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19h ago·Apr 12, 2026
Automate the detection of signup friction using AI agents that navigate like real users and report issues instantly.
19h ago·Apr 12, 2026
We built ContractIQ, a 6-agent procurement workflow that turns contract documents into a decision-ready renewal package. IBM watsonx Orchestrate manages the full agent flow, including routing, parallel execution, confidence checks, and approval gates. Redis powers workflow state, inter-agent messaging, audit history, and cached memory across the pipeline. Tavily brings in live external research like vendor pricing, market signals, and competitive alternatives to ground the final recommendation.
19h ago·Apr 12, 2026
OpenClaw Finance Agent — Enterprise AI that learns your business without seeing your secrets. Finance teams want AI that understands their vendor patterns, approval workflows, and GL structures — but compliance (GDPR, SOX, CCPA) blocks them from exposing sensitive data to train it. The result is that companies only use AI for suggestions. It's too risky to let it actually execute anything. We built a local-first AI system that solves this with comprehensive tokenization. Before any AI model sees the data, every sensitive field is replaced with a structural token. Microsoft Corp becomes VENDOR_001[TIER_LARGE]. $150,000 becomes AMT_LARGE[CFO]. Employee emails become APPROVER_LEVEL_CFO. PII is never extracted in the first place — it doesn't enter the pipeline at all. The model learns your company's patterns from tokens alone. The system uses two-tier AI routing. A locally fine-tuned model (LoRA on Mistral 7B, 4-bit quantized) handles 80% of routine tasks like invoice processing, spend analysis, and GL posting — under 500ms at $0.01 per task. Complex reasoning like forecasting and risk analysis routes to Claude API at $0.50 per task. Smart routing decides based on task complexity and model confidence. Every AI decision passes through MCP governance servers before execution. Is the approver authorized for this amount? Is the GL account valid? Is there budget available? Are there any policy violations? All gates must pass before the system acts. Every decision is logged to an immutable, hash-chained audit trail stored in tokens — not real data. SOX compliant by design. The architecture runs two isolated AI agents: Hermes handles coding and infrastructure (Docker, GitHub, codebase intelligence, Doppler secrets), while Midas handles financial analysis and compliance (governance, audit, GL validation, Tavily research, Redis). Each agent has its own Claude Code CLI wrapper and dedicated MCP servers. Namespace isolation ensures Hermes never touches financial governance and Midas never touches infrastructure. Training improves continuously through three channels: Tavily batch-researches public financial data, the /dream command generates synthetic training examples on demand, and Honcho from Plastic Labs automatically optimizes prompts based on real usage patterns. The system gets smarter with every interaction. Custom AI trained on your tokenized data. Runs on a laptop. Zero cost per local query. Full compliance audit trail. Policy-governed execution. Built in 48 hours for the IBM Hackathon, Procurement and Finance Track. https://www.loom.com/share/d05ac73cb5f94fb1b2ad99df0e1d32ea
19h ago·Apr 12, 2026
Vasco is a mobile-first AI agent that gives building engineers the diagnostic depth of a senior chief engineer — without the $300,000+ salary. In this demo, a tenant in apartment 3B reports a heat pump faulting on high pressure. In under five minutes, Vasco identifies the unit, recognizes it as the third short-cycling event on the same riser this quarter, traces the supply chain upstream to a secondary condenser water pump issue, produces a sourced diagnostic walkthrough, and dispatches the contractor with the full diagnosis attached — all from the engineer's phone. Built on IBM watsonx Orchestrate, Claude Haiku 4.5 (pre-router), Redis (institutional memory), Tavily (live manufacturer documentation), Deepgram Nova-3 (STT), and ElevenLabs (TTS). Live app: https://vasco.one Youtube Video: https://youtube.com/shorts/HytYA_oQOZM?feature=share Github Video: https://github.com/hawkeye047/vasco/blob/main/vasco_video.mp4
19h ago·Apr 12, 2026
TrustOps Sentinel AI is a real-time AI governance platform that intercepts and evaluates agent actions before execution. Using a policy enforcement layer, contextual intelligence, and blockchain-backed auditing via Algorand, it ensures proactive prevention, explainability, and secure compliance for enterprise AI systems. Demo: https://drive.google.com/file/d/17iV6LugpKBKiDnN5EqTe2ceei4dCYTjW/view?usp=drivesdk
19h ago·Apr 12, 2026
MAIN DEMO: https://drive.google.com/file/d/1AWSg1w5srtz2kc2d1u2acTc1YYBGPmcT/view?usp=sharing STREAMLIT UI DEMO:https://drive.google.com/file/d/1oVChH6PqPA2oJ0zWeaNZ5tTYfMuagV1G/view?usp=drive_link DESCRIPTION: Enterprise Accounts Payable workflows generate significant volumes of invoice exceptions — discrepancies betweenpurchase orders, goods receipts, and supplier invoices that require manual investigation and resolution. These exceptions are difficult to resolve programmatically because the root cause typically exists outside the ERP system, in emails, verbal agreements, or undocumented supplier changes. NOCEPT is an agentic system built on IBM watsonx Orchestrate that implements a six-step sequential tool-call pipeline for autonomous exception resolution. Orchestrate manages the agent loop, invoking tools in order: exception classification via three-way match analysis, variance tolerance gating, Redis-backed historical similarity scoring against past approved cases, keyword confidence scoring across linked emails and transcripts, and Tavily-powered external web research for corroboration. Each tool writes its decision to Redis with a TTL, and the resolution tool reads whichever gate fired first. Short-circuit logic terminates the pipeline early on auto-approval. If no gate reaches threshold, the agent escalates with all intermediate evidence preserved in the audit log.
19h ago·Apr 12, 2026
DEMO VIDEO LINK: https://youtu.be/RzUFxBRLbJo OrgSim is a decision rehearsal engine for internal business choices. Instead of automating tasks, it asks: “If we make this decision now, how will different departments react over time, and where will conflicts emerge?” This is novel because there has not been a orchestration system that also incorporates a predictive component into it. utilizing a swarm simulation of agents from different departments to speak to one another allows for this simulation of scenario to identify conflicts and to avoid them. Most tools tell you what happened or automate what you already do. OrgSim tries to predict organizational ripple effects before a decision is made.
20h ago·Apr 12, 2026
In today’s music industry, the challenge is no longer access to talent, but the ability to identify the right talent early enough to act on it with conviction. As the volume of independent releases, social signals, and fragmented audience data continues to grow, A&R teams are forced to sort through an overwhelming amount of noise to find the few artists who represent real commercial opportunity. We believed there was room for a system built not for casual discovery, but for strategic decision-making at the enterprise level — which is why we built Sonar; an elite A&R intelligence agent designed for record labels and music companies such as Warner, Sony, and Universal, as well as distributors and talent-focused platforms such as DistroKid, to help their teams scout emerging artists with greater speed, confidence, and precision. Rather than replacing the instincts of an experienced A&R executive, Sonar is built to strengthen them. The platform functions as an intelligence layer that helps companies surface early-stage artists with meaningful momentum before they become obvious to the rest of the market. By leveraging advanced metrics, cross-platform validation, and predictive analytics, Sonar allows decision-makers to evaluate not just who is visible in the moment, but who is demonstrating the kinds of patterns that signal future breakout potential. In doing so, it enables labels and talent organizations to make smarter, earlier, and more defensible investments — reducing guesswork while increasing the likelihood of identifying artists whose growth can translate into long-term value for the business. It didn't let me add a video for some reason. Powerpoint slides https://1drv.ms/p/c/9c5cb125c160f90c/IQBkYlTV340KQo_SfvMk7w14AbYKUPR5qNnbM8ut_9n--OA?e=RcI86n
20h ago·Apr 12, 2026
AI agent that resolves invoice exceptions for accounts payable teams. Upload an invoice — image or text — and the agent extracts structured data, matches it against purchase orders, searches the web for vendor verification, and applies your company's rules to deliver a decision (approve, resolve, or escalate) with full reasoning and audit trail. Configurable exception rules, an LLM-as-judge for custom policies, and a human review queue close the loop between AI recommendations and human oversight. Live demo: https://genuine-dream-production-1287.up.railway.app Deck: https://genuine-dream-production-1287.up.railway.app/deck You can try it out on the live site and also download a very short demo video here: https://genuine-dream-production-1287.up.railway.app/demo.webm
20h ago·Apr 12, 2026
Pivt is an agentic supply chain crisis management platform built on IBM watsonx Orchestrate, designed to help logistics companies detect, respond to, and resolve delivery disruptions in real time, starting with weather-driven events. When a live NWS weather alert intersects an active delivery corridor, Pivt's three-agent pipeline activates automatically: the Optimizing Agent calculates reroute options with full cost and SLA analysis, the Facility Agent identifies the nearest viable drop facility if rerouting isn't justified, and the Driver Agent notifies the driver and pushes an updated route directly to Google Maps, all within 15 minutes. The platform uses Tavily for deep, real-time research on corridor conditions, weather headlines, and logistics disruption signals, giving every agent accurate and current situational context before making a recommendation. Redis serves as the platform's memory layer, maintaining live state across the agent pipeline so each agent picks up exactly where the last one left off, preserving shipment context, dispatch decisions, and route history across the full response flow without losing continuity. Together, the three agents act as a supply chain monitoring and response system and a built-in CRM for crisis resolution, turning what is today a 4–8 hour manual scramble into a fully orchestrated, auditable, 15-minute response. You can find the presentation link here: https://drive.google.com/file/d/1wPigTtPBGt5ioSkyl7xfD3oa7jMLKU-6/view?usp=sharing https://hel1.your-objectstorage.com/hackersquadcontent/project-recordings/project_rec_cmnw32fvc00iyr00kk6fq5ana-2026-04-12T184912.mp4
