ConsultIn

Quantivo AI, also known as BOA (Business Opportunity Analysis), is an AI-powered SaaS platform that helps small business owners and entrepreneurs make data-driven decisions about their business opportunities. Given a business's context — category, location, growth stage, and goals — the system automatically scrapes real local market data, routes and filters it through a deterministic classification pipeline, and runs parallel sentiment and SWOT analysis using LLM agents orchestrated via LangGraph. The result is a comprehensive report featuring an executive summary, market insights, actionable recommendations, and a visual heatmap of the local competitive landscape. The platform is built around a contract-first architecture: a shared Pydantic schema and Protocol-based interface layer let independent workstreams — scraping, routing, retrieval, agent reasoning, and orchestration — develop and test against mocks in parallel before wiring in production components. Under the hood, Quantivo AI uses a hybrid dense-and-sparse retrieval system (Qdrant with BGE-M3 embeddings) fused via reciprocal rank fusion, and every confidence score is computed quantitatively from source count, agreement, and recency rather than left to subjective LLM judgment. The system is designed for graceful degradation: if any single data source or agent fails, the pipeline still produces a partial, clearly-labeled report instead of failing outright. Quantivo AI was built for the AMD Developer Hackathon (ACT II, Track Unicorn), with LLM inference and embedding generation running on AMD MI300X/MI350 GPU hardware via Fireworks AI and a self-hosted BGE-M3 embedding server, demonstrating a production-realistic AI pipeline built entirely on AMD's AI stack.

AuraOS

Aura is a sovereign, neuro-symbolic AI operating system designed to make advanced artificial intelligence more transparent, efficient, collaborative, and accountable. Instead of sending every instruction directly to a language model, Aura can locally interpret natural-language intent, organize it into structured semantic and routing representations, identify relevant code or knowledge, and prepare a bounded task before model use is required. Aura combines deterministic routing, a 4,096-primitive lexical system, polysynthetic six-slot intent structures, visual code topology, guarded human-agent workflows, and a Learning Arena where verified experiences can be tested before becoming reusable knowledge. Its Observatory allows users to see how Aura understood an instruction, while the Human Agent Arena supports node-specific dialogue, approval gates, verification, and preserved attempt artifacts—including failed work that may help future refactoring. Aura is built around human authority. Models can propose, explain, investigate, and assist, but they cannot silently expand scope, bypass evidence requirements, or merge changes without approval. The result is an AI architecture focused not only on capability, but on sovereignty, inspectability, learning, and responsible action.

AIVE-Artificial Intelligence Venture Engine

AIVE (Artificial Intelligence Venture Engine) is a Cognitive Discovery Operating System that transforms unstructured information into structured knowledge, evidence-backed insights, and actionable opportunities. Instead of only summarizing documents, AIVE builds an internal understanding of the information it receives and reasons across multiple knowledge sources. The platform ingests research papers, patents, technical documents, reports, datasets, presentations, websites, and other digital content. It extracts concepts, entities, relationships, evidence, and patterns, organizing them into an interconnected knowledge structure that enables deeper analysis and intelligent reasoning. Users can upload their own knowledge, explore it through an AI copilot, ask complex questions, and receive responses supported by evidence with traceability to the original sources. Rather than relying on predefined workflows or keyword matching, AIVE is designed to adapt dynamically to new domains and continuously expand its understanding as additional information is introduced. Beyond search and summarization, AIVE identifies research gaps, hidden relationships, emerging trends, contradictions, technology transfer possibilities, commercial opportunities, and innovation pathways. It generates research-grade reports, interactive knowledge graphs, visualizations, comparison tables, timelines, and decision-support outputs to help users understand complex information and make informed decisions. Built on knowledge engineering, graph-based reasoning, retrieval systems, and large language models, AIVE provides a unified workspace for researchers, engineers, enterprises, and innovators. Its vision is to become a trusted cognitive partner that accelerates scientific discovery, product innovation, strategic decision-making, and enterprise knowledge management through transparent, explainable, and evidence-driven intelligence.

AI Classroom Edge Intelligence

AI Classroom Edge Intelligence is a privacy-first classroom AI platform built for schools that need useful AI without sending every piece of student information to the cloud. The platform evaluates each task by privacy level, connectivity, and complexity, then routes it to Offline Edge Mode, a Local Classroom Server, or Fireworks AI Cloud Assist. Sensitive or restricted information stays local. Eligible anonymized, high-complexity tasks are sent through a secure Express backend to Fireworks Serverless using Qwen3.7 Plus. The project includes an AMD Model Router, Edge Runtime Monitor, Rural Connectivity Simulator, Privacy and Local Data Ownership Console, Classroom Digital Twin, and teacher approval workflow. Live results display the provider, model, route, privacy classification, latency, safety note, and AI response. API keys remain server-side, and the privacy guard blocks sensitive requests from cloud inference. The project was inspired by rural schools where connectivity can be unreliable and student privacy is critical. Instead of acting as a simple chatbot, it serves as an intelligent routing and decision-support system. Teachers review, edit, approve, or reject recommendations before instructional actions are recorded. The current implementation includes a working browser interface, real backend routing, live Fireworks Serverless integration, server-side key protection, and Docker containerization. Local AMD AI PC inference, GPU/NPU acceleration, device telemetry, and production synchronization are clearly identified as future work. The long-term vision is a school-owned AI platform combining local intelligence, optional cloud reasoning, persistent classroom evidence, and teacher oversight for rural and underserved communities.

Lexyprep

Verity — Fact-Checking for the Rest of Us

Most people believe first and verify never , there simply isn't time to check every headline, post, or forwarded message before deciding whether to trust it. That gap is where misinformation lives and spreads. Verity closes it. You give Verity a claim — a headline, a statistic, a line from an article — and it separates what's actually checkable from what's just opinion, then searches for independent evidence across 40+ public authoritative sources. Instead of trusting a single model's best guess, Verity requires independent checks to agree before returning a verdict: true, false, or somewhere in between, with a source trail you can click through and verify yourself. Every verification runs live on AMD Instinct hardware via the Fireworks AI API, using Gemma and other open-source models routed through a 5-pass consensus architecture — nothing pre-computed, nothing faked. The models are swappable and inspectable by design, because a trust tool that asks you to trust it blindly defeats its own purpose. Verity is built for the moment right before someone shares something they haven't checked , turning fact-checking from a newsroom luxury into something anyone can do in seconds.