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
![Distilling an Aligned Jev System into a Classifier You Own]()
Distilling an Aligned Jev System into a Classifier You Own
2026-09-21A Jev scorer aligned with 140 human labels taught a 66M-parameter DistilBERT that scored 0.912 against the human label, two points above its teacher, at 5.6 to 15 ms an item. The accuracy is the least general part; the per-slice ship gate is the part to copy.![Fine-Tuning Jev: You Can't. Here's What Gets You the Same Effect]()
Fine-Tuning Jev: You Can't. Here's What Gets You the Same Effect
2026-09-21You can't fine-tune Jev: TypeSafe serves the same weights to everyone. So we kept it frozen and adapted the questions and a small fitted head around it. On 140 labels, one new plain-English question bought 10 points of accuracy, and it finds the hidden pattern about a quarter of the time.![Jev vs Laya: Same Labels, Same Questions, One Variable]()
Jev vs Laya: Same Labels, Same Questions, One Variable
2026-09-21We ran Jev and the open-weights Laya through one harness where only the engine changes. Jev led by 4.7 points alone and 6.8 with our feedback layer, and then fine-tuning Laya on the same 140 labels beat both.![Can You Trust Jev's Confidence?]()
Can You Trust Jev's Confidence?
2026-09-19Jev's confidence values are useful: higher really does mean more likely right. But on 8,801 labeled examples they ran overconfident, and how you phrase the request changes the pattern. Calibration fixes most of it.
