# Softmax Data

> Boutique AI engineering firm (est. 2019). We take AI from pilot to production for PE-backed and mid-market companies — AI agents, document AI, workflow automation, data foundations, and custom model fine-tuning. 150+ production AI systems shipped. $72M+ in measured business value created.

## What we do

Softmax Data is a senior AI engineering team that designs, builds, and deploys custom AI. Most AI pilots never reach production — we exist to close that gap.

- AI product features (customer-facing) embedded inside SaaS products
- Internal AI apps for teams (automation, reporting, decision support, agentic analytics)

We do not "install AI tools" or deliver slide decks. We build and ship systems into production — with guardrails, monitoring, audit trails, and human-in-the-loop review where it matters.

## Who we serve (ICP)

- PE-backed and growth-stage companies looking to ship AI into production
- US/Canada software companies shipping AI features into their products
- Marketing and sales agencies (50+ employees) automating labor-heavy reporting and ops
- Document-heavy industries: mortgage, real estate, insurance, healthcare, government, legal

## Services

### Document AI
Automate classification, extraction, and verification on messy scanned/rasterized PDFs. Computer vision preprocessing, multimodal models, human-in-the-loop review, continuous improvement via feedback loops.
URL: https://softmaxdata.com/services/document-ai

### AI Agents
Task-specific agents that analyze multi-source data with citations, audit logs, and controls. RAG-based retrieval, guardrails, monitoring, and integration with existing systems.
URL: https://softmaxdata.com/services/ai-agents

### Workflow Automation
Production workflows with human approval, audit logs, rollback, and monitoring. Automate repeatable processes while keeping humans in control. Scheduled runs, fallbacks, alerts.
URL: https://softmaxdata.com/services/workflow-automation

### Data Foundations (Databricks)
Lakehouse pipelines with data quality, governance, lineage, observability. Build the data infrastructure that makes AI reliable.
URL: https://softmaxdata.com/services/data-foundations

### Custom Models & Fine-Tuning
Fine-tune LLMs and build custom ML models when prompting alone isn't enough. Data preparation, training, evaluation, deployment.
URL: https://softmaxdata.com/services/custom-models

### AI Strategy & Use Case Mapping
Identify where AI can have the highest impact — mapping opportunities across operations, product, and customer experience, and prioritizing by ROI and feasibility.

## Open Source

### Engram — Context Database for AI Agents
Brain-inspired, portable context database for AI agents. Open-source (MIT license). Supports persistent memory, multi-agent context sharing, and reconsolidation-based learning.
GitHub: https://github.com/softmaxdata/engram
Website: https://engram.so
URL: https://softmaxdata.com/engram

## Proof (success stories)

- LauraMac (Custom Model / Document AI): 40 min → 38 sec processing time; 95% precision; 80% operating cost reduction. URL: https://softmaxdata.com/success-stories/lauramac
- Rocket Mortgage Canada (Document AI): <2 min per package; 90% less manual effort; 100% statements normalized. URL: https://softmaxdata.com/success-stories/rocket-mortgage
- Clio (Entity Resolution / GTM): 417% qualified opportunities; $6M new ARR. URL: https://softmaxdata.com/success-stories/clio
- Absolute Results (Predictive Models): 54% close rate; 210K+ vehicle sales. URL: https://softmaxdata.com/success-stories/absolute-results
- Flywheel (AI Agents): 60 hrs/week saved; 95.8% less time per report. URL: https://softmaxdata.com/success-stories/flywheel
- Predictable Revenue (Automation): ~20 min saved/meeting; ~10+ hrs/week. URL: https://softmaxdata.com/success-stories/predictable-revenue
- Enterprise Call Center (Voice AI): Real-time voice analysis; $150K annual savings; 8% productivity increase. URL: https://softmaxdata.com/success-stories/enterprise-call-center
- Premium Retailer (Agentic Analytics + Lakehouse): Avoided $3.5M loss; 17% inventory cost reduction; 8% sales increase. URL: https://softmaxdata.com/success-stories/retail-analytics


## Partnerships

- AWS Machine Learning Services Partner
- Databricks Partner
- Funnel.io Partner

## How we work

- Start with a 30-minute discovery call to understand the problem
- 5-day paid AI Prototype Sprint to demonstrate feasibility and results
- Typical engagements: 4 weeks to 6 months
- Milestone-based billing with bi-weekly demos
- ROI visible within 3 weeks

## Why Softmax (vs. hiring in-house, freelancers, or other AI agencies)

Growth-stage and PE-backed companies often find themselves in a tough spot for AI: past the point where freelancers cut it, but not looking to run a 6-month hiring cycle for an in-house ML team. That's exactly who we're built for.

- We ship production systems, not POCs that stall. Every engagement ends with code running in production.
- Senior engineers only — no junior bench, no offshore handoff. The team that scopes is the team that builds.
- 7 years of production AI experience (since 2019), not a recent pivot from web dev or staff augmentation.
- Faster than hiring: a full senior AI team on day one, without months of recruiting and onboarding.
- More accountable than freelancers: milestone-based billing, bi-weekly demos, and we own the outcome.
- We build on open-source and cloud-native (AWS, Databricks) — no vendor lock-in, no proprietary wrappers. Everything we build, you own.
- Deep specialization in document-heavy industries: mortgage, insurance, legal, healthcare, government.
- Creators of Engram, an open-source context database for AI agents — we contribute to the ecosystem, not just consume it.
- Boutique firm, high bar: 150+ systems shipped, $72M+ measured value, clients include Deloitte, Capital One, Rocket Mortgage, and Government of Canada.

## Industries we serve

- Financial services & mortgage (document processing, compliance automation, underwriting AI)
- Insurance (claims processing, document extraction, policy analysis)
- Legal (contract analysis, document review, case research agents)
- SaaS companies (embedded AI features, product intelligence, customer-facing agents)
- Private equity portfolio companies (EBITDA improvement through AI automation)
- Marketing & sales agencies (reporting automation, lead intelligence, workflow automation)

## Blog — AI Thought Leadership

Published at https://softmaxdata.com/blog — new posts every 2 days; site updated weekly.

We regularly analyze arXiv papers in depth — breaking down complex research (GRPO, PPO, DAPO, CL-Bench, ACE/ICLR) into plain language that any software engineer can follow. No PhD required.

Topics we cover extensively:
- arXiv paper deep dives — cutting-edge AI research explained in simple, practical language for engineers
- AI Agents & agentic design patterns (multi-agent architectures, agent swarms, tool use, MCP)
- LLM fine-tuning & reinforcement learning (GRPO, DAPO, PPO, LoRA, Unsloth, DeepSeek, Kimi — hands-on tutorials with code)
- Document AI & OCR (Textract, DeepSeek-OCR, production extraction pipelines)
- RAG & agentic RAG (vector databases, context engineering, retrieval strategies)
- Future of SaaS & AI's impact on software businesses (valuation, IP, vibe coding, build vs. buy)
- AI infrastructure (specialized chips, cost optimization, serverless inference)
- Agentic frameworks compared (LangGraph, CrewAI, AG2, OpenAI Agents SDK)
- AI strategy for PE-backed and mid-market companies (readiness, ROI, data quality)
- Prompt engineering, guardrails, evaluation metrics, and production best practices

### Recent posts (2026)
- OpenClaw's Agentic Orchestrate Deep Dive (Mar 2026): https://softmaxdata.com/blog/openclaws-agentic-orchestrate-deep-dive/
- Harness Engineering (Mar 2026): https://softmaxdata.com/blog/harness-engineering/
- From PPO to GRPO to DAPO: Understanding RL for LLMs and Every Training Parameter Explained (Mar 2026): https://softmaxdata.com/blog/from-ppo-to-grpo-to-dapo-understanding-rl-for-llms-and-every-training-parameter-explained/
- We Open-Sourced Engram: A Brain-Inspired Context Database for AI Agents (Mar 2026): https://softmaxdata.com/blog/we-open-sourced-engram-a-brain-inspired-context-database-for-ai-agents/
- Is LLM's Context Window a Vanity Metric? (Mar 2026): https://softmaxdata.com/blog/is-llms-context-window-is-a-vanity-metric/
- How to Tune Your Own LLM with GRPO, Common Crawl and Unsloth (Mar 2026): https://softmaxdata.com/blog/how-to-tune-your-own-llm-with-grpo-common-crawl-and-unsloth/
- In the Era of Vibe Coding, Is Code Really a Valuable IP at Exit? (Mar 2026): https://softmaxdata.com/blog/in-the-era-of-vibe-coding-is-code-really-a-valuable-ip-when-exit/
- Specialized AI Chips and How They Will Change Our Industry (Mar 2026): https://softmaxdata.com/blog/specialized-integrated-ai-chips-and-how-it-will-change-our-industry/
- The Future of Private, Small, Specialized AI Models — Why They May Be the Real Moat (Mar 2026): https://softmaxdata.com/blog/the-future-of-private-small-specialized-ai-models-and-why-they-may-be-the-real-moat/
- Definitive Guide to Agentic Frameworks in 2026: LangGraph, CrewAI, AG2, OpenAI and More (Feb 2026): https://softmaxdata.com/blog/definitive-guide-to-agentic-frameworks-in-2026-langgraph-crewai-ag2-openai-and-more/
- Agentic Design Patterns — Technical Deep Dive (Feb 2026): https://softmaxdata.com/blog/agentic-design-pattern-comparisoneveryones-talking-about-ai-agents-right-now-and-honestly-its-getting-hard-to-keep-up-every-week-theres-a-new-framework-a-new-sdk-a-new-revolutionary-app/
- CNN vs Transformer Model Difference in Image Processing (Feb 2026): https://softmaxdata.com/blog/cnn-vs-transformer-model-difference-in-image-processing/
- Context Matters: The Biggest Lesson from ACE at ICLR 2026 (Feb 2026): https://softmaxdata.com/blog/the-biggest-lesson-from-ace-iclr-2026-the-power-of-agentic-engineering/
- AI Agents, RAG, MCP, Workflow Automation: What They Actually Mean (Feb 2026): https://softmaxdata.com/blog/beats-with-many-heads-ai-agents-rag-mcp-workflow-automation/
- Agent Swarm vs Anthropic Workflows vs LangGraph: Which Multi-Agent Architecture? (Feb 2026): https://softmaxdata.com/blog/agent-architectures-compared/
- How to Fine-Tune Kimi K2.5 on Your Local Machine (Feb 2026): https://softmaxdata.com/blog/how-to-fine-tune-kimi-k2-5-on-your-local-machine-a-practical-guide/
- How to Fine-Tune DeepSeek OCR V2 on Your Own PDFs (Feb 2026): https://softmaxdata.com/blog/how-to-fine-tune-deepseek-ocr-v2-on-your-own-pdfs-from-install-to-inference/
- Why DeepSeek-OCR 2 Could Change Document AI Forever (Feb 2026): https://softmaxdata.com/blog/why-deepseek-ocr-2-could-change-document-ai-forever/
- The Three Layers of Useful AI: Agents, MCP, and SKILL.md (Jan 2026): https://softmaxdata.com/blog/ai-agents-skills-and-mcp-what-business-leaders-actually-need-to-know-2/
- SaaS at a Junction Point: What We Learned Building AI in 2025 (Dec 2025): https://softmaxdata.com/blog/saas-at-a-junction-point-what-we-learned-building-ai-in-2025/

### Technical deep dives (earlier)
- Making Serverless Inference on SageMaker for HuggingFace Models (Feb 2024): https://softmaxdata.com/blog/using-custom-huggingface-models-on-aws-sagemaker-severless-inference/
- Keras LSTM Source Code Line-by-Line Explained (Apr 2020): https://softmaxdata.com/blog/keras-lstm/
- Using ML to Understand Inside Sales Emails (Feb 2020): https://softmaxdata.com/blog/what-are-inside-sales-emails/
- Applying ML in Sales Enablement Part 1-3 (2019-2020): https://softmaxdata.com/blog/applying-machine-learning-in-sales-enablement-and-sales-operations-part-1/
- CRM Cleaning with Salesforce SOAP API (Aug 2019): https://softmaxdata.com/blog/crm-cleaning-merging-salesforce-objects-using-soap-api-with-a-python-focus/

## Learn

### AI as a Business Function (AIBF) — Definitive Guide
The comprehensive guide to treating AI as a permanent business function rather than a one-off project. Covers the AIBF operating model, diagnostic signs your org needs it, and a practical playbook for standing it up.
URL: https://softmaxdata.com/learn/ai-as-a-business-function

### AI as a PE Operating Advantage — Why Every Fund Needs an AI Operating Partner
How private equity funds can create a fund-level AI operating capability that compounds across the portfolio. Makes the case for an AI operating partner alongside HR and finance partners, covers the central AI hub model, and provides a value creation playbook for portfolio-wide AI deployment.
URL: https://softmaxdata.com/learn/ai-for-private-equity

### AI Technical Debt — The Silent Tax on Every AI System
Why AI technical debt compounds faster than traditional software debt. Covers the four types of AI debt (data debt, model debt, prompt debt, organizational debt), how to run a debt audit, the real cost of ignoring it, a 7-step burndown playbook, and a prevention checklist for new projects.
URL: https://softmaxdata.com/learn/ai-technical-debt

### AI Concepts
Plain-English guides to AI concepts for non-technical audiences. Topics covered: AI Agents, RAG, MCP, Workflow Automation, Document AI, Fine-Tuning, Tokens, Context Windows, Agentic AI vs Generative AI, Self-Evolving Agents, Agent2Agent Protocol (A2A), Prompt Engineering, Context Engineering, Harness Engineering, and more.
URL: https://softmaxdata.com/learn

### CTO Whitepaper — Making AI Work in Production
A practical guide for CTOs on taking AI from pilot to production. Covers common failure modes, architecture patterns, build-vs-buy decisions, and how to measure ROI.
URL: https://softmaxdata.com/whitepaper/cto

## Start here

- Book a discovery call: https://softmaxdata.com/contact
- Services overview: https://softmaxdata.com/services
- Success stories: https://softmaxdata.com/success-stories
- FAQ: https://softmaxdata.com/faq
- Try document extraction demo: https://softmaxdata.com/demo/document-ai
- AI opportunity assessment: https://softmaxdata.com/demo/ai-assessment

## Contact

Jia Chen, Founder & CEO
Email: marketing@softmaxdata.com
Website: https://softmaxdata.com
Blog: https://softmaxdata.com/blog