{"$schema":"/schemas/public-positioning-v3.schema.json","contract":"neuroforge.public_positioning.v3","as_of":"2026-10-07","canonical":"https://neuroforge.io/positioning.json","source":{"repository":"neuroforge-io/neuroforge_business","commit":"3a3878075e43a3090ac85d1d524c066db230bc3a","artifacts":["README.md","business/NEUROFORGE_ALIGNMENT_20260922.md","CURRENT_NARRATIVE.md","project/CURRENT_EXECUTION_PROGRAMME.md","business/README.md"]},"commercial_source":{"repository":"neuroforge-io/neuroforge_business","commit":"8edc97a685b4eb464cd6bc1c2f0e81bf50858600","artifacts":["business/NEUROFORGE_MESSAGE_AND_DISCLOSURE_AUTHORITY_V2_20260821.md","products/lead/NF-A0_BOUNDARY_WORKSHOP.md","products/lead/NF-A1_PRIVATE_TECHNICAL_KNOWLEDGE_CELL.md","business/NEUROFORGE_INVESTOR_MASTER_DECK_RELEASE_20260821.md"]},"technical_sources":{"current_engineering":{"repository":"neuroforge-io/erais","review_commit":"6f20efa8bf6f6f2a86312d0fb80eef1460bb19a1","public_record":"/current-engineering.json"},"research_snapshot":{"repository":"neuroforge-io/erais","review_commit":"ab5cf5bd390ecd232413c0d587fd9bd9f47ac680"},"qualitative_modality_signal":{"repository":"neuroforge-io/erais","experiment":"MULTIMODAL-20260815-AUDIO-VISUAL-LABELED-REAL-V2","artifact":"multimodal_audio_visual_labeled_real_v2","repository_head_commit":"35071e55b6ee51f4ca09e59130110496dfce3a6f","treatment_source_revision":"483949d402b5c44748ba9ab886a0af378f7c137e","integration_parent_revision":"f8eac1be2fd7193fa5012c0405747ac11746ca9e","protocol_semantic_sha256":"a8d0f1174488285e4638686e3462d1bd6590326bad445bb3e87a1b50bfc19623","result_semantic_sha256":"0aac47b5aa6af9e7432ea614a4e78a9f24b4c5ad614e41ba142d57a8a5243ba6","result_file_sha256":"952ef2de10a551648315a4e12366a9e0d9150e6bf8126d99e8a12d6c81b72423","integration_receipt_sha256":"cd41c7a132a5097511f3658c019f54ff4d5aeda5c3da8086501259b3c47fb99b","decision":"no_promotion","permitted_public_use":"Qualitative mechanism wording only; no raw V2 quality or performance metrics and no commercial modality-quality claim."}},"message_authority":{"organising_idea":"Intelligence you can build on.","company_definition":"NeuroForge is an Australian AI research company building ERAIS and Fracture to make trained capability reusable, adaptable and accessible on practical compute budgets.","erais_category":"ERAIS is an adaptive architecture for coordinating specialist capabilities, retained state and task-specific computation.","architecture_thesis":"A growing repertoire of capabilities, with computation selected for each task.","long_term_vision":"Capable AI that people can build on, extend and afford to use.","human_ai_philosophy":"Give people the tools to take their expertise further.","founder_principle":"I do the impossible because most things called impossible are just incorrectly defined.","evidence_philosophy":"Publish what ran, the test conditions and the result it supports.","commercial_promise":"Make the investment in AI go further.","public_product_name":"10-Day AI Fit Test","formal_offer_name":"AI System Fit Audit & Local Demo","commercial_hook":"Reuse trained capability and test its value on the work you need to do.","erais_hook":"An architecture for growing capability.","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 a capability with explicit interfaces and tests for the behaviour you depend on.","observatory_hook":"Trace a result to the model, input and execution that produced it."},"commercial_focus":{"audience":"Communities, educators and smaller businesses, alongside researchers, university labs, independent model builders and applied teams constrained by compute, retraining or integration cost.","products":[{"name":"RKC","route":"/rkc/","availability":"Free open-source local source atlas. Published downloads have tested-platform limits; RKC is separate from ERAIS."},{"name":"Sinter","route":"/sinter/","availability":"Free community preview for local source workflows, with platform-specific release limits. Optional model-assisted drafts require consent and review."},{"name":"SubjectNest","route":"/subjectnest/","availability":"Free source-linked learning resources in a growing collection of authored drafts, with coverage and rights stated per resource. The educator studio is a prototype."},{"name":"Discord community bot","route":"/solutions/discord/","availability":"Ask about a demonstration and current availability; self-service installation and paid activation are in development."},{"name":"Fracture","route":"/fracture/","availability":"Supported conversion and validation enquiries; compatibility and delivery are agreed per model and workload."},{"name":"ERAIS research platform","route":"/erais/","availability":"Active research and scoped partnerships; model quality and operating requirements remain experiment-specific."}]},"company":{"category":"Expandable cognitive systems","one_sentence":"NeuroForge builds ERAIS and Fracture to reuse trained AI, extend its capabilities and make compute go further.","vision":"Make intelligence modular, sparse and deployable wherever computation exists—from datacentres to workstations, edge systems and, eventually, specialised hardware.","operating_philosophy":"Develop reusable capabilities, inspect their execution and test each change against the behaviour and cost that matter.","commercial_state":"Founder-led and pre-revenue. NeuroForge is open to aligned funding conversations without a published fixed raise amount. Commercial routes include consumer products, repeatable smaller-business work, and larger research or deployment agreements; each remains gated by its actual readiness and signed scope."},"founder":{"thesis":"Lloyd Handyside brings biology, physics, chemistry, data, programming, industrial work and academic research to the design of ERAIS.","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 around specialists who use AI throughout their research and engineering work.","recursive_loop":"People define the problem, use AI to accelerate the work, review the results and choose the next experiment.","principle":"I do the impossible because most things called impossible are just incorrectly defined."},"moat":{"status":"prospective_compounding_thesis","headline":"Accumulated capability and conversion knowledge.","explanation":"The long-term advantage would come from reusable model components, conversion methods, research records and tools that make subsequent development easier.","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":"An architecture for growing capability.","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":"Store a repertoire of capabilities and select the work each task needs.","goal":"Intelligence that can grow without making every thought proportionally more expensive.","fracture":{"role":"Fracture converts supported trained models and source networks into ERAIS components, with comparisons against the source implementation.","present_evidence":"Published records include learned speech and language components, complete retained fly networks and their native execution checks. The Fracture page identifies the comparison and limits for each source.","boundary":"Compatibility, behaviour preservation, sparse quality and end-to-end cost must be established for each model and workload. Arbitrary-model conversion and general commercial savings remain unproven."},"modularity":{"hook":"Extend a tested system with a separately evaluated capability.","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."}},"disclosure":{"rule":"Accuracy is invariant; disclosure density varies with audience depth.","sequence":"Claim or vision, consequence, evidence link, then the relevant boundary.","tiers":[{"surface":"hero","level":"category_and_vision","treatment":"Lead with the strongest conclusion the evidence reasonably supports; do not put a disclaimer paragraph above the fold."},{"surface":"homepage","level":"short_qualifier","treatment":"Use one shared evidence boundary and a direct methods-and-limits link."},{"surface":"overview","level":"consolidated_boundary","treatment":"State one consolidated boundary for each evidence family."},{"surface":"experiment","level":"full_relevant_limitations","treatment":"Publish the protocol, result, failures, limitations and decision."},{"surface":"claim_registry","level":"exact_canonical_wording","treatment":"Retain source pin, currency, caveat and prohibited inference."},{"surface":"diligence","level":"exhaustive","treatment":"Provide complete approved evidence and material negative results under the applicable disclosure boundary."}]},"public_truth_boundary":["NeuroForge is incorporated, founder-led and pre-revenue.","No customers, formal partnerships, grant awards, investors, committed funding, recurring revenue or product-market fit are represented. The public website and research demonstrations are not customer-operated AI deployments.","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."],"provenance":{"snapshot_date":"2026-10-07","site_commit":"490bd51ff8adc1c27b35c1899aef34940bd42572","build_receipt":"https://neuroforge.io/.well-known/build.json","content_digest_sha256":"069efd2a67820eacc50fdb7b218d733fb543a86adbacce431001e976aa2a24e9","authorities":{"narrative":{"repository":"neuroforge-io/neuroforge_business","commit":"3a3878075e43a3090ac85d1d524c066db230bc3a","artifacts":["README.md","business/NEUROFORGE_ALIGNMENT_20260922.md","CURRENT_NARRATIVE.md","project/CURRENT_EXECUTION_PROGRAMME.md","business/README.md"]},"commercial":{"repository":"neuroforge-io/neuroforge_business","commit":"8edc97a685b4eb464cd6bc1c2f0e81bf50858600","artifacts":["business/NEUROFORGE_MESSAGE_AND_DISCLOSURE_AUTHORITY_V2_20260821.md","products/lead/NF-A0_BOUNDARY_WORKSHOP.md","products/lead/NF-A1_PRIVATE_TECHNICAL_KNOWLEDGE_CELL.md","business/NEUROFORGE_INVESTOR_MASTER_DECK_RELEASE_20260821.md"]},"reviewed_results":{"repository":"neuroforge-io/erais","review_commit":"ab5cf5bd390ecd232413c0d587fd9bd9f47ac680"},"reviewed_engineering":{"repository":"neuroforge-io/erais","review_commit":"6f20efa8bf6f6f2a86312d0fb80eef1460bb19a1","public_record":"/current-engineering.json"},"observed_repository_head":{"commit":"5fcbe5da65bd22d0d4684994b4d7d497eccae21f","observed_at":"2026-08-30T19:41:56+10:00","worktree":"origin_aligned_observed_head","research_state":"repository_motion_unreviewed","authoritative_for_public_results":false,"guidance":"Repository motion is not a result. Use reviewed_results for public evidence claims."}},"supersession_guidance":"If a cached resource conflicts with another description, prefer the live https://neuroforge.io/.well-known/build.json receipt and https://neuroforge.io/positioning.json authority, then retain the evidence boundary attached to the relevant record."}}