Capability statement

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General Details


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Our Approach, directly brings Ai software solutions to Privacy / Security / Cost / and Research and Development.
The software is Fully - -

  • *- HIPPA Compliant ,[1]
  • FERPA - Compliant [2]
  • *- Meets all standards and metrics in the [3] [4] HITECH Act in 2021 ,
    especially in the Interoperability and Standards metrics.
  • - Fully GDPR compliant , [5]
  • Tier 4 , NIST CSF 2.0 provider and practitioner. – [6]
  • ISO / IEC 27001-2022 SOC 2 Type 1 Compliant [7] [8] [9]
  • Meets and follows ALL Directives within Executive Order 14719 - Removing Barriers to American Leadership in Artificial Intelligence [10]
  • Meets needs of the National Ai Innovation Office. [11]
  • NIST AI 100-1
    Artificial Intelligence Risk Management Framework (AI RMF 1.0)
    COMPLIANT [12]

  1. https://www.hhs.gov/hipaa/for-professionals/index.html
  2. https://studentprivacy.ed.gov/ferpa
  3. https://www.hhs.gov/hipaa/for-professionals/special-topics/hitech-act-enforcement-interim-finalrule/index.html
  4. https://www.hipaajournal.com/what-is-the-hitech-act/
  5. https://eur-lex.europa.eu/eli/reg/2016/679/oj
  6. https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.1302.pdf
  7. https://www.iso.org/standard/2700
  8. https://secureframe.com/hub/soc-2/type-1-vs-type-2
  9. https://drata.com/learn/soc-2/vs-soc-19
  10. https://www.presidency.ucsb.edu/documents/executive-order-14179-removing-barriers-americanleadership-artificial-intelligence
  11. https://www.nitrd.gov/coordination-areas/ai/
  12. https://www.nist.gov/itl/ai-risk-management-framework

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Software Details


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  • Current Version : - Flipping Frog v0.5
  • Architecture : X86 / ARM.
  • Loading Method : Plug and Play / Local Execution.
  • Power Consumption : $0.00090 per X1,000 tokens.
  • Estimated Response Time : 5 - 6 seconds max
  • Data Transmission Pathway : Local
  • Network Type : LAN Access / API Routing
  • Built in Context Lengths: 128 - 8,000 est.
  • Features
    • Text Input Response
    • Image Analyser - JPG / PDF etc .
    • Custom data section analyzation
    • Context Training Space / Drag and Drop Research papers.
    • Context Training dependent on Context Size
    • Language Translation - (better with training)
    • Thinking , Image , Coding , or ML models ,all available.
  • Trained Features
    • STEM / Physics / Math / Chemistry
    • Language Interpreter
    • Coding
    • Generalization
    • Thinking / Reasoning.

Hardware Requirements.

Model Sizes in B , Represents

the parameters or size , in billions.

Larger sizes require larger machines.

Machine Requirements :

CPU Ai - model SPECS

  • Small -:- 0B - 8B -:- CPU (Ryzen 5 / Intel i5 or better recommended)
  • Med -:- 8b-20b CPU (Ryzen 7 / Intel i7 or better recommended) 16 GB+ system RAM
  • High - : - 70B+ -:- High-end CPU (Ryzen 9 / Intel i9 or better, or Apple M-series) 32 GB+ system RAM

GPU Ai - model SPECS

  • Mid Range - 14–32B GPU-accelerated -:- NVIDIA GPU with 16–24 GB VRAM Strong CPU
    (Ryzen 7 / i7 or better) + 32–64 GB system RAM 1TB NVMe SSD
  • High performance- NVIDIA GPU with 24–48 GB+ VRAM (e.g., RTX 4090 24GB for comfortable 70B Q4, or dual GPUs / A6000 48GB for headroom) High-end CPU
    (Ryzen 9 / i9 or better) + 64 GB+ system RAM 1TB+ fast NVMe SSD
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Proposed, Software Future Tooling


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General Q & A