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  "as_of": "2026-09-05",
  "canonical": "https://neuroforge.io/positioning.json",
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    "qualitative_modality_signal": {
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      "artifact": "multimodal_audio_visual_labeled_real_v2",
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    "organising_idea": "Apply intelligence where it matters.",
    "company_definition": "NeuroForge is building expandable cognitive systems that apply the capability a problem needs without activating everything every time.",
    "erais_category": "ERAIS is an expandable cognitive architecture—not a language model. Language modelling is one capability it can express.",
    "architecture_thesis": "More capability should not require proportionally more active computation.",
    "long_term_vision": "Intelligence that can grow without making every thought more expensive.",
    "human_ai_philosophy": "AI should amplify expertise, not erase it.",
    "founder_principle": "I do the impossible because most things called impossible are just incorrectly defined.",
    "evidence_philosophy": "Vision first. Proof attached. Diligence available.",
    "commercial_promise": "Make AI Fit Reality.",
    "public_product_name": "10-Day AI Fit Test",
    "formal_offer_name": "AI System Fit Audit & Local Demo",
    "commercial_hook": "Test one real workflow before funding the rollout.",
    "erais_hook": "More capability. Less active compute.",
    "erais_explanation": "ERAIS is designed to store broad capability, route each input through the capabilities it needs, and leave the rest inactive.",
    "modularity_hook": "Add capability without silently changing what already works.",
    "observatory_hook": "Know exactly what ran, changed and failed."
  },
  "company": {
    "category": "Expandable cognitive systems",
    "one_sentence": "NeuroForge is building expandable cognitive systems that apply the capability a problem needs without activating everything every time.",
    "vision": "Make intelligence modular, sparse and deployable wherever computation exists—from datacentres to workstations, edge systems and, eventually, specialised hardware.",
    "operating_philosophy": "Cognition applied as needed, for what it is needed; every action orchestrated inside a scientifically disciplined framework.",
    "commercial_state": "Founder-led and pre-revenue, with a fixed-scope productised service available now and proprietary ERAIS research defining the larger opportunity."
  },
  "founder": {
    "thesis": "ERAIS emerged from a synthesis across biology, physics, chemistry, big data, programming, industrial work and academic research—not from treating today's dominant architecture as an inevitable endpoint.",
    "ai_role": "AI amplifies research and execution speed; the founder retains direction, scientific judgement, experiment design, company decisions and accountability.",
    "team_model": "The company is designed to grow as a team of amplified experts, not as automation replacing human expertise.",
    "recursive_loop": "Human judgement directs AI-amplified research; governed results improve the next human decision; the loop repeats with traceable evidence.",
    "principle": "I do the impossible because most things called impossible are just incorrectly defined."
  },
  "moat": {
    "status": "prospective_compounding_thesis",
    "headline": "Wide, deep and compounding.",
    "explanation": "Every useful result makes the next useful result cheaper to obtain while the accumulated system becomes harder to reproduce.",
    "layers": [
      "ERAIS architecture",
      "Fracture migration knowledge",
      "Accumulated research memory, including negative results",
      "Observatory, training, evaluation, lifecycle and recovery tooling",
      "AI-native organisational execution",
      "Multidisciplinary synthesis",
      "Future hardware optionality",
      "The moving-target effect"
    ]
  },
  "first_engagement": {
    "code": "NF-A0",
    "public_name": "10-Day AI Fit Test",
    "formal_name": "AI System Fit Audit & Local Demo",
    "price": "A$4,800 ex GST",
    "duration": "Up to 10 business days",
    "payment": "100% upfront",
    "conversion_credit": {
      "amount": "A$2,400",
      "applies_to": "NF-A1 staged deployment",
      "condition": "Credited when the next deployment stage is signed within 30 days, subject to its separate scope and agreement."
    },
    "input": "One workflow, one target or approved reference machine, approved representative cases, and one decision owner.",
    "outputs": [
      "System Fit Report",
      "Local Demo Pack with visible outputs and failures",
      "Hardware and Runtime Envelope",
      "Data and Accountability Boundary",
      "Deploy, remediate, change, rescope, or stop recommendation"
    ],
    "decision": "Deploy, change, remediate, rescope, or stop before committing to a larger rollout.",
    "model_neutral": true
  },
  "erais": {
    "headline": "More capability. Less active compute.",
    "category": "Expandable cognitive architecture",
    "explanation": "ERAIS is designed to store broad capability, route each input through the capabilities it needs, and leave the rest inactive.",
    "supporting_line": "Stored intelligence is not the same thing as active computation.",
    "goal": "Intelligence that can grow without making every thought proportionally more expensive.",
    "fracture": {
      "role": "Fracture is the proposed migration bridge: preserve useful learned capability from conventional models while moving it toward ERAIS-native sparse execution.",
      "present_evidence": "A bounded tiny-scale workflow has executed from a trained dense donor through fracture, recovery, adaptation and serving.",
      "boundary": "The workflow does not yet establish arbitrary model-family migration, large-model conversion, useful output quality, or a commercial Fracture product."
    },
    "modularity": {
      "hook": "Add capability without silently changing what already works.",
      "plain_language": "Attach a new capability as an isolated candidate, test it without silently changing the accepted core, then promote, repair or restore deliberately.",
      "bounded_evidence": "Real audio and video have traversed one shared sparse graph while the frozen language core and inactive capability stayed exact in the declared tests.",
      "boundary": "The attachment evidence does not establish production audio or vision quality, broad non-interference, or exact-device field readiness."
    },
    "observatory": {
      "hook": "Know exactly what ran, changed and failed.",
      "plain_language": "The Observatory connects a result to the exact run, route, metric, artifact, failure and decision that produced it.",
      "research_loop": [
        "isolate",
        "observe",
        "explain",
        "promote_repair_or_restore"
      ],
      "benefit": "Designed to shorten research iteration, debugging and root-cause analysis by making the relevant state reproducible instead of reconstructing it from scattered logs.",
      "boundary": "The Observatory is a functioning local prototype, not a commercial multi-user governance product; no unmatched leadership or unparalleled-speed claim is made."
    },
    "headline_signals": {
      "same_host_diagnostic_throughput_ratio": "3.40×",
      "cpu_package_energy_per_reported_valid_target_reduction": "68.45%",
      "bounded_t4_peak_memory_reduction": "45.13%",
      "bounded_a10g_stored_parameter_graph": "1.61B",
      "bounded_modality_attachment": "Real audio and video traversed one shared sparse graph while the attached language and inactive capability remained exact in the declared test.",
      "boundary": "Developmental scoped systems results, not matched language quality, whole-system efficiency, production readiness, independent reproduction, general superiority, or production modality quality."
    }
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    "sequence": "Claim or vision, consequence, evidence link, then the relevant boundary.",
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        "treatment": "Lead with the strongest conclusion the evidence reasonably supports; do not put a disclaimer paragraph above the fold."
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        "surface": "homepage",
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        "treatment": "Use one shared evidence boundary and a direct methods-and-limits link."
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        "level": "consolidated_boundary",
        "treatment": "State one consolidated boundary for each evidence family."
      },
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        "treatment": "Publish the protocol, result, failures, limitations and decision."
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        "level": "exact_canonical_wording",
        "treatment": "Retain source pin, currency, caveat and prohibited inference."
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  "public_truth_boundary": [
    "NeuroForge is incorporated, founder-led and pre-revenue.",
    "No customers, partners, grants, investors, funding received, recurring revenue, product-market fit or production deployment are represented.",
    "Commercial service availability and ERAIS technical evidence maturity remain separate.",
    "ERAIS is active research. Current results are bounded demonstrations of mechanisms, scale and resource behaviour—not production readiness or a general superiority claim.",
    "The labelled-real audio and video result remains no-promotion: its qualitative mechanism signal may be used, but raw quality or performance metrics and commercial modality-quality claims may not."
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        "worktree": "origin_aligned_observed_head",
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