWS Machine Learning Partner
Partner Databricks Partner
2019
AI-only since
150+
Production Systems Shipped
$72MM
Business Value Created
50+
Companies Supported

WE'VE SEEN THIS BEFORE

We know your pains

Every AI idea on LinkedIn sounds inspiring — until you need to put them in production.

Building a product

  • Board wants "everything AI" — your product can't afford to break
  • AI-native startups shipping features weekly — eroding your market share rapidly
  • Can't hire ML engineers — 6 months open, zero closes
  • Your web dev agency turned "AI expert" — ships vibe-coded ChatGPT wrappers that crumble on real usage
  • Need a feature factory — but agencies own your IP
How we solve this →

Running operations

  • Repetitive back-office tasks burning labor and blocking capacity for new clients
  • Copy-paste workflows — an invitation to errors and inconsistent standards
  • Overwhelmed by "AI products" — none fit your specific use case or team
  • Tried vibe-coding and no-code AI — they turn your data into a punchline
  • Want to adopt AI but genuinely not sure where to begin
How we solve this →

Two ways we deploy AI that pays for itself

Not sure what these are? Learn the concepts →

01

AI Product Features

For software companies

We build AI capabilities directly into your product — so your customers get faster workflows, smarter automation, and a reason to stay.

Examples include:
  • Document AI — extract, classify, and verify from noisy scans and complex forms ?
  • AI Agents — in-product AI that completes real tasks, not a chatbot demo ?
  • Model fine-tuning — domain-adapted models for edge cases prompting can't solve ?
  • Custom model training — purpose-built models for your data, deployed on cloud or edge ?
Learn more →
02

Internal AI Apps

For operations & leadership teams

We build internal tools powered by AI — so your team spends less time on manual work and more time on decisions that matter.

Examples include:
  • Agentic analytics — deep agents that surface evidence-backed answers, not dashboards ?
  • Reporting automation — unify ads, CRM, and ecommerce into client-ready reports
  • Workflow automation — eliminate repetitive tasks pulling your team off high-value work ?
Learn more →

Featured success stories

View all success stories →

WHY SOFTMAX

Simply put, we know what we are talking about

AI-only since 2019

We don't do web apps, mobile, or "digital transformation." Every engineer on our team builds production AI systems, full-time. That's all we've done for seven years.

Senior AI engineers, not offshore juniors

Our team trained models in graduate school, taught AI at MIT before LLMs went mainstream. Not people who learned AI from YouTube two years ago. We build multi-modal pipelines, fine-tune LLMs, and deploy real infrastructure — it's simply a different caliber.

Production, not prototypes

We don't vibe-code a demo and call it done. We ship systems built for clients with SOC 2, ISO 27001, and MISMO requirements — systems that handle edge cases, pass audits, and run reliably at scale.

What our clients say

Start with our interactive AI tools and free resources

Try them out, experience how we make AI work.

Quick bites from our blog

Practical AI knowledge from our engineering team.

SaaS

SaaS at a Junction Point: What we learned building AI in 2025

2025 has been an eventful year for most businesses. Tariff hikes, market volatility, renewed bubble talk—and, inevitably, everything AI. This year, we worked across mortgage, retail, real estate, and marketing—but the common thread wasn’t the industry, it was the economics. We built workflow automation for marketing agencies that lifted productivity by 12%. We deployed AI agents that helped retailers cut inventory costs while increasing turn rates. We consolidated fragmented data and built agen

AI Agent

Definitive Guide to Agentic Frameworks in 2026: Langgraph, CrewAI, AG2, OpenAI and more

This post is written as of Feb 26th 2026 and we will update it as things progress. Everyone's building agents right now. If you've been anywhere near the AI engineering space in the past year, you've watched the number of "agent frameworks" explode from a handful to... a lot. And honestly, picking the right one has become its own kind of problem. We started working on Agentic AI in production in 2025, and yet, we saw that there are so many features to develop with. Some of them are genuinely g

AI Agent

<Technical Deep Dive> Agentic Design Patterns

I spent the last few weeks digging into the major approaches, reading through docs, watching talks, and actually trying to build with some of these tools. Heres what I found, and more importantly, what I think actually matters when you're deciding how to build agentic systems. The Two Taxonomies That Actually Matter Before we get into specific tools and frameworks, its worth understanding that there are basically two influential ways people have sliced up agentic design patterns. And they com

computer vision

CNN vs transformer model difference in image processing

Let's start with an interesting experiment. Here is a Gemni-generated cow (above) with macaw feathers as the texture. Is it a cow or a Macaw? We used two vision models: ResNet and ViT to figure out what it is Let's start with ResNet: I am not sure where the Triceratops comes from, but Macaw is a plausible guess. Then we used ViT: It identifies it as an Ox, very close to a Cow. So why do these two models differ so much? I guess when you look at this image locally or globally you have reach

The AI engineering team you can rely on, for a long time.

We've shipped 150+ production AI systems since 2019. We work in your codebase, ship on your timeline, and hand off so your team owns it.

Book a discovery call

30 minutes. No pitch deck. We'll tell you if we're the right fit — and if we're not, we'll say so.