The OS that catches
what 95% miss.
An operating system, not a dashboard. ~100 specialized agents across nine named detection products. We sell the OS that runs our company.
Asset SG-04 reported 78% earned value, but procurement data shows 12% real progress. Pattern matches EVM-PPC divergence threshold.
F&B retrofit on Asset HK-12 triggers code-upgrade obligation. Caught Day 0 vs industry mean Day 47.
14.3% verified energy savings on Asset MY-02 baseline. ASHRAE Guideline 14 compliance confirmed.
RFI velocity + meeting-minute pattern + schedule slip on Asset TW-08 indicates claim filing risk in 47 days.
Median payback for production-grade agentic deployments: 6.7–9 months. Sources: CrewAI Feb 2026 enterprise survey; JPMorgan 450+ agentic use cases (Q1 earnings call); Walmart $4.2B/yr waste-reduction agentic AI program (April 2026 disclosure); Anthropic / Model Context Protocol April 2026 ecosystem update. How AISB verifies AI-building savings against this benchmark →
The OS, working in real time.
What the OS actually catches.
Not a dashboard. Not a chatbot. Nine named failure modes with verifiable signal-to-fire and citation chains. Three shown below; six more on the detections page.
Asset SG-04 · 78% reported / 12% real procurement. Divergence > threshold.
EVM Theater Detection
Catches owners reading green status that procurement data contradicts. 100% precision on a 100-project synthetic corpus.
See the methodologyAsset TW-08 · RFI velocity + minutes pattern + slip = 47-day filing risk.
Claims Early Warning
Detects contractor claim risk forty-seven days before filing. Zero competitor in the category.
See the methodology47 admitted, 12 rejected. Source authority + standards anchor + numeric specificity + corroboration + contradiction check.
5-Signal Claim Classifier
The unsolved-field-wide gap, closed. Daily anti-collapse audit across all eight production squads.
See the methodologyWhy 95% of CRE AI pilots disappoint.
Become one of fifteen.
Six months. No NDA. No contract. Feedback for credit. Selection within fourteen days.
How we differ from BMS engines
The protocol layer is a commodity. The moat moved up.
Open-protocol BMS is now table stakes. CONTEXUS ships 13+ production modules on open APIs. OpenRemote is open-source end-to-end. Schneider EcoStruxure opened its protocol layer in April 2026. JCI/Nantum validates the category but locks back into single-vendor stack. The proprietary-protocol moat that BMS engines rented for 20 years is closing fast.
BEAST OS sits as the layer above the protocol stack — where the durable moat actually lives in 2026.
PRIVACY BROKER · v84
Differential privacy + jurisdictional consent
ε-budget enforcement, k-anonymity floor, GDPR / BIPA / PDPA / Colorado biometric / SG PDPA / EU AI Act — the only fused-occupancy product an enterprise legal team can sign off on. Ask →
CLAIM CLASSIFIER · v85
Verified knowledge, not vibes
5-signal admission protocol — source authority, standards anchor (ASHRAE / IPMVP / IBC), numeric specificity, cross-source corroboration, contradiction check. The unsolved-field gap, closed.
RETROFIT COMPLIANCE SCAN · v73
Code-triggered upgrade detection
SG CORENET X (Oct 2025), NYC LL97, HK BD, JP BSL, AU NCC — we surface upgrade obligations a touch-the-building project triggers, before permit submission stalls.
If your AI building vendor's differentiation is the protocol, the moat is on the wrong side of the table. Open BMS is the floor — not the ceiling. Read the full thesis →
The 88% Pilot Failure Trap — And The Three Axes Where AISB Closes The Gap
Deloitte and Schneider Sustainability Research both published in 2026 the same finding: 88% of enterprise AI pilots fail to reach production. The failure is not random. It decomposes into three measurable axes — eval gaps, governance, and reliability. Each axis maps to a named AISB platform component with a public spec link. This is the auditable answer to a buyer's first defensive question.
64% — Eval Gaps
Failure mode: Pilot output looks correct in demos but cannot be validated against held-out tests, golden cases, or production-grade benchmarks. Buyer cannot answer "how do we know it is right today, tomorrow, next quarter?"
AISB component: v82 Daily Squad Self-Test Loop — every CRE squad runs an autonomous 18-test Standard Suite at 06:30 TPE daily, with US plus APAC jurisdictional coverage and golden-corpus regression detection. Failures route to fixes, not to silence.
Reinforced by: v42 Coherence Loop (every upgrade must pass 5 gates before landing); v76 Recurrent Reasoning Protocol (every iterative loop has measurable halt criteria).
Source: Deloitte 2026 enterprise AI maturity study; Schneider Sustainability Research 2026 (21% maturity gap).
57% — Governance
Failure mode: Pilot recommendations cannot be traced to source, cannot be reviewed by domain experts, and cannot be blocked when wrong. Procurement and legal teams have no insertion point.
AISB component: Expert Council pipeline (Harper for research and fact-check; Benjamin for math and logic verification; Lucas for narrative and blind-spot detection). Mandatory gate on every high-stakes output. Pessimism Gate v80 defaults BLOCK on FIN trade proposals, DIS recommendations, CRE-TS engineering, and public-publish — affirmative evidence required to PASS.
Reinforced by: v65 Adversarial Ship Gate (4-phase intelligent pipeline with graduated BLOCK/ADVISE/PASS verdicts); Plan Verification Rule v69 (current-state claims must be verified in-session, never written from memory).
Source: Deloitte 2026 governance-gap analysis; agentic-AI governance literature consensus 2026.
51% — Reliability
Failure mode: Pilot runs fine for two weeks and degrades silently. No drift detection, no provenance audit, no halt criteria. The team finds out from a stakeholder complaint, not from the system.
AISB component: v76 Recurrent Reasoning Protocol halt criteria (every loop has weighted halt-signal composite — entropy decrease, citation stability, drift score, Kairos confidence — and a hard minimum and maximum iteration count). v65 Drift Detector (pairwise claim contradiction scoring between parallel agents). v61 Provenance Hardening (immutable raw landing, per-claim source anchors, append-only ledger).
Reinforced by: v94 Memory Watchdog (system-wide RAM and swap pressure monitoring); v115 Evolution Event Audit Trail (every applied or rejected agent mutation emits an immutable audit record).
Source: Schneider Sustainability Research 2026 reliability-gap analysis.
Why this matters for procurement: Each component cited above has a public specification — published in this site's library, in the BEAST OS architecture document, or in the audit ledger. A buyer can verify the architecture before signing, not after the pilot fails. Use the Agent Door to run any of the three components against a sample question, or read the EU AI Act Readiness Procurement Document for the regulatory-anchored framing.