A quarter billion dollars in revenue processed at scale, at nearly 100% uptime. 180 billion tokens of production LLM workload. We don't talk about AI—we operate it.
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.
Our Mission
Deliver reliable, secure, and efficient business solutions using collaboration between human and artificial intelligence in every aspect of development and operations. We build systems that keep working when nobody is watching.
Our Values
Prioritize Solutions Over Tools
Investing in products and services only delivers business value if you're in the business of products and services. We're in the business of solutions.
Design for Humans
Computers exist to help humans accomplish things. Not the other way around. Make it easy for the human, not the computer.
Focus on Business Logic
The only code you should be writing is the business logic that solves real problems. Don't waste time reinventing wheels.
Continuously Improve
Enable rapid, iterative change through CI/CD and DevOps—then let the systems improve themselves. Self-optimizing classifiers and self-steering agent systems get better from production feedback without waiting for an engineer to notice.
Collaborate with AI Humanely
The most scarce and valuable resource is human time and attention. Leveraging artificial people allows us to scale that attention without burning out real people.
Implement Infrastructure as Code
Leverage DevOps to implement Infrastructure as Code, and MLOps and LLMOps to do the same for the models. Every part of a production system—including training runs and evaluations—should be created by code so it's reproducible, not clicked together by hand.
Commodify AI Models
Treat AI models as replaceable, not magic black boxes. In a world with no moats, don't invest too much in any given castle.
Optimize Resource Usage
Balance efficiency with cost-effectiveness. When intelligence is cheap, the goal shifts from conserving compute to conserving context and cognitive load.
The Anthus Platform
Solve complex business problems with AI and ML using a proven, reusable technology stack. We provide interoperable building blocks: Korporus hosts the application surface, Tactusdefines durable procedures, Kanbus coordinates workflow state, Plexus governs evaluation and MLOps, Biblicus and Virtuus ground systems in inspectable data,Caducus adds operational visibility, and Babulus extends the same code-first philosophy into content and video output.
Plexus
Plexus is the Anthus MLOps platform and agent incubator: the system that evaluates, deploys, and continuously improves AI agents with human feedback in the loop.
Korporus
Korporus is the Anthus application shell and AI-agent operating environment: a federated, containerized host for platform apps, shared UX chrome, and modular web interfaces.
Tactus
Tactus is a programming language and runtime for durable AI-agent procedures, with sandboxing, built-in BDD specifications, checkpointing, and human-in-the-loop controls as first-class concepts.
Kanbus
Kanbus is a project-management system for orchestrating AI-agent workloads with git-backed issues, wiki-style planning, and durable task memory.
Biblicus
Biblicus is the Anthus corpus-analysis platform for extraction, retrieval, and evidence-first knowledge workflows over real document collections.
Virtuus
Virtuus is the Anthus structured-data layer: a filesystem-native virtual table engine for JSON records with just-in-time indexing and fast query access after load.
Caducus
Caducus is an application-health and operator-assistance system that turns noisy operational events into prioritized issue patterns, alerts, and level-zero triage support.
Babulus
Babulus is an AI-agent-driven marketing automation system that treats video production as code, with narration-first workflows, generated assets, and deterministic previews.
VideoML
VideoML is the Anthus video pipeline for representing video as code, generating script and timing artifacts, and rendering automatable media workflows.
Grounded research to video output
Biblicus + Tactus + Babulus + Korporus
Use Biblicus to manage the source corpus, Tactus to define the repeatable procedure, Babulus to generate the narrative output, and Korporus to host the resulting service as a coherent application.
Production agent service with operational discipline
Tactus + Plexus + Korporus + Caducus
Define the agent behavior in Tactus, evaluate and improve it through Plexus, run it inside Korporus, and monitor it through Caducus so the result behaves like a service instead of a demo.
Workflow-heavy human and AI collaboration
Kanbus + Tactus + Plexus
Keep task memory and work orchestration durable in Kanbus, drive execution through Tactus procedures, and feed the resulting evaluation and feedback loops back into Plexus.
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
Featured Solutions
![B0rd]()
B0rd
2026 - ongoingB0rd is a standalone LED-matrix desk display built by Anthus Microelectronics, a spinoff venture rooted in Anthus's ubiquitous use of AI agents and the constant all-day need to monitor their progress and steer them. It puts glanceable information — launch countdowns, agent status, notifications, an idle clock — on the wall or desk without a screen you have to sit in front of.
- Handbuilt microelectronics with a custom handbuilt (AI-assisted) OS
- Standalone appliance: setup over Wi‑Fi, no app store, no phone required
- Glanceable cues for long-running agent sessions — walk away from the desk and still know
- In sync by design: matching units stay in step with no pairing or hub
![Speaker Role Classifier]()
Speaker Role Classifier
November 2025A real-world example of how the agentic programming paradigm enables rapid solution development. We faced a problem where ambiguity in speaker role names could have made a traditional programmatic solution complex and time-consuming. By giving an AI agent the right tools, we delivered a working solution quickly and moved on—demonstrating how agent-based programming enables much greater agility and speed. View the project on GitHub.
![Classification with Confidence]()
Classification with Confidence
September 2025A practical exploration of LLM fine-tuning for text classification. By fine-tuning GPT-4o-mini, we significantly improved classification accuracy and alignment to specific business requirements. The project demonstrates a production-ready approach to model alignment with confidence scoring that determines when human review is needed—a practical application of human-in-the-loop principles that balances automation with expert oversight. View the project on GitHub.
![SQLBot: Your AI Database Analyst]()
SQLBot: Your AI Database Analyst
September 2025SQLBot is a new kind of interface for your database. Instead of writing SQL queries yourself, you delegate high-level analytical tasks to an AI agent. It reasons through your request, executing a chain of queries and analyzing the results until it arrives at a complete answer—all while keeping your data safe with built-in safeguards. View the project on GitHub.
View all our Solutions.
Recent Articles
![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.![Maximize Value, Not Intelligence]()
Maximize Value, Not Intelligence
2026-08-22In 2023 we argued for using the dumbest model the problem will bear. In 2026 there are public coding-value leaderboards, the cheap tier got genuinely good, and the interesting question changed: not which model to buy, but which jobs just became worth automating.![The Year Coding Became a Commodity]()
The Year Coding Became a Commodity
2026-08-15What happens when AI coders that are better than humans become a cheap commodity? Since 'vibe coding' was coined in 2025, that same capability has gotten about 10× cheaper — and the price drop coincided with agents learning to run autonomously for hours. The two effects multiply, and the bottleneck moved from implementation to specification.![Cybernetic Development]()
Cybernetic Development
2026-05-19Vibe Coding is the spark, but cybernetic development is the fire. Why the future isn't writing code—it's governing AI agents with systems thinking.
Please see our Articles for more.
