Selected Contributions

Context

A specialized engineering team needed intensive, isolated review cycles over dense technical documentation. This required a secure system capable of retrieving and analyzing the documentation in depth while protecting sensitive information and underlying data structures.

Implementation

• • LangGraph stateful multi-agent topology with discrete retrieval, domain-contextualization, and evaluation nodes for iterative review workflows.
• • Azure AI Search abstracted into the agent runtime, powered by frontier LLMs via Azure AI Foundry for deep semantic parsing of engineering reports.
• • Automated citation-verification loop that validates integrity and reroutes incomplete reviews back through the agent chain.

Context

Employees often knew what a document was about but not its title or identifier, making retrieval across large repositories slow and unreliable. A way to find documents by their meaning was needed.

Implementation

• • Composable multi-agent system via Model Context Protocol (MCP), with a primary Python coordinator treating specialized reasoning agents as decoupled, dynamically executable tools.
• • On-prem Weaviate instance with a hybrid search pipeline blending dense vector semantic lookups and sparse scalar keyword matching for exact file identifiers.
• • Tailored conversational interface adapting payload schema to user inquiry intents — from atomic attribute parsing to holistic summary reports — with persistent file links injected into source outputs for strict data lineage.

Context

Staff in various functions received repetitive employee questions about policies and procedures already documented on the intranet. This created a need for a secure Q&A layer that provides direct, authorized answers and reduces manual email requests.

Implementation

• • Network of context-bounded reasoning agents on hardened Atlassian MCP integrations, separating sensitive business units into decoupled agent runtimes.
• • Real-time federation over live Jira and Confluence REST APIs via MCP, bypassing batch vectorization to surface ticket updates and document changes instantly.
• • Strict routing logic restricting each agent client to predefined workspace scopes, enforcing role-based data isolation and eliminating cross-domain contamination.

Context

Support teams handled high volumes of incoming requests through a manual triage and resolution process, creating a need to automate classification and surface relevant past resolutions at scale.

Implementation

• • Autonomous Jira triage pipeline: incoming payloads evaluated, classified by technical intent, and queue-prioritized via LLM-driven classification.
• • Historical retrieval layer on Azure AI Search vectorizing past ticket metadata, comments, and resolution metrics for real-time semantic matching between new incidents and verified historical fixes.
• • Decision-gate framework: resolution payloads auto-dispatched for informational requests; contextual summaries and historic references generated to accelerate human review of complex bugs.

Context

Misclassified documents increased delays and downstream workload, as teams had to spend additional time verifying that document packages were correctly organized and complete. The business needed automated classification both at ingestion and later checks to reduce this manual effort.

Implementation

• • Fine-tuned text classification model matched to the organization's document taxonomy, replacing error-prone manual entry with automated file type predictions at ingestion.
• • Dual-mode inference infrastructure: synchronous API endpoints for real-time user uploads plus isolated, parallelized background queues for asynchronous batch processing of legacy file caches.
• • Confidence-score thresholds in the classification layer: low-confidence files routed to a human-in-the-loop audit dashboard; high-certainty classifications published directly to the production database.

Context

Large-scale CAD projects generate millions of object collisions. While some are cosmetic, others represent genuine structural risks. The goal was to create a system to identify critical issues.

Implementation

• • Spatial semantic enrichment pipeline analyzing low-level geometric attributes and bounding boxes to infer functional object types within unstructured CAD assemblies.
• • Specialized feature extraction and geometric heuristics to classify anonymous design components into defined physical categories, bypassing the absence of native design tags.
• • Clean taxonomy schema appending semantic labels to detected collision coordinates, giving downstream engineering systems the contextual criteria to filter minor spatial false-positives.

Context

The business needed to extract structured data from diverse documents, ranging from historical scans to modern digital files, through a standardized processing capability that could support multiple use cases.

Implementation

• • Centralized data-ingestion gateway pairing structural PDF parsing with intelligent OCR pipelines for text and layout normalization across multi-generational asset formats.
• • Layout-aware extraction logic to locate, isolate, and map variable data points across non-uniform document coordinates, neutralizing structural skew without breaking on template shifts.
• • Processing engine abstracted into a multi-tenant microservice infrastructure — a single parsing foundation powering multiple downstream workflows.