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Practice02

AI that does real work.

Voice agents that answer real customers, machine learning proven under competition pressure, and LLM systems woven into production workflows — built, hosted, and verified by us.

What we have built

Three systems, running.

Each of these is ours: we designed it, we host it, and we are on the hook when it misbehaves.

Producttworing.ai

TwoRing & Switchboard

A 24/7 AI receptionist for trade businesses — and the self-hosted voice engine we are building underneath it: LiveKit telephony, provider failover, full call recording.

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Competition0.937 public AUC

The Knee Ledger

Competition machine learning on knee MRI, run as an evidence ledger: every experiment declares its pass-bar before it runs, and roughly forty that failed are written down beside the ones that worked.

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SystemsML + risk engineering

AllSpark

An autonomous algorithmic trading platform: ML signal generation behind hard risk gates, live market data, and monitoring that assumes things go wrong.

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How we do AI

Three rules we do not bend.

01

AI does the work, a human signs it

Every AI system we ship has a practitioner accountable for its output. On consulting engagements that means AI on the first pass — variance triage, reconciliation, document parsing — and a named person verifying every result.

02

Nothing depends on one vendor

Speech, language and voice each sit behind an adapter that can fail over mid-request, with a local model underneath the cloud one. What that costs to build, and why we pay it, is on the TwoRing page.

03

Proven where the leaderboard keeps score

We test our methods in open competition, where a wrong claim costs rank. The discipline that survives there — gated experiments, receipts for every claim — is the discipline we bring to client work.

Next step

Have a job for a machine?

Tell us the work you want off a human's plate. We will tell you honestly whether AI can do it — and stand behind it if we build it.

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