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.
Built at Enterprise Agents Hackathon: Powered by IBM Watsonx Orchestrate AI Labs, Tavily, and Redis
nocept
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.
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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