Agents that do the work. Not demos that describe it.
The gap between an impressive demo and a system that survives eighteen months in production is enormous, and it is almost entirely made of the unglamorous parts: evaluation, provenance, refusal behaviour, and the honesty to escalate to a human. That gap is where we live.
- AI systems in prod
- 27
- Unsourced claims
- Zero
- Voice latency
- <400ms
- Eval coverage
- Day one
Nine systems we take all the way to production.
AI Agents
Autonomous systems wired into your real tools — with permissions, audit trails and a stop button.
Planning · Tool use · Human handoff
Generative AI
Text, image, audio and video generation, evaluated against your brand rather than a benchmark.
Multimodal · Brand-tuned · Evaluated
Machine Learning
Forecasting, ranking and anomaly detection on your data, with the drift monitor built in on day one.
Forecasting · Ranking · Drift alerts
Conversational AI
Assistants that resolve the request instead of deflecting it, and escalate cleanly when they can't.
RAG · Memory · Escalation
Voice AI
Sub-400ms speech pipelines with barge-in — conversations that don't feel like waiting.
Realtime · Barge-in · 40+ languages
Computer Vision
Detection, segmentation and inspection at the edge, running on the hardware you already own.
Edge inference · Inspection · OCR
Intelligent Automation
The unglamorous middle of your business, automated end to end — and observable when it breaks.
Workflows · Integrations · Observability
Business Intelligence
Natural-language analytics over your warehouse, where every number can be traced back to its query.
Text-to-SQL · Semantic layer · Provenance
Applied Research
For the problems without a paper yet. We prototype, measure honestly, and tell you when to stop.
Prototyping · Evals · Honest no
The most valuable thing an AI system does is refuse.
Every team benchmarks what their model can do. Almost nobody benchmarks what it does when it shouldn't — and that's the behaviour that decides whether the system survives contact with a regulator, a customer, or a bad day.
A model that answers every question is not confident. It is uncalibrated. On Helios Orbit, the behaviour that convinced a bank's compliance team wasn't retrieval quality or reasoning depth — it was the refusal path. Below a confidence floor, the system escalates to a human instead of producing a fluent, plausible, wrong number.
Read the full argumentWhat we ship with every system
Span-level provenance
Every claim traces to the exact source span that produced it.
Calibrated confidence
The model knows what it doesn't know — and says so.
Evaluation harness
Regression suites that run on every prompt change, not quarterly.
Human escalation
A defined path out, with the context attached.
Drift monitoring
Alerts when the world moves and the model doesn't.
- 0 mo
- In production
- 0
- Unsourced claims
- 0 yr
- Corpus indexed