Enrichment & relationship intelligence
MENT
Oct 2023 - Oct 2025AI Engineer
Remote
Better context. Less waiting.
MENT is a privacy-first network-intelligence platform. My work covered collecting and enriching information, attributing it to the right profile, extracting structured fields, and exposing relationship insights through backend and conversational services.
About 15 minutes to under 3
Shortening the extraction pipeline
The core AI pipeline was taking about 15 minutes. I parallelized asynchronous LLM calls that extracted different schema sections, such as professional and personal fields, and handled mapping and validation in Python. The pipeline then ran in under 3 minutes, enabling interactions closer to real time.
This result belongs to asynchronous extraction and Python processing. It is separate from the Neo4j graph and multi-agent contributions. The workload size, percentile latency, and measurement protocol are not recorded.
Approximately 33% improvement
Attributing information to the right person
Similar or identical names made data-to-profile attribution ambiguous. I improved attribution by approximately 33% versus the prior system, working on profile disambiguation and enrichment quality.
The exact metric definition, sample size, and relative-versus-absolute interpretation are not documented. This is an attribution result, not a 33-percentage-point accuracy gain or a knowledge-graph benchmark.
Multi-agent contribution
Enrichment through cooperating workflows
I contributed to multi-agent enrichment in n8n, integrated Model Context Protocol tools, and contributed RabbitMQ integration and retries. Grafana tracing supported visibility into the workflows.
I also built and deployed Python microservices and Celery workers for background collection, enrichment, insight generation, and AI workloads. Azure OpenAI integrations supported retrieval and conversational services. Exact agent topology and agent counts are not established.
Graph engineering
Relationships that can be queried
I designed and implemented a Neo4j graph of contact profiles and relationships. It supported insights around shared interests, employers, and multi-hop network connections.
This demonstrates graph data modeling and relationship traversal. The available evidence does not establish a production GraphRAG deployment or a causal link between the graph and the attribution measurement.