Autonomous systems that execute workflows and answer hard questions
LLM-powered agents that call tools, pull from multiple data sources, run multi-step analysis, and take action—with human oversight where it matters.
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What an agentic solution of ours actually looks like
Live agent trace from a client deployment — showing dynamic tool creation, multi-step reasoning, and full traceability.
Why Softmax for AI Agents
We're not a tool installation shop. We build custom AI agent systems with deep business context—and we've been doing it for 7 years.
We build context, not wrappers
We encode your business logic into agents that outperform generic tools.
We interview your staff, understand your business model, and encode the subtle details into a business context layer. Our agents outperform generic tools like Databricks AI or Claude Cowork because they understand your domain—not just general patterns.
Agents that call tools—and create them
Not just pre-built integrations—dynamic tooling when needed.
Our agents don't just use pre-built integrations. They call external tools such as AWS SageMaker, SalesForce; they also dynamically create their own tooling when needed. That's the difference between a demo and a production system.
Real AI engineers, not YouTube graduates
LangGraph, CrewAI, AutoGen—we know the trade-offs.
We're a team of AI/ML engineers and data scientists who've shipped systems to production for 7 years—not consultants who discovered AI last year. We build with LangGraph, CrewAI, and AutoGen because we understand the trade-offs, not because they're trending.
Autonomous systems that reason, act, and iterate
An agent isn't a prompt wrapper or a chatbot. It's an LLM-powered system that calls tools, executes workflows, and completes tasks with minimal human intervention.
Tool Calling
Query databases, call APIs, execute code, read files—agents use tools to get real work done
Multi-Step Reasoning
Break complex tasks into steps, iterate based on results, and adapt when things don't go as planned
Multi-Source Integration
Pull from warehouse, CRM, spreadsheets, APIs, and documents—not just one system
Human Oversight
Guardrails, approval gates, and audit trails—so agents stay within bounds
What We've Built
Questions that used to take weeks
Deep analytics agents
Business questions that required analysts pulling data, building models, and iterating for weeks—now answered instantly.
The agent reasons through large data volumes, asks its own follow-ups, runs intermediate analysis, and produces answers that hold up to scrutiny.
Retailer using agent for inventory optimization and acquisition analysis—shortened analytics cycles from 2 weeks to instant, avoided a $3MM bad acquisition.
Work that varies but needs consistent results
Variable input, consistent output
Data sources change client-to-client, format-to-format—but the output quality has to be consistent.
The agent reasons through the variation instead of breaking on edge cases. Humans review the output, not every step.
Flywheel's multi-source reporting across different client data setups—12% productivity gain, ability to take on more clients, consistent service quality.
Documents that don't fit templates
Intelligent document processing
Insurance intake forms, appraisal reports, manufacturing docs—every document is different.
But you need consistent data extraction and entry. The agent handles extraction, validation, and workflow routing—not just OCR.
Insurance workflows with highly variable document formats, consistent data entry into downstream systems.
Where we've shipped AI Agents
Built an agent workflow for multi-source reporting with review built in.
Read case study →Large Outdoor Retail Chain (Confidential)
Built agentic analytics for operational questions like inventory cost reduction, using transaction-level data plus business context.
Case study incomingReferences and deeper examples available on request.
What we ship
A production-ready agentic system your team can run, evaluate, and own.
Both include:
How we work
Strategy
Pick the highest-ROI agent job, define success, and identify constraints and data sources.
Build
Ship a working thin-slice quickly, then iterate with evidence: evaluation questions, trace logs, and real user feedback.
Launch
Deploy, monitor, and make it ownable.
Start here
Book a discovery call and we'll confirm:
- The best first agent job (highest ROI)
- What context/data is needed
- The safest path to production
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