Product
Product Vision

Product Vision

Suite of tools to facilitate enterprise execution

Ship solution that helps people focus on business value

  • software
  • hardware

Local-first (easily deployable & online-ready) suite of tools including frontends, backends, language models to facilitate enterprise execution with AI.

Build & deploy AI apps with a mobile phone.

  • resonsive markup for all pages + good UX

Execute AI Enterprise with a mobile phone.

Enterprise Runtime Stack

LLM-empowered features

AI-native enterprise execution platform. Each feature, each process is to be thought of from the perspective of how it can be enhanced with LLMs. Throughout the entire Enterprise Runtime Stack.

Neural interface

Implement a neural interface to interact with LMs. Instead of sending text / voice to LMs, make LMs able to recognize thoughts and execute them. (e.g. neuralink ?)

bci (brain-computer-interface)

Responsible AI

SAFe has a bit analog approach to Responsible AI. My vision is just implement all evaluation types that are possible in programmatic way with LMs and AI Agents. So that any developer of an AI tool can:

  • see the benchmark dashboard in real time with a report with all evals based on studying his data and code, processes with LMs and outlines of mistakes
  • & recommendations of how to fix them
  • & one-click implementation of fixes (like with code linting errors)
  • & risks or law violations, etc.

Make entire SAFe & Responsible AI thing just a program, which is easy to run locally for your project. Functional enough to provide real value to developers of AI. Fast enough to be performant.

SRE ?

Consider adding SRE management to the Enterprise-helper?

Solutions / Features

Finance

Enable AI-ified insights into finance & possible optimizations.

Language Models

  • Integrated solution, that would be capable of
    • Access and operations with the Operating System and File System
      • CRUD of files
      • Writing code
      • Terminal access (executing commands)
      • Browsing
      • Running LMs locally
    • Knowledge base with UI (Confluence analog)
    • Ticket system with UI (JIRA analog)
    • SAFe system with UI
    • How do I make it?
  • tools for different purposes (like Vscode for coding, or Figma for UI prototyping might be released at some point bundled with LMs, trained for performance in their corresponding domain). Most probably, it is a matter of time, when it becomes a new industry standard.

What does it mean for the product I'm building?

Users collaboration

  • Users can
    • work with the product fully locally (solo-mode)
    • deploy the app to the server and work with the product together
      • user access

How do I architect the data-persistence to satisfy both scenarios?

Architecture

  • Data persistence
    • Local Database to persist the data of the app.
    • What else? Persistence of LM interactions?
    • etc ?

Random

Legal

  • Build own LM from sratch or use an existing one and build on top of it?
    • Can probably use Llama, based on Legal Considerations until the cutoff of 700M MAU. For fastest TTM.
    • Consider building own (opens in a new tab) when approaching this point? Or from the very beginning? For full control & flexibility + legal rights (intellectual property) ?

POCs

List, what are current POCs, what I intent to achieve with them.

  • Dev POCs
    • Dev POC #1
      • Strategic Themes + Chat with docs (?)
    • Dev POC #2
      • Dev POC #1 + RAG + WebSearch (?)
    • Dev POC #3.1
      • Put together
        • knowledge base
        • ticket system
        • software-engineer AI
        • continuous training pipeline
        • DevOps platform
        • Status page system
    • Dev POC #3.2
      • Implement a chain of entities
        • Strategic themes
        • Epics
        • Capabilities
        • Features
        • User Stories
    • Dev POC 4
      • continuous training pipeline (litgpt)
    • Dev POC 5
      • chat + web-container (dev env: code IDE, browser, terminal)
    • Dev POC 6
      • generate app from strategic themes
        • repo-context.json + LM + Strategic Themes
  • Training POCs

LM Evaluation for SAFe

  • Create own evaluation for LMs for SAFe questions.
    • Create a dataset of Q&A.
      • Base
      • Supervised
  • Evaluate models against it.
    • Get the results.
    • Compare
  • Which LM performs best on this benchmark (give it a name) ?

Hardware

Llm Empowered FeaturesProduct Hardware Vision