A quarter billion dollars in revenue processed at scale, at nearly 100% uptime. 180 billion tokens of production LLM workload. AI in production, not just in demos.
What We Do
![Knowledge bases with learned ontologies and taxonomies]()
Knowledge Bases
Your agents are only as good as what they can look up. We build knowledge bases with ontologies and taxonomies that learn from your data, refining their own structure instead of going stale the week after someone hand-built them.
![Unattended automation with a human in the loop]()
Self-Aligning Automation
Unattended business process automation with a human in the loop. Reviewers correct it and say why; the system turns the explanation into a stated policy and applies it from then on.
![Agent systems, tools, and orchestration]()
Agent Systems
A model is half a system. The other half is the harness: the tools it can reach, the procedures it follows, the limits it runs inside, and the ability to work for hours without losing the plot.
![Custom and fine-tuned machine learning models]()
Machine Learning
Custom classifiers, fine-tuned models, and calibrated confidence that tells you which decisions to trust and which to escalate. We find the cheapest model that clears your bar, prove that it clears it, and run it in production on AWS—training, serving, and evaluation included.
Clients usually arrive asking about one of these. The work rarely stays in one box.
Our Approach: Cybernetic Development
AI is an engine for generating code. The differentiator is the governor: the constraints, feedback loops, and judgment that keep systems reliable in production.
Modern failures increasingly look less like isolated “bugs” and more like operational, multi-system breakdowns. Great unit tests help—but they don’t cover every emergent scenario. So we build layered defenses and close the loop with real-world feedback.
- Specs first: define behavior before implementation.
- Defense in depth: sandboxed tools, CI gates, staged rollouts, and fast rollback.
- Operational feedback: telemetry and incident-driven regressions that tighten the loop over time.
- Simplify and delete: reduce degrees of freedom to eliminate entire classes of failure.
The Anthus Platform
Solve complex business problems with AI and ML using a proven, reusable technology stack that grew out of real delivery work — runtime, agent execution, knowledge, observability, and media, with the enterprise controls that matter in production.
B0rd — desk displays for agent monitoring
![B0rd LED matrix desk display]()
Glanceable signal when agents run all day
Anthus Microelectronics grew out of the same workflow problem: when coding agents run for hours, the bottleneck moves to monitoring and steering them. B0rd is a standalone LED-matrix desk display — launch countdowns, agent status, notifications, an idle clock — readable from across the room. Handbuilt hardware running a handbuilt (AI-assisted) OS. Matching units stay in sync without pairing or a hub.
- Standalone appliance — browser setup, no app store
- Glanceable cues for long-running agent sessions
- In sync by design across matching units
Case studies
Call Criteria
100% of calls reviewed, up from a sample. Call Criteria's human QA couldn't scale without scaling headcount, so we built a self-evolving RLHF system: reviewers correct the AI and say why, and the system turns the explanation into policy it applies from then on.
Venue Driver
16 years of continuous operation across Las Vegas nightlife. When an AWS data center failed catastrophically, we relocated the entire system within hours — ticket scanning at the nightclubs never stopped.
See our solution patterns.
Recent Articles
![Grok Bot Gave My Coding Agents a Boss]()
Grok Bot Gave My Coding Agents a Boss
2026-08-29T22:47:00-04:00Grok Bot rapidly became the control room for my army of bots: filing bugs, dispatching parallel Cursor workers, rejecting weak work, and returning fixes for acceptance testing—with less help from me than I expected.![From pair programmer to executive]()
From pair programmer to executive
2026-08-29T10:30:00-04:00Coding agents became cheaper and better at long-running work at the same time. The next step is not another pair programmer. It is a software organization with reporting lines, review gates, and a human setting direction from above the day-to-day loop.![Bugonomics: The Flip Side of Cheap Coding]()
Bugonomics: The Flip Side of Cheap Coding
2026-08-29When breaking software gets as cheap as writing it, cheaper offense means more offense. The same price-performance curve that turned a $200 coding habit into a $20 one is turning million-dollar exploit chains into something approaching commodity.![The Content Paradox: Why Making AI Writing Easier Makes the Internet Worse]()
The Content Paradox: Why Making AI Writing Easier Makes the Internet Worse
2026-08-28When you make content creation 100x cheaper, you don't get 100x better information—you get 100x more noise. The economic logic of Jevons Paradox explains why AI is drowning the internet in synthetic mediocrity.
