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

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

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Not sure where to start? Run a free AI opportunity scan →

Your next big idea is in good company

Partner

2019

AI-only since

150+

Projects Shipped in Production

$72MM

Business Value Created

50+

Companies Supported

How it works

Three steps. No surprises.

In a market full of vendors who sound the same, choosing wrong costs you six months you don't have. That's why we keep it simple:

Talk

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

Prove it in 5 days

We build a working prototype on your data, in your environment. You see results before you commit.

Ship it, own it

We build in your codebase, on your infrastructure. Everything we build, you own. No lock-in, no dependencies.

Book a discovery call

WE'VE SEEN THIS BEFORE

Sound familiar?

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 last vendor claimed AI expertise but couldn't survive contact with production
→ 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 no-code AI tools — none handle your real-world complexity
→ Want to adopt AI but genuinely not sure where to begin

How we solve this →

WHAT "TANGIBLE" LOOKS LIKE

5,000-page mortgage packages. 8 seconds. 95% precision.

LauraMac processes mortgage document packages — thousands of pages per package, hundreds of document types, noisy scans, inconsistent formats across states and years.

They tried other vendors. They tried building it themselves on AWS. Nothing survived contact with mortgage-grade compliance and precision requirements.

Softmax built a document intelligence pipeline that processes 5,000-page packages in 8 seconds. Split, classify, extract, verify, stack — all at 95% precision.

The engagement started four years ago. It kept expanding because it kept delivering. 80% cost reduction. And LauraMac's team owns everything we built.

"Within just three months, Softmax delivered a solution that accurately processes 5,000-page PDFs in only 8 seconds." — Amit Aggarwal, CTO @ LauraMac

See more stories →

Before us → After us

Document AI · Custom AI Models

40 min per mortgage package. Other vendors failed. 8 sec per 5,000-page package. 80% cost reduction. → AI Agent

60 hrs/week assembling reports by hand. 5 min per report. 12% more clients taken on. → Workflow Automation · Custom AI Models

Duplicate leads polluting CRM, wasting sales time. $6M new ARR from cleaned pipeline. → Document AI

Manual bank statement tracing for income verification. <2 min per decision. 90% less manual effort. → Agentic Analytics · Data Foundations Premium Retailer Confidential Accountant review missed a $3.5M discrepancy. $3.5MM loss caught before close. →

View all success stories →

WHY SOFTMAX

What you're actually hiring

AI-only since 2019

We were fine-tuning transformers before GPT-3 existed. Every engineer on our team builds production AI systems, full-time. We don't do web apps, mobile, or "digital transformation."

Production, not prototypes

We ship systems for clients with SOC 2, ISO 27001, and MISMO requirements — across multiple cloud platforms, with real latency and reliability targets. If your compliance team needs to sign off, we've done that dozens of times.

We build the infrastructure, not just the applications

Engram, our open-source context database for AI agents, is used by teams building persistent agent memory. You're hiring the team that makes the bricks — not just assembles them.

You own everything

Every line of code, every model weight, every architecture decision. Full documentation, runbooks, and training for your team. We build for handoff, not dependency.

What our clients say

Don't take our word for it. Here's what they say when we're not in the room.

Amit Aggarwal

CTO @ LauraMac

"We needed a Document AI feature capable of analyzing large, complex mortgage documents to assess loan risks. Within just three months, Softmax delivered a solution that accurately processes 5,000-page PDFs in only 8 seconds, verifies signatures, stacks documents, and reliably extracts data—even from complex, noisy scanned documents."

95%Document Processing Precision

80%Operation Cost Reduction

Michael Steel

CEO @ Flywheel Digital

"Softmax's AI Agent project was transformative for us. They moved fast, understood our business, and helped us save tons of time while elevating our brand promise."

95.8%Time Saved on Repetitive Work

12%More Businesses

Collin Stewart

CEO @ Predictable Revenue

"Softmax delivered a bespoke AI solution that transformed our workflow, driving significant gains in both productivity and our NPS. Most impressively, they took us from initial design to full deployment in just four weeks."

4 WeeksFrom Conception to Deployment

15%Productivity Gain

Start with our interactive AI tools and free resources

Try them out, experience how we make AI work.

Interactive tool

AI Opportunity Snapshot

Describe your situation in plain English and get a personalized AI roadmap with solution architecture, tools, and next steps.

Try it free →

RAG Chatbot

Chat with this website

Ask questions about our services, approach, and capabilities in plain English. No keyword searching — just ask what you want to know.

Start chatting →

Open source

Engram — Context DB for AI Agents

Our open-source library for agentic context storage. Brain-inspired memory that works across any LLM, any framework.

Learn more →

Quick bites from our blog

arXiv deep dives, agentic design patterns, fine-tuning tutorials, and production AI lessons — explained so any engineer can follow. New posts every two days.

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

LLM

What is Gemma 4 and how to use, finetune it

Google just dropped Gemma 4, calling it their most capable family of open models to date. Built from the same research behind Gemini, these models pack serious multimodal intelligence into packages small enough to run on your phone and large enough to compete with frontier models on a server. If you’ve been following the open-weight model space, this is a big deal — and not just because of the benchmarks. Gemma 4Our most intelligent open models, built from Gemini 3 research.Google DeepMindGem

#finetuning

How to finetune Yuan 3.0 on your local machine - Practical Guide

We previously wrote about how to fine-tune Kimi 2.5 . We talked about Yuan 3.0 in depth in another post. This time we're tackling Yuan 3.0 Flash — a 40B-parameter MoE model that activates only 3.7B parameters per inference. It was built specifically for enterprise document workflows: RAG, table understanding, summarization, and multimodal document processing. Here's how to fine-tune it on your own hardware. Why Fine-Tune Yuan 3.0? Yuan 3.0 Flash already beats GPT-5.1 on enterprise RAG benchma

claude

5 Hidden Easter Eggs in the Claude Mythos Preview System Card

Anthropic just dropped the system card for Claude Mythos Preview — their most capable model to date, and one they've decided not to release publicly. At 245 pages, it's dense. Most commentary has focused on the big-picture safety story: the model is too capable for general release, it's being used for defensive cybersecurity only, etc. But buried in those pages are some genuinely wild details that read more like science fiction than a technical safety document. Here are five that stopped me mid

Your customers are waiting. Your board is asking.

Let's get something real into production. We've shipped 150+ AI systems since 2019 — in your codebase, on your timeline. Everything we build, you own.

Book a discovery call

Not sure where to start? Run a free AI opportunity scan →

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