P34 Enterprise

Deploy AI decision infrastructure across your business

P34 sits between your operational telemetry and your executable actions: a governed decision layer that evaluates every candidate action, refuses the weak ones, and sizes the rest into portfolios your team — or your agents — execute under controls.

Design a pilot Book a technical discovery call

Serious, operational and measurable. Same P34 core as the membership — the value proposition here is controlled deployment.

The decision layer

Business telemetryOrders, prices, inventory, fulfillment, outcomes — the signal that substitutes for missing price discovery

Business menu + contextCandidate actions with quantities, timing, constraints — grounded into the P34 schema

P34 decision layerUniverse → false-positive control → portfolio / no-trade

Controlled executionCaps, review thresholds, approvals, reconciliation

Use-case families

Where enterprises deploy P34

Retail / wholesale

Purchase-order and shipping portfolios from live deal menus. Validated in production at a reseller operating at roughly $100M in annual sales.

Credit-like decisioning

Loan approvals, card limit increases, overdraft offers — where applicable and after market-specific compliance review. ~3,000 loans issued in a live model test.

Capacity allocation

GPU hours, electricity, freight capacity, construction materials — timing and allocation decisions where operations drive economic value.

Logistics / freight

Load acceptance, routing and bidding across candidate menus with per-lane economics and conversion feedback.

Onboarding & incentives

Customer onboarding offers and incentive portfolios scored on lifetime economics rather than conversion alone.

Private / alternative assets

Manager underwriting, receivables, specialty insurance workflows — approved computable markets under separate review.

Deployment path

Rollout in five governed stages

P34 is not positioned as an uncontrolled trading bot. Allocation increases only after evidence of calibration, downside control and operational reliability.

01Menu groundingTranslate business operations into the P34 menu schema — including the deals you declined.

02Context trainingBuild market-and-business context from historical menus and realized outcomes.

03Shadow testRun P34 against live menus without executing its trades. Compare, measure, argue with it.

04Limited pilotExecute only capped, reviewed or low-risk portfolios with full reconciliation.

05Scale with controlsIncrease allocation with caps, drawdown limits, data-quality checks and audit.

Controls

Governance is part of the architecture, not an afterthought

Every deployment ships with an explicit control envelope. The model's own discipline — no-trade as a first-class output, false-positive control — is wrapped in operational governance your risk team defines.

Portfolio caps Review thresholds Drawdown limits Data-quality checks Audit logs Allowed-market lists Drift monitoring Kill switches Human approval points Market-specific compliance

Current release limitations — stated plainly

P34 does not yet automatically prove a market is computable — that's what the fit assessment and shadow stage are for.
Your business must have had more options historically than it chose — policy-selection signal is required.
Latency runs from under a minute to hours; very high-frequency use is out of scope.
Generalization across all markets is not fully known; pilots proceed through shadow testing and controlled deployment.

Integration

Meets your stack where it is

APIs

Async REST at api.hyperc.com/v1 — fit, poll, portfolio. JSON or Parquet on the wire; a pytest workflow for CI.

Data connectors

Menus and sales tapes from your ERP, marketplace, LMS or warehouse exports — the MSD data contract is deliberately minimal.

Agent frameworks

P34 primitives as tools for Claude, ChatGPT or your in-house agents — decisions stay model-grade while orchestration stays yours.

Human approval

Approval queues, four-eyes sign-off and reconciliation hooks at every execution boundary you keep manual.

Evidence

What we can show before you commit

Live deployment

~$100M

annual-sales reseller operated with limited supervision

Amazon wholesale: thousands of signals ingested, purchase and shipping orders generated in production. Company-reported.

Synthetic stress test

+$14.8k vs −$219.8k

P34 vs tuned conventional baseline, difficult benchmark

Controlled synthetic market with selection bias and regime change. In the easy stationary benchmark P34 preserved upside (+$228.6k vs +$227.1k). Methodology in the technical report.

Published methodology

Falsifiable

working paper, data contract, benchmark protocol

The Computable Markets working paper defines the MSD data contract and an explicit falsification protocol. Bring your quant team.

Research & evidence → Industry case studies →