Axiom AI: Multi-Agent Orchestration Platform
By Colter Mahlum, Founder & CEO of Mahlum Innovations — published 2026-04-01.
Colter Mahlum architected and shipped this case study end-to-end. Mechanical engineer turned AI builder, he has personally led 11+ production AI deployments across manufacturing, healthcare, finance, and consumer apps from Bigfork, Montana.
Building a Multi-Agent AI Platform That Replaces 4–6 Subscriptions Per User
App: Axiom AI | Industry: AI Platform / SaaS | Client: Axiom AI (horizontal SaaS for AI-forward developers and ops teams) | Company size: Indie founders, technical teams, small-to-mid tech companies | Duration: ~4-month intensive build, continuous iteration
Summary
A multi-agent AI orchestration platform that lets users deploy, coordinate, and monitor swarms of specialized Claude/GPT/Gemini agents from a single command center to autonomously execute complex software, research, and operational workflows.
The Challenge
Single-agent AI tools (ChatGPT, Claude.ai, Cursor) hit a ceiling fast — they can't run long autonomous workflows, can't coordinate specialists across domains, can't execute on the user's actual machines, and can't recover from their own mistakes. Teams were stitching together brittle scripts, multiple subscriptions, and constant babysitting to get real work done.
Our Approach
- Designed a custom Observe → Think → Act → Evaluate orchestration loop with dynamic agent spawning, AI-powered task grading, and time-travel debugging
- Built model-routing layer supporting Claude (Sonnet/Opus), GPT-5, Gemini, and Codex with intelligent auto/manual/hybrid selection per agent
- Added local LLM support (Ollama, LM Studio, vLLM, llama.cpp) for private/on-prem inference
- Created Jarvis personal assistant integrating 76+ external services (Gmail, Slack, GitHub, Stripe, calendars) via AES-256-GCM encrypted credential vault
- Implemented secure WebSocket bridge for remote execution on connected desktops/mobiles (shell, git, docker, deploys, file ops)
- Shipped native desktop client + iOS companion app (Expo React Native) for monitoring on the go
Outcomes & ROI
- 76+ — Service Integrations: Out-of-the-box connections to Gmail, Slack, GitHub, Stripe, calendars, and 70+ more via Jarvis
- 50 — Sequential Agent Steps: Autonomous workflows run up to 50 steps without human intervention vs. ~1–3 turns in stock chat tools
- 4–6 — Subscriptions Replaced: Replaces Claude Pro, ChatGPT Plus, Cursor, and similar tools per power user
Cuts multi-step engineering tasks (e.g. 'scaffold + deploy a feature') from hours of manual prompt-shepherding to a single instruction, while consolidating 4–6 separate AI subscriptions into one platform.
Technologies Used
Anthropic Claude, OpenAI GPT-5, Google Gemini, Local LLMs (Ollama/vLLM), WebSockets, PostgreSQL + Drizzle, Expo React Native, Stripe Subscriptions
Key Takeaways
- Multi-agent orchestration unlocks workflows single-agent tools physically can't reach — but only when paired with disciplined evaluation gates between steps
- Letting users choose between cloud and local models is now table stakes for serious AI tooling
- An encrypted credential vault is the difference between an interesting demo and a tool people actually trust with their accounts
Frequently Asked Questions
What makes a multi-agent platform different from ChatGPT or Claude?
Multi-agent platforms coordinate specialized agents across domains (frontend, backend, research, ops) and run long autonomous loops. Stock chat tools max out at 1–3 turns of meaningful work; orchestration platforms like Axiom can run 50+ sequential steps with self-evaluation between each.
Can it run on private models for sensitive workloads?
Yes — Axiom integrates with Ollama, LM Studio, vLLM, and llama.cpp so teams with regulatory or IP concerns can route sensitive agents to on-prem inference while still using cloud models for general tasks.
How long did this build take?
Roughly 4 months of intensive solo + AI-assisted development on Replit, with continuous iteration into public launch in April 2026.
Related Services
About the Builder
Mechanical engineer turned AI builder. Colter personally architected and shipped Axiom AI end-to-end — strategy, model development, and the production MLOps work — alongside 10+ other AI systems across manufacturing, wealth management, healthcare, and consumer apps. Read full bio · LinkedIn.
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