Autonomous systems that execute workflows and answer hard questions
Built by the team behind Engram, the open-source memory layer for AI agents. We don't just integrate agent frameworks—we build the infrastructure that makes agents reliable in production.
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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 companies choose Softmax for agentic AI
We're not a tool installation shop. We build custom agent systems with deep business context—and we've been doing it since before "agentic" was a buzzword.
Agents running real businesses, today
Not demos—production systems that companies depend on daily.
Our agents run Flywheel's cross-client reporting (60 hrs/week saved) and power agentic analytics for a premium retailer (avoided a $3.5M loss). These aren't prototypes—they're systems that business operations depend on every day.
We build the infrastructure layer, not just the agents
Engram, our open-source memory layer, powers the agents we ship.
Most consultancies assemble pre-built pieces. We go deeper—we built and open-sourced Engram, a persistent context database that gives agents memory across sessions, models, and teams. When you hire Softmax, you're hiring the team that understood the problem deeply enough to build the infrastructure and give it away for free.
Fluent in the hard parts of agentic AI
Orchestration, context engineering, security, observability—not just prompting.
Building a reliable agent means solving orchestration across multi-step chains, managing context windows that don't silently degrade, locking down tool permissions so agents stay in bounds, and tracing every decision for auditability. We've worked through these problems across dozens of production deployments—not in theory, in shipped systems.
We build context, not wrappers
Your business logic encoded 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 because they understand your domain—not just general patterns. That's context engineering applied to your specific problem.
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
We didn't just adopt agent frameworks. We built the missing piece.
Every agent framework has the same blind spot: agents forget everything between sessions. After hitting this wall on client projects, we built Engram — an open-source context database that gives agents persistent, cross-model memory. It stores knowledge as atomic concepts, learns from outcomes, and works across Claude, GPT, Gemini, or any LLM.
When you hire Softmax for agent work, you're hiring the people who understood the problem deeply enough to build the infrastructure layer — and then gave it away for free. That's the level of depth we bring to every engagement.
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.
Built by us, open for everyone. Engram, our open-source context database for AI agents, powers persistent memory in the agent systems we build.
Learn more →See how we build AI agents for SaaS products →
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
Not sure where to start? Run a free AI opportunity scan →