AI Workflows

ESG compliance monitoring is manual, slow, and error-prone. This workflow automates real-time ESG data collection, risk scoring, and regulatory reporting using LangGraph orchestration with live market data feeds.

RAG pipelines work in demos but break under production load. This workflow builds rag-scale, a LangGraph pipeline with Pinecone Serverless, adaptive chunking, and load-balanced retrieval that handles 10K+ concurrent queries without degradation.

Claims processing eats 20 minutes per case. This workflow uses LLM-powered document extraction, fraud pattern detection, and multi-agent triage to cut processing time to 3 minutes while flagging 94% of fraudulent claims.

Contract review costs law firms $150-400 per hour. This workflow extracts obligations, flags risky clauses, and scores contract risk in 45 seconds — making contract review 50x faster while catching obligations human reviewers miss.

Academic peer review takes 3-6 months. This workflow builds a multi-agent system that reviews papers in 10 minutes, identifies methodology gaps, checks citation accuracy, and generates structured review reports — accelerating the entire review cycle.

Manual code review misses security flaws. This workflow builds pr-guard, a LangGraph pipeline that scans PRs with CodeQL, detects AI-generated vulnerabilities, and suggests automated fixes.

Data pipelines fail silently and teams discover anomalies hours later. This workflow builds pipeline-watch, a LangGraph pipeline with Apache Flink for real-time anomaly detection, automated root cause analysis, and self-healing remediation.

AI agents can now operate desktop applications through visual grounding and GUI action tokens. This workflow builds gui-agent, a LangGraph pipeline that takes a natural-language task, captures screenshots, identifies UI elements with visual grounding, generates click/type/scroll actions, and executes multi-step workflows across desktop applications.

Cloud AI has latency and privacy costs. Edge AI runs models directly on devices. This workflow builds edge-infer, a LangGraph pipeline that routes inference requests to quantized models running on-device via WebGPU, with adaptive model selection based on task complexity and device capabilities.

Documents are not just text — they contain images, charts, tables, and diagrams. This workflow builds multi-rag, a LangGraph pipeline that ingests multi-modal documents, indexes both text and visual content, and retrieves information across modalities using vision-language models and hybrid search.

Enterprise finance teams process millions of transactions daily, and manual reconciliation is a top source of accounting errors. This workflow builds recon-fleet, a LangGraph pipeline with Temporal durable execution that matches transactions across ledgers, flags discrepancies, routes disputes through human review, and produces an audit trail — turning a days-long manual process into an automated, auditable workflow.

95% of clinical trials miss their enrollment targets, and the primary bottleneck is patient-to-trial matching. This workflow builds trial-match, a LangGraph pipeline that ingests de-identified patient profiles, matches them against active trial criteria, scores eligibility, and routes borderline cases through a clinician escalation gate — all within a privacy-preserving architecture that never exposes raw patient data to the model.