Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

Chroma

Chroma is building the database that learns. It is an open-source AI-native embedding database. Chroma makes it easy to build LLM apps by making knowledge, facts, and skills pluggable for LLMs. The fastest way to build Python or JavaScript LLM apps with memory

General
Relese date2023
AuthorChroma
Typeembedding database

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Chroma AI technology page Hackathon projects

Discover innovative solutions crafted with Chroma AI technology page, developed by our community members during our engaging hackathons.

Agentic Audit

Agentic Audit

Agentic Audit is a proactive, multi-agent compliance monitoring system designed for enterprise organizations. Instead of relying on manual searches or reactive LLM prompts, our platform utilizes an asynchronous Python backend to continuously monitor highly regulated portals, such as the SEC or EU data privacy boards. By leveraging Bright Data's Web Unlocker and Scraping Browser, our system successfully bypasses the geo-blocks and heavy JavaScript barriers that typically block standard bots, allowing us to reliably capture raw regulatory text and timestamped screenshot evidence. Once our monitoring engine detects a change via SHA-256 hash comparisons, it triggers a multi-agent AI swarm. These agents analyze the text diffs, strip away the legal jargon, and map the updated regulations directly against internal company controls. The final output is delivered to a React dashboard where compliance officers can view a side-by-side comparison of the AI's risk assessment and the verified Bright Data screenshot. The system goes beyond basic summarization by providing actionable, auto-generated engineering tickets for immediate remediation, turning raw web data into operational enterprise security.

BargainHunter_B2B And Pricing Surveillance Agent

BargainHunter_B2B And Pricing Surveillance Agent

BargainHunter is an autonomous, enterprise-grade AI agent explicitly designed for the Web Data UNLOCKED Hackathon to revolutionize Go-To-Market (GTM) Intelligence and Finance strategies. Built on a robust FastAPI backend and orchestrated by a sophisticated LangGraph multi-node architecture, BargainHunter automates the critical process of real-time market and pricing surveillance. The system executes a seamless multi-step AI workflow: First, a Scraper Node leverages Bright Data to extract targeted competitor pricing while filtering out irrelevant noise. Next, an Analyzer Node aggregates this data to identify pricing anomalies and market leaders. A Recommender Node then employs Retrieval-Augmented Generation (RAG) using a Pinecone vector database to recall historical intelligence reports, providing historical context for strategy formulation. Finally, a unique Critique Node acts as a manager, utilizing a self-reflection loop to ensure all AI-generated pricing strategies strictly adhere to enterprise business rules before finalizing the output. Featuring real-time streaming execution, users can watch the AI's step-by-step logic live via a Streamlit dashboard. Furthermore, the system includes a robust B2B alerting mechanism that instantly notifies GTM and Finance teams of high-priority market shifts, empowering data-driven, strategic decision-making.

Dementia ASR Screening

Dementia ASR Screening

Overview The Dementia ASR Screening & Multi-Factorial Risk Stratification Application is an advanced digital health portal designed to bridge the gap between AI-driven speech biomarkers and modifiable clinical risk factors. Built on a premium, mobile-first dark theme interface, the portal enables patients to undergo non-invasive, visual speech elicitation tests in seconds, generating comprehensive, weighted dementia conversion risk assessments. Key Features & Technological Innovation Lossless Acoustic Elicitation: Features a custom-built, client-side Web Audio API recorder that encodes raw microphone samples directly into lossless 16-bit Mono PCM WAV format. This ensures 100% processing compatibility with cloud-based Automatic Speech Recognition engines. Speechmatics SaaS ASR Integration: Orchestrates advanced speech analyses via Express backend proxies. By disabling disfluency filtering (remove_disfluencies: false), the portal natively captures and tags spoken filler words. Acoustic Pause Biomarker Extraction: Reads word-level transcript timestamps to automatically isolate and flag long conversational hesitation gaps exceeding 1.5 seconds, a key marker in acoustic cognitive screens. Wiley 2025 Multi-Factorial Stratification: Incorporates a validated clinical risk algorithm based on the October 2025 study in Alzheimer's & Dementia (DOI: 10.1002/alz.70870): Age Scoring, Cognitive Reserve, BMI,Depression Screening (PHQ-2), Weighted Clinical Risk Dial: Automatically weighs Vocal speech biomarkers (40% weight) against Clinical modifiable factors (60% weight) to render a composite, color-coded Stratified Risk Indicator (Low, Moderate, or High Risk). Comprehensive Session Review Timeline: Connects to a self-healing SQLite database to store serialized disfluency details, allowing patients to inspect past tests and review historical reports exactly as they looked live along with an Interactive Demographics Editor.

SentinelMesh — Enterprise AI Security

SentinelMesh — Enterprise AI Security

Enterprise AI agents are powerful — but ungoverned. A single manipulated prompt can leak credentials, exfiltrate PII, or trigger unauthorized actions inside production systems. Most teams have no visibility into what their agents are doing until something breaks. SentinelMesh solves this with a three-layer security architecture built on top of Lobster Trap, a custom Go-based Deep Prompt Inspection proxy: • Ingress DPI: Every outgoing prompt inspected in under 1ms via regex pattern matching. Prompt injections, PII requests, malware generation, data exfiltration, obfuscation, and sensitive path access blocked before they reach the model. • Egress DPI: Every model response buffered and inspected before delivery. Credentials and PII in outputs blocked at the proxy layer. • Intent Mismatch Detection: Each agent declares its intent per-request. Lobster Trap independently classifies actual intent. Mismatches trigger HUMAN_REVIEW — compliance officers approve or reject from the live dashboard. Three-layer RBAC is enforced independently at: the LangGraph orchestrator, the ChromaDB vector store, and the Lobster Trap policy file. Any single layer can block a request. The result: a 14-vector adversarial test suite with all attacks blocked, a live governance dashboard with real-time audit logs and attack heatmaps, and a Railway-deployed backend enterprise security teams can evaluate today.

Onus HybridMind: AI Procurement Auditor

Onus HybridMind: AI Procurement Auditor

Enterprise procurement teams are bound by complex vendor contracts containing rebate thresholds, volume discounts, and penalty clauses buried in dense legal documents. Meanwhile, SQL databases hold the transaction records, but no one cross-references them against contract terms at scale. The result is millions in uncollected rebates and compliance gaps going undetected. To solve this, we built HybridMind: an autonomous AI audit agent that bridges structured SQL procurement data and unstructured vendor contracts in real time. It is not a standard RAG chatbot; it is a deterministic, multi-agent auditor built specifically to stop financial leakage. Our backend leverages LlamaIndex Workflows to orchestrate three distinct AI agents powered by Gemini 3.1 Flash Lite. The process begins with the Executor Agent. It converts natural language into strict SQL queries against our Supabase PostgreSQL database while simultaneously querying our ChromaDB vector store for the exact legal clauses. Next, the Verifier Agent takes both data sources and performs logical validation. It cross-references the SQL math against the PDF rules to detect discrepancies, such as verifying if a 12,000 unit purchase correctly triggers a 10,000 unit rebate threshold. Finally, the Chronicler Agent packages the verified finding and broadcasts the financial metrics to our live React dashboard via WebSockets. This architecture ensures transparent reasoning. While standard AI often hallucinates numbers, HybridMind forces the AI to show its raw SQL, explicitly cite its contract clauses, and prove its math. By keeping the frontend stateless and the backend firmly grounded in dual-silo retrieval, nothing is hidden behind a black box. Ultimately, HybridMind turns silent leakage into documented liability.