Briefing
TCO calculator
Corpus size, training cadence and concurrency against three-year hardware, cloud and hybrid totals. Walked through in the briefing against your estate.
OpenPillar 2 · Compound · Layer · Improve · Carry forward
Build AI that accumulates on your side of the boundary — without buying a hyperscaler.
You want your AI to learn your deal-flow, your product, your customers, your voice. Hyperscalers won't do that — and even if they would, you can't justify the GPU spend against an uncertain business case. Today your options are: rent generic AI forever, or build your own and fail.
Evidence
Every claim with a proof.
Every item below is a live demo on this site, a mechanism you can inspect in the product, or an artefact that arrives under NDA. Nothing here is a mock-up.
Interactive
Compounding-curve chart
Scrub the timeline and see the order in which retrieval, evaluation gates, preference data and adapters accumulate on your side of the boundary.
OpenLive mechanism
Adapter distribution, eval-gated
How an externally trained adapter is registered, scored against your baseline, signed and distributed to the appliance — and blocked from promotion if it regresses.
OpenCode
Decision-journal data model
How the reinforcement loop accumulates preference + outcome data over years — without anything leaving your appliance.
OpenBriefing
Five-year asset trajectory
What year five of accumulation looks like, and an honest account of when owned adapters start to differ measurably from a generic model with retrieval.
OpenHow it works
The architectural choices behind the promise.
The sequence runs on your infrastructure. First, retrieval over your corpus. Then an evaluation harness that scores outputs against your preferences and gates every model change. Then adapters trained on the data you have curated, distributed and eval-gated through the same signed pipeline. Preference and outcome data accumulate over time. The pipeline is portable — when a stronger base model ships, your adapters re-fit onto it.
Read the full system viewWhat it costs
Pricing
Compounding is a function of compute spent on training. Tier pricing scales with hardware footprint. Cloud-burst training is available where data residency permits. Talk to the discovery agent for a sized recommendation.