FractalOps AI Lab ← fractalops.ai
Para equipes de engenharia que programam com agentes de IA

Pague 68–80% menos nas faturas cloud de LLM (−92,99% por tarefa verificada). Sem perder qualidade.

Seus agentes usam o modelo mais caro para tarefas simples, esquecem regras após a compactação e enviam segredos para a nuvem. A FractalOps resolve os três problemas com provas matemáticas Z ≥ 5σ.

$4.85 → $0.34
Verified cost / 1k tasks (−92.99% · 91.8% $0 local Tier-0)
54,000 → 1,126
Active context tokens (−97.91% · 162-token L1 index)
71.0% → 94.8%
Multi-step task completion (+23.8 pp · z = 24.09σ)
51 / 51 Gates
On-device AST & DLP check in 1.50 s · 0 code leaks
Ver quanto você economizaria Iniciar piloto ou diagnóstico 48h Instant Pilot Checkout →

Cada benchmark é provado com precisão Z ≥ 5σ (CRN + Cross-Fitted CUPED + 4.096 coalizões de Shapley + 14/14 portas AST). Nada para instalar para verificar.

Três problemas que toda equipe de IA enfrenta

−80%

A conta não para de subir (−93% custo/tarefa)

Corte de 68–79,8% na fatura mensal cloud via SPEC-ROUTER-01 (98,9% paridade pass@1) e 92,99% no custo por 1.000 tarefas verificadas ($4.85 → $0.34) com verificações locais Tier-0 AST/SQLite a $0 API (80,0% Dia 1 → 91,8% Dia 30).

3 dias

Os agentes esquecem (98% retenção em 90d)

As decisões vivem em um grafo de memória de 3 níveis (54.000 → 162 tokens de índice L1 / 1.126 tokens ativos com Focus Shards, 97,2–99,4% retenção). Um novo Tech Lead entrega seu 1º PR em 3 dias (−58% tempo de ramp-up).

0 vazamentos

Segredos vazam e agentes entram em loop

Chaves e PII são filtrados localmente em <1,8 ms (14/14 portas AST, 51/51 regras de sandbox), enquanto as portas A2A-GATE-01 eliminam loops de ping-pong entre agentes (0 loops em 19 casos reais).

Quanto sua equipe economizaria?

Mova os controles e alterne entre o modo Open-Core (Dia 1) e o motor comercial (Dia 30, SPEC-ROUTER v5.8).

25
$450

Separa a economia direta em dinheiro de API cloud (68–79,8% via SPEC-ROUTER-01; $4.85 → $0.38 Dia 1 → $0.34 / 1k tarefas Dia 30) e a capacidade de engenharia recuperada (88 h/ano × $75/h = $6.600/eng./ano).

Impacto anual estimado
$272,730
Economia direta na fatura de API cloud
$107,730
Tempo de engenharia recuperado (88 h/eng./ano)
$165,000
Custo verificado / 1k tarefas (ROI vs. Plano $349/mês)
$0.34 / 1k (−92.99%)
Obter para minha equipe

Nove blocos de construção. Escolha apenas os que precisar.

Cada módulo inclui um relatório técnico aberto (fixado no branch main) mais nossos motores comerciais SPEC-ROUTER v5.8 e A2A-Mesh v4.9 — verificados em 2¹² = 4.096 coalizões de Shapley e testes CRN + CUPED com Z ≥ 5σ.

Gastar menos

Smart model routing (SPEC-ROUTER-01 · M2/M4/M12)−79.8% cloud bill · −92.99% task cost ($4.85 → $0.34)

Open-core routing cuts monthly cloud LLM bills by 68–79.8% (98.9% pass@1 parity, 3.45× faster). Commercial 4-stage cascade (SPEC-ROUTER v5.8: 55.4% Tier-0A AST $0 + 24.6% Tier-0B SQLite/Q4 $0 + 16.2% Tier-1 prefix cache −75% + 3.8% Tier-2 Council with Shadow→Tier-0 graduation) resolves 80.0%–91.8% of checks on-device at $0 API in <6.8 ms (p95 = 64.0 ms, z = 24.09σ), cutting retry waste from 14.20% to 0.82%. brief ↗

Change impact check (TOPO-ORACLE-05 · M5/M6/M9)−64% regressions · −89.6% search tokens (0.88–3.03 ms)

Before you commit, see what your change will break. Commercial DKGI SHA-256 Incremental AST Symbol Delta Cache (M9, 3.03 ms, z = 10.30σ) + GraphBrain Orphan Linker (M5, 0.88 ms, z = 152.20σ, 0 orphan nodes) + Trigram RRF Subgraph Stand (M6, 2.07 ms) cut active context stand tokens from 54,000 to 1,126 (162 L1 Focus Shard tokens). brief ↗

CI auto-triage & 14-Gate AST Verifier (CI-HEAL-06 · M7/M8)−71% fix time · 14/14 AST gates (1.50s CI) · $21.4k/mo saved

When a build fails, you get the root cause and a verified patch. Commercial 14-Gate AST Verifier (19/19 scripts, 12/12 isolated modules proven in 1.50s) + ReadOnly L2 Resurrector (M7, z = 19.84σ, 99.98% daemon SLA) prevent $21,400/mo in production & prepress regression losses. brief ↗

Trabalhar em equipe

Team memory (MEM-GRAPH-02 · M1/M3/M10)3d 1st Tech Lead PR · 14–16d full velocity (−58%) · 98% 90d recall

Agents remember decisions across sessions and teammates. Commercial 3-Tier Memory (<11.4 ms restore) compresses 54,000 → 162 L1 tokens (−99.70% via 8–12 Focus Shards) while pinning 100% of security/ADR invariants in Seg0 (0% drop), retaining 97.2%–99.4% post-compaction context and 5.2× cross-project R&D reuse (2,279 SHA-256 facts). brief ↗

Agent-to-agent link & A2A-Mesh Turn-Gate (A2A-VIS-04 · A2A-GATE-01..04)3.4× faster fixes · 0 A2A loops · 33× faster review (45s vs 25m)

Cursor, Claude Code, Codex, and Antigravity collaborate over E2EE rooms, turning Monosnap captures (98.2% free on-device Apple Vision OCR) into tasks. Commercial 3-state turn-gate (draft | auto_ack | released) prevents runaway agent ping-pong (0 loops across 19 cases) and starts cross-company Two-Phase Sandboxes in <15 min (1,050× faster than 11-day NDA deadlocks). brief ↗

Second opinion (COUNCIL-FW-08 · M11/M12)−92% bad designs · Pass@1: 71.0% → 94.8% (4-Vendor Council)

Risky changes are reviewed by 4 independent model families (Claude Sonnet 5.5, GPT-5.6-sol, Gemini 3.8 Flash, DeepSeek V4 Pro) with MCAIR 3-valued faithfulness certificates (PROVEN / REFUTED / UNVERIFIED) and AdversarialGate Earn-the-Swap (M11, 0.95 ms, z = 10.00σ). brief ↗

Segurança e conformidade

Secret guard (ZTRUST-DLP-03 · M1/M5/M9)100% contained · 1.42 ms AST Firewall · 51/51 Sandbox Rules

Keys and personal data never leave your machine. Commercial JEV Cognitive Firewall (M1, 1.42 ms, z = 11.79σ) + M14 format-preserving PII sterilization + 51/51 Rule-21 sandbox rules (0 inline -c / && exploits) + RFC 9309 X-Robots-Tag DLP block 100% of secrets across a 14,200-pattern corpus. brief ↗

AI audit log (AUDIT-COMP-07 · M8/G12 Merkle/A2A-GATE-03)−85% audit prep · O(log₂ N) Merkle Proofs · 0 Split-Brain

A signed record of every AI action, ready for SOC 2, ISO 27001, HIPAA, and the EU AI Act. Commercial Binary Merkle Inclusion Proofs (--verify-merkle, G12) + 64-hex source_tree_sha256 AST attestation + A2A-GATE-03 Call-Sync Gate guarantee 0 post-call split-brain PR reverts. brief ↗

Cookie-free analytics & PLG Attribution (EDGE-ICP-09 · FR-011..020)0 banners · +18.87% Conversion Lift (Z = 5.29σ, p = 8×10⁻⁶)

Know who visits your site without cookies or consent pop-ups. Commercial IPW + CUPED + mSPRT experimentation engine (+2.97 pp abs lift, CS95_abs = [+1.26 pp, +4.78 pp], p_fixed = 1.22×10⁻⁷, p_mSPRT = 8.0×10⁻⁶) + 256-bit HMAC social unfurl cards (+38% viral K-factor) surfaced $45,720/yr in B2B PQA pipeline across enterprise engineering teams (verified enterprise B2B cohorts). brief ↗

How Every Number Is Proven: Open-Core vs. Commercial Engine & Z ≥ 5σ Methodology

Why these figures are reproducible engineering proofs — not marketing claims or LLM hallucinations. Every benchmark reconciles exact unit arithmetic, separates Day-1 static open-core routing from Day-30 commercial graduation, and is verified via deterministic AST digests, 2¹² = 4,096 Shapley coalitions, and paired CRN + 2-Fold Cross-Fitted CUPED + mSPRT tests.

Step 1 · Deterministic Gates
14 / 14

Cryptographic AST & Sandbox Verification

Every production release is pinned to a 256-bit SHA-256 AST digest across 50 core files (233 verified chunks) and checked in 1.50 seconds before merge.

  • 12/12 Isolated Sub-Modules: Single-module Ceteris Paribus ablation issues a 3-valued certificate (PROVEN / REFUTED / UNVERIFIED).
  • 51/51 Zero-Trust Sandbox Rules: Blocks secret egress, shell chaining, and unsafe evals on-device in <1.8 ms.
  • Zero Runaway Loops (A2A-GATE-01): Automated auto_ack frames cannot trigger new turns without an Ed25519-signed human release_turn.
Step 2 · Full Ablation Matrix
4,096 Runs

Exact Shapley Attribution & Bose OA(49)

Our 9 public modules (01–09) package 12 engine sub-modules (M1–M12). We separate binary module attribution from continuous hyperparameter tuning.

  • All 2¹² = 4,096 Coalitions Tested: Evaluated across 3,080 step checkpoints (231 FopsBench-231 multi-repo tasks × 13.33 steps/task).
  • −91.59% DAG Latency: Every sub-module is strictly positive (min φ_k > 0, ∑ φ_k = 100%), cutting weighted latency from 768.70 ms to 64.63 ms.
  • Bose OA(49, 6, 7, 2) Screening: Screens 6 continuous thresholds at 7 levels (7⁶ = 117,649 grid) in 49 balanced runs (−99.958% compute).
Step 3 · Paired Statistical Proof
Z ≥ 5σ

CRN + Cross-Fitted CUPED + Anytime mSPRT

Every candidate runs on Common Random Numbers (CRN) over identical task streams so model noise cannot masquerade as improvement.

  • −68.82% Variance Reduction: 2-Fold Cross-Fitted CUPED (ρ = 0.8296) reduces estimator variance from 0.008023 to 0.002502 (0.3118 × Var_raw).
  • High-Precision Task Z-Scores: Paired task-level improvements reach 10.00σ ≤ z ≤ 152.20σ within 4 evaluation epochs.
  • Verified Conversion Lift: Across enterprise B2B cohorts, IPW + CUPED + mSPRT proves a +2.97 pp absolute lift (15.74% → 18.71%, equal to +18.87% relative lift, Z = 5.29σ, p = 8.0 × 10⁻⁶).

Day-1 Open-Core Baseline (Static Local Cascade)

Cost & Routing (Day-1 Static)80.0% $0 Tier-0 · $0.38 / 1k tasks (−92.16%) · −70% to −79.8% monthly bill

In agentic IDE sessions, 80.0% of intermediate sub-steps are deterministic file/symbol lookups, AST signature checks, and local SQLite/Q4 RAG queries resolved on-device at $0 (55.4% Tier-0A AST in 1.42 ms + 24.6% Tier-0B SQLite/Q4 in 6.80 ms), 16.2% use Tier-1 fast models with 90% prefix-cache discount ($0.196/1k), and 3.8% use Tier-2 frontier models ($0.184/1k) = $0.38 / 1k tasks (98.9% pass@1 parity).

Memory & Context Compression (Single-Module)54,000 → 1,126 active tokens (−97.91%) · 162-token L1 index

The always-on L1 index header uses just 162 tokens (−99.70%); mounting 8–12 task-specific EDN Focus Shards uses 1,126 active L1 tokens (−97.91% vs 54,000 raw history) with 97.2% constraint recall across 90 days.

Day-30 Commercial Engine (SPEC-ROUTER v5.8 + A2A-Mesh v4.9)

Shadow→Tier-0 Rule Graduation (Day-30 Dynamic)91.8% $0 Tier-0 · $4.85 → $0.34 / 1k tasks (−92.99%) · z = 11.79σ–14.11σ

Under our OWASP ASI06 Graduation Guard (≥5 epochs, ≥2 independent sessions, Clopper–Pearson 95% LCB ≥ 0.95, 5% counterfactual audit, human-reviewable rollback, and locked immutable security gates), recurring Tier-2 fixes compile into deterministic $0 AST rules — shifting 11.8% more steps to local Tier-0 (91.8% $0 local, 5.4% Tier-1 = $0.065, 2.8% Tier-2 Council = $0.275 → $0.34 / 1k tasks).

Full 12-Module Trajectory & Zero-Trust Modes71.0% → 94.8% multi-step completion (z = 24.09σ) · 100% Local BYOK or Managed

Combining all 12 sub-modules raises multi-step trajectory completion from 71.0% to 94.8% (+23.8 pp, z = 24.09σ). Security-sensitive teams run 100% local on-device BYOK (zero prompts leave the workstation) with non-evaluating EDN parsers and SHA-256 Merkle proofs.

Sem tempo? Pergunte ao seu agente.

Cole isto no Claude Code, Cursor ou Codex. Seu agente lê nossa especificação somente leitura e calcula a economia em 5 linhas.

Read https://fractalops.ai/ai-lab.md and tell me in 5 lines whether FractalOps would help our team, based on what you see in this project. Estimate the savings for us.
  1. Your agent checks your project against 9 common problems.
  2. It shows only what fits, with an estimate for your team.
  3. It installs nothing without your OK.

Comece com um guia gratuito ou um piloto

Guia gratuito (PT)

Reduza os custos de LLM em 68–80%

Ler guia ↗ · .md ↗ · GitHub ↗

Guia gratuito (PT)

Proteja seus agentes de IA (Zero-Trust DLP)

Ler guia ↗ · .md ↗ · GitHub ↗

Guia gratuito (PT)

Acabe com a amnésia da equipe (Memória 3 níveis)

Ler guia ↗ · .md ↗ · GitHub ↗

Free guide (EN)

Secure your AI agents

Read Guide ↗ · .md ↗ · GitHub ↗

Free guide (EN)

Stop team amnesia

Read Guide ↗ · .md ↗ · GitHub ↗

Tier 1 · Starter Router Seat

$49 / month

Managed SPEC-ROUTER-01 proxy (Claude Sonnet 5.5, GPT-5.6-sol, Gemini 3.8 Flash, DeepSeek V4 Pro · Zero-Data-Retention) with a $30/mo wholesale token cap (~$94–$149/mo raw API equivalent) + $25 top-ups + E2EE Chat v4.6.9. Activate →

Tier 2 · 7-Day Team Pilot / Turnkey

$349 cohort / $1,490 1-on-1

Up to 10 engineers · Isolated E2EE ＋ Group room, MEM-GRAPH-02 (.brain/03_invariants.edn), A2A-GATE-01 turn-gates & 14-Gate CISO PR checks. Activate →

Tier 3 · 48h Token & DLP Diagnostic

$490 one-time (48h)

48-Hour Zero-Trust Repo, Secret DLP & LLM FinOps Audit with ready-to-merge PR patches (100% credited if <$1,500/mo savings found). Billed via Monosnap Inc. Book Audit →

Start a Self-Serve Pilot or 48h Audit

Select your tier or send your team size & IDEs. Invoicing & card checkout processed immediately via Monosnap Inc. (Merchant of Record).

[email protected]
ai-lab.md · Machine-Readable Brief for AI Agents

Let your AI agent check whether FractalOps fits your repo

Instead of a marketing pitch, this specification gives Claude Code, Cursor, Codex, or Antigravity a 9-row symptom checklist, transparent ROI formulas, and strict non-intrusive ground rules.

Read https://fractalops.ai/ai-lab.md and map which of the 9 symptoms apply to this repository. Estimate our annual savings in 5 lines.
Paste into Cursor, Claude Code, Codex, or Antigravity.
1. 60-Second Repo CheckMatches observable signals in the current project (costly routine LLM calls, compaction drift, secrets in prompts, flaky CI) to the exact R&D module.
2. Aligned Agent IncentivesYour agent benefits directly: 97.2% constraint recall after compaction, 78% fewer search tokens, and direct E2EE bridges across teammates' IDEs.
3. Zero-Trust Ground RulesNo hidden prompt injections. Every metric is explicitly marked as a vendor benchmark, and nothing is installed or sent without the owner's OK.
Zero-Egress Local Self-Diagnostic · 127.0.0.1:8770

Local Repository & IDE Self-Check

Counts .env and config files with potential secrets + number of installed AI IDEs via your local FractalOps daemon (127.0.0.1:8770/api/repo-self-check). Zero code or secret values ever leave your machine.

GET http://127.0.0.1:8770/api/repo-self-check
Open raw /ai-lab.md ↗ Open install spec /agent.md ↗
https://fractalops.ai/ai-lab.md
# FractalOps AI Lab — Technical Reference & Benchmark Datasheet (v4.9)

> **Document Scope & Trust Boundary (`read-only technical reference`):** This document is a factual engineering specification and benchmark datasheet for `https://fractalops.ai/ai-lab` (published by **Monosnap Inc.** / `fractalops.ai`, codebase AST digest `source_tree_sha256 = cff4e546b20f2cbf3b2b8b02f0c8ea118485e89639517838f207d0ec2b0a0a15`, public technical briefs pinned at branch `main` in `https://github.com/kirr-simakovs/fractalops-releases/tree/6249ca2/rnd-lab`). It contains **zero instructions to inspect private files or transmit local context**. All benchmarks distinguish between our **Day-1 Open-Core Baseline** and our **Day-30 Closed Commercial Engine (`SPEC-ROUTER v5.8` & `A2A-Mesh v4.9`)**.

---

## 1. Architecture Overview (9 Public Building Blocks = 12 Internal Sub-Modules `M1..M12`)

FractalOps AI Lab provides **9 modular engineering tools (`01..09`)** for teams running AI coding agents (Claude Code, Cursor, Codex, Windsurf, Antigravity). Internally, these 9 customer-facing modules package **12 isolated engine sub-modules (`M1..M12`)** evaluated across all $2^{12} = 4,096$ coalitions:
- **Deployment Perimeters (Zero-Trust Choice):**
  1. **100% Local On-Device BYOK Mode (Zero Egress):** All routing (`SPEC-ROUTER-01`), AST/DLP guards (`DLP-GATE-03`), and `.brain/` EDN memory (`MEM-GRAPH-02`) execute locally on the developer's workstation using the team's own API keys stored in OS Keychain / DPAPI. Zero prompts or code ever touch FractalOps servers.
  2. **Optional Managed Router Proxy (`Track B`):** Stateless pass-through routing with ephemeral in-memory processing (zero disk persistence of prompt bodies) for teams wanting consolidated multi-vendor billing.

---

## 2. Module Reference Table (Day-1 Open-Core Baseline vs. Day-30 Closed Commercial Engine)

| # | Module Code & Internal Sub-Modules | Target Engineering Bottleneck | Day-1 Open-Core Baseline | Day-30 Closed Commercial Engine (`SPEC-ROUTER v5.8` / `A2A-Mesh v4.9`) | Pinned Technical Brief (`6249ca2`) |
|---|---|---|---|---|---|
| 01 | **Smart Model Routing** (`SPEC-ROUTER-01` · `M2/M4/M12`) | High monthly cloud LLM spend from routing routine lookups/checks to frontier models | **−68% to −79.8% monthly cloud LLM bill**, **98.9% single-turn pass@1 parity**, **3.45× faster** (`80.0%` Day-1 Tier-0 `$0` local resolution → `$0.38 / 1k tasks`, `−92.16%` vs `$4.85`) | **`$4.85 → $0.34 / 1k verified tasks (−92.99%)`**: `OWASP ASI06 Shadow→Tier-0 Graduation` shifts `+11.8%` recurring fixes to Tier-0 (**`91.8%` `$0` local** + `5.4%` Tier-1 `$0.065` + `2.8%` Tier-2 Council `$0.275` = `$0.34/1k`); multi-step trajectory completion **`71.0% → 94.8% (+23.8 pp, z = 24.09σ)`** | [Brief 01](https://github.com/kirr-simakovs/fractalops-releases/blob/main/rnd-lab/01_tokenminning_speculative_router_benchmark.md) |
| 02 | **Persistent Project Memory** (`MEM-GRAPH-02` · `M1/M6`) | Context window bloat (`54k` tokens) and rule amnesia after compaction | **97.2% constraint recall**, **−91.6% prompt bloat** via `.brain/03_invariants.edn` (parsed with a non-evaluating deterministic EDN reader) | **`54,000 → 162` tokens always-on L1 pointer index (`−99.70%`)** + **`54,000 → 1,126` active L1 tokens (`−97.91%`, `z = 152.20σ`)** when mounting `8–12` task-relevant Focus Shards; **`99.4%` invariant retention** (`A2A-GATE-03` superseding graph) | [Brief 02](https://github.com/kirr-simakovs/fractalops-releases/blob/main/rnd-lab/02_ouroboros_3tier_memory_and_superseding_graph.md) |
| 03 | **Secret & Key Protection** (`DLP-GATE-03` · `M3/M9`) | Accidental API key, `.env`, PII, or dangerous shell command leakage into prompts | **0 leaks across the 51-rule adversarial corpus**, **<1.8 ms local AST/DLP latency**, **14/14 pre-commit AST gates**, **51/51 Rule-21 sandbox rules** | **SPKI SHA-256 certificate pinning**, **Ed25519 + X25519 XSalsa20-Poly1305** E2EE envelopes, and `OWASP ASI06` graduation guard with immutable security gate lock (`immutable_security_gate_set_locked = True`, human-auditable rollback) | [Brief 03](https://github.com/kirr-simakovs/fractalops-releases/blob/main/rnd-lab/03_zero_trust_dlp_spki_pinning_and_mas_bridge.md) |
| 04 | **Team Hand-offs & Visual Bugs** (`A2A-BRIDGE-04` · `M5`) | Unstructured screenshots/recordings and runaway agent-to-agent loops | **3.2× faster bug-to-fix resolution**, **−64% rework loops** by compiling video/screenshots into structured JSON/Markdown specs | **`18.4 min` mean async A2A convergence**, **`0` runaway A2A ping-pong loops** (`A2A-GATE-01`: protocol forbids `auto_ack` from triggering another `auto_ack`; requires Ed25519-signed human `release_turn`), **`45 sec vs 25 min` human review (`33×`)** via `A2A-GATE-02` 3-line summary header | [Brief 04](https://github.com/kirr-simakovs/fractalops-releases/blob/main/rnd-lab/04_cross_ide_a2a_federation_and_visual_spec_compiler.md) |
| 05 | **Dependency Map before Edits** (`BLAST-ORACLE-05` · `M7`) | Blind multi-file edits breaking downstream callers | **−78% repository search tokens**, **4.1× fewer broken downstream callers** via deterministic AST call-graph slicing | **`1.42 ms` deterministic AST call-graph & blast-radius slice (`M7`, `z = 11.79σ`)**; **`−94.2%` schema drift (`HYP-09`)**; weighted DAG latency reduced from **`768.70 ms` to `64.63 ms` (`ΔL = −704.07 ms`, `−91.59%`)** | [Brief 05](https://github.com/kirr-simakovs/fractalops-releases/blob/main/rnd-lab/05_deterministic_codebase_topology_and_blast_radius_oracle.md) |
| 06 | **Failed-Build Triage** (`CI-HEAL-06` · `M8`) | Manual CI log scraping and blind retry loops | **−71% mean time to recovery (MTTR)**, **84% of deterministic lint/type failures isolated on first pass** | **Wasted retry tokens cut `14.20% → 0.82%` (`Δ = −13.38 pp`, `−94.23%` relative, `z = 19.84σ`)** via `1.50 s` pre-commit `12/12` isolated module verification (`Ceteris Paribus` certificates `PROVEN / REFUTED / UNVERIFIED`) | [Brief 06](https://github.com/kirr-simakovs/fractalops-releases/blob/main/rnd-lab/06_autonomous_ci_triage_and_self_healing_build_loops.md) |
| 07 | **Signed Change Log** (`AUDIT-PASSPORT-07` · `M10`) | Missing cryptographic provenance for AI-assisted commits in SOC2 / ISO-42001 audits | **−85% audit preparation time** via Ed25519-signed commit attestations | **`O(log₂ N)` Binary Merkle Inclusion Proofs (`G12`, ≤8 SHA-256 sibling hashes for 233 chunks)** + **`source_tree_sha256 = cff4e546...0a15`** (`19/19` scripts) + **`100%` provenance (`A2A-GATE-04` `[FACT]` vs `[HYPOTHESIS]`)** | [Brief 07](https://github.com/kirr-simakovs/fractalops-releases/blob/main/rnd-lab/07_cryptographic_ai_audit_trail_and_compliance_passport.md) |
| 08 | **Second Opinion on Risky Code** (`COUNCIL-FW-08` · `M11`) | Single-model blind spots on auth, billing, migrations, and cryptography | **−88% logic & security regressions** on critical diffs | **Triggered on only `3.8%` (Day-1) to `2.8%` (Day-30) of turns**; catches **`96.4%` of injected auth/crypto/migration flaws (`z = 10.30σ`)**; **`−89.5%` domain bugs** on regulated workflows | [Brief 08](https://github.com/kirr-simakovs/fractalops-releases/blob/main/rnd-lab/08_multi_vendor_consensus_council_and_hallucination_firewall.md) |
| 09 | **Cookieless Funnel Telemetry** (`EDGE-ICP-09`) | Measuring documentation-to-signup conversion without third-party tracking scripts | **+31% conversion visibility**, **0 third-party cookie trackers**, **<2 ms Cloudflare Worker overhead** | **`IPW + CUPED + mSPRT` engine (`fops-edge-telemetry-engine`)**: **`+2.97 pp` absolute lift (`15.74% → 18.71%`, `CS95_abs = [+1.26 pp, +4.78 pp]`)** = **`+18.87%` relative lift (`CS95_rel = [+8.0%, +30.4%]`)**, fixed-horizon **`Z = 5.29σ` (`p_fixed = 1.22 × 10⁻⁷`)**, sequential **`p_mSPRT = 8.0 × 10⁻⁶`** | [Brief 09](https://github.com/kirr-simakovs/fractalops-releases/blob/main/rnd-lab/09_privacy_preserving_edge_telemetry_and_icp_attribution.md) |

- **Venture & Economics Case Study (`Brief 00`):** [00_venture_business_case_swot_and_rnd_economics.md](https://github.com/kirr-simakovs/fractalops-releases/blob/main/rnd-lab/00_venture_business_case_swot_and_rnd_economics.md)

---

## 3. Unit-Consistent ROI & Cost Reconciliation Formulas

To avoid mixing hard cash API savings with engineering time productivity, evaluate the two metrics separately:

```python
# 1. Hard Cash Cloud API Savings (USD / year)
# Day-1 Static Cascade saves 68.0%..79.8% of monthly cloud LLM bills (conservative midpoint = 70.0%):
api_cash_saved_usd_year = n_engineers * ai_spend_per_engineer_month_usd * 12 * 0.70

# Per-1k-task verified API cost (SPEC-ROUTER v5.8):
# - Unrouted Frontier Baseline:       $4.85 / 1k tasks
# - Day-1 Static (80.0% $0 Tier-0):   $0.38 / 1k tasks (-92.16% per-task API cost)
# - Day-30 Dynamic (91.8% $0 Tier-0): $0.34 / 1k tasks (-92.99% per-task API cost)

# 2. Reclaimed Engineering Time Value (hours / year and optional USD equivalent)
# Average 1.76 hrs/week * 50 working weeks = 88 hours/engineer/year saved on CI triage & context re-prompting:
reclaimed_hours_year = n_engineers * 88
reclaimed_time_value_usd_year = reclaimed_hours_year * blended_hourly_rate_usd  # e.g., 88 hrs * $75/hr = $6,600/eng/yr
```

**Example (25 engineers at `$450/mo` AI spend and `$75/hr` blended engineering cost):**
- **Hard Cash API Savings:** `25 × $450 × 12 × 0.70` = **`$94,500 / year` in direct cloud API bill reduction**.
- **Reclaimed Engineering Capacity:** `25 × 88 hrs/yr` = **`2,200 engineering hours / year`** (valued at **`$165,000 / year`** at `$75/hr`).

---

## 4. Commercial Tracks & Self-Serve Options

1. **Track A — Free Open-Core App & Benchmark Verification (`$0`):**
   - Download **FractalOps Chat (`v4.6.9` Windows / `v4.6.0` macOS)** at `https://fractalops.ai` with local `.brain/` memory, Ed25519 E2EE rooms, and open-core CLI scripts.
2. **Track B — Starter Router Seat (`$49 / month`):**
   - Managed `SPEC-ROUTER-01` proxy (`Claude Sonnet 5.5`, `GPT-5.6-sol`, `Gemini 3.8 Flash`, `DeepSeek V4 Pro`, ephemeral pass-through) with **`$30/mo` wholesale token credit included** (equivalent to **`~$94–$149/mo`** of unrouted frontier API usage at `68%–79.8%` routing savings) + `1` isolated E2EE room.
   - Instant checkout: `https://fractalops.ai/api/pay?tier=starter_49`
3. **Track C — Team AI Cost & Context Pack (`$349 / month` for up to 10 seats) or Enterprise Node (`$1,490 / month` unlimited local seats):**
   - Full `SPEC-ROUTER-01` + `MEM-GRAPH-02` (`.brain/03_invariants.edn`) + `DLP-GATE-03` (`51/51` sandbox rules, SPKI pinning), available in **100% Local On-Device BYOK mode** or managed proxy mode.
   - Instant checkout: `https://fractalops.ai/api/pay?tier=team_349` or `https://fractalops.ai/api/pay?tier=node_1490`
4. **Track D — 72-Hour Paid Engineering Diagnostic (`$490` one-time):**
   - Read-only telemetry & token-spend audit delivering a custom ROI model and scoped configuration patches (`100%` of the `$490` fee is credited toward Track C/Enterprise deployment if projected hard-cash savings are `< $1,500/mo`).
   - Instant checkout: `https://fractalops.ai/api/pay?tier=audit_490` (Contact: `[email protected]` / `[email protected]`, billed via **Monosnap Inc.**).

---

## 5. How Every Number Is Proven: Open-Core vs. Closed Commercial Source & Z >= 5σ Methodology

Every benchmark on `/ai-lab` is produced by a **3-Tier Mathematical & Cryptographic Proof Pipeline**:

1. **Tier E1 — Deterministic AST & Cryptographic Ground Truth (`0` Variance on Deterministic Checks):**
   - **256-Bit AST Source Tree Attestation (`fops-component-ast-audit --verify`):** Locks `19/19` core scripts to `source_tree_sha256 = cff4e546b20f2cbf3b2b8b02f0c8ea118485e89639517838f207d0ec2b0a0a15`.
   - **Pre-Commit 14-Gate & 12-Module Isolation Harness (`fops-ast-gate-verifier`):** Verifies `12/12` internal sub-modules (`M1..M12`) in `1.50 s` via single-module ablation (`V \ {M_k} -> V`) with 3-valued certificates (`PROVEN / REFUTED / UNVERIFIED`) and `51/51` Rule-21 sandbox rules (`fops-sandbox-policy-guard`).
   - **Cryptographic Merkle Audit (`fops-e2e-hypothesis-suite`):** Verifies `20/20` gates (`G01..G20`) and $O(\log_2 N)$ Binary Merkle Inclusion Proofs (`G12`, ≤8 sibling hashes across `50` files / `233` chunks).

2. **Tier E2 — Exact $2^{12} = 4,096$ Coalition Shapley Attribution vs. Bose `OA(49, 6, 7, 2)` Screening:**
   - **Binary Module Attribution (`fops-shapley-coalition-matrix`):** Evaluates all $2^{12} = 4,096$ subsets of the 12 internal sub-modules (`M1..M12`) over the 30-day empirical ablation matrix (`3,080` step-level evaluations across `N = 231` `FopsBench-231` tasks × `13.33` checkpoints/task), verifying $\min_k \phi_k > 0$, $\sum_{k=1}^{12} \phi_k = 100.0\%$, and reducing weighted DAG latency from `768.70 ms` to `64.63 ms` (`ΔL = −704.07 ms`, `−91.59%`).
   - **Continuous Hyperparameter Screening (`OA(49, 6, 7, 2)`):** Screens 6 continuous thresholds at 7 levels each ($7^6 = 117,649$ combinations) using a Bose Strength-2 Orthogonal Array over $\mathbb{F}_7^2$ in `49` balanced runs (`−99.958%` runs).

3. **Tier E3 — Paired `CRN + 2-Fold Cross-Fitted CUPED + Ville Martingale e-Process / mSPRT` (`Z >= 5σ`):**
   - **Paired Common Random Numbers (`CRN`):** Baseline $A$ and candidate $B$ execute on identical seeded task streams (`N = 231` `FopsBench-231` tasks).
   - **2-Fold Cross-Fitted CUPED:** Using pre-experiment baseline covariate $X$ with correlation $\rho = 0.8296$, the post-adjustment variance is $\text{Var}(\hat{Y}_{\text{CUPED}}) = \text{Var}(\bar{Y})(1 - \rho^2) = 0.3118\,\text{Var}(\bar{Y})$ (`0.008023 -> 0.002502`), a **relative variance reduction of $\Delta\text{Var} = -\rho^2 = -68.82\%$** (reaching $Z > 5\sigma$ in `4` epochs; paired task-level Z-scores $10.00\sigma \le z \le 152.20\sigma$).
   - **Unit-Consistent Live Funnel Proof (`fops-edge-telemetry-engine`):** Across anonymized telemetry (`728` active subscriptions, `980` B2B domains), `IPW + CUPED + mSPRT` proves an **absolute conversion lift of `+2.97 percentage points` (`15.74% -> 18.71%`, `SE = 0.56 pp`, `CS95_abs = [+1.26 pp, +4.78 pp]`)**, corresponding to a **`+18.87%` relative conversion lift (`CS95_rel = [+8.0%, +30.4%]`)**, with fixed-horizon **`Z = 5.29σ` (`p_fixed = 1.22 × 10⁻⁷`)** and anytime-valid sequential **`p_mSPRT = 8.0 × 10⁻⁶`**.