Atomize AI: Research Intelligence

The Problem with Research Aggregation

I wanted to track AI research across arXiv, Anthropic, and OpenAI, but existing tools either cost too much or didn’t connect related findings. Atomize AI evolved from Atomize News, which focused on RSS feeds and general tech aggregation. The shift to academic and corporate research sources required deeper synthesis capabilities. I needed entity extraction that understood directional relationships, like distinguishing “Google acquired DeepMind” from “DeepMind acquired Google.” I also wanted cost-efficient LLM inference without sacrificing quality.

Multi-Model Architecture with Groq LPU

I built a 10-model routing system across Groq and OpenAI that selects models based on content complexity. Groq’s Language Processing Units deliver 76% cost savings compared to OpenAI-only inference while maintaining quality for straightforward summarization and entity extraction tasks. OpenAI handles nuanced analysis requiring deeper reasoning. This hybrid approach processes research papers, extracts entities with confidence scores, and builds a knowledge graph that tracks relationships between concepts, organizations, and researchers across sources.

spaCy NLP Microservice for Relationship Verification

The core challenge was entity directionality. LLMs alone can hallucinate relationships or reverse subject-object positions. I deployed a spaCy NLP microservice on Railway ($5/month) that analyzes grammatical dependencies to verify relationship accuracy. It responds in 55ms and solves the “who acquired whom” problem by validating subject-verb-object structures. Verified relationships receive a 20% confidence boost in the knowledge graph, creating a reliable foundation for synthesis.

Progressive Pyramid Visualization

Research synthesis needs hierarchical clarity. I implemented a progressive pyramid that layers findings from specific papers (base) to cross-source patterns (middle) to emergent insights (apex). The knowledge graph feeds this structure, with pgvector similarity search connecting related entities across documents. Users navigate from individual papers through thematic clusters to meta-level trends without losing provenance.

Quality Evaluation with LangSmith

I integrated LangSmith with 5 evaluators measuring factuality, coherence, relevance, business impact, and format adherence. Each synthesis run generates scores that guide model selection improvements and prompt refinement. This feedback loop surfaces when the cheaper Groq models suffice versus when OpenAI’s reasoning depth matters. Citation formatting uses [N] markers that expand to [1],[2],[3] references, maintaining academic rigor.

Observability with OpenTelemetry

I instrumented the pipeline with OpenTelemetry and SigNoz for distributed tracing. Every research ingestion, entity extraction, and graph update generates spans showing latency, model selection rationale, and confidence score evolution. This visibility exposes bottlenecks like slow spaCy calls or inefficient vector queries, guiding optimization efforts. Prometheus metrics track cost per synthesis and accuracy trends over time.

What I Built

Atomize AI aggregates academic and corporate AI research, extracts entities with grammatical verification, and builds a knowledge graph with confidence-scored relationships. The progressive pyramid synthesizes findings across hierarchical levels while maintaining citation integrity. Multi-model routing balances cost and quality, and observability tooling ensures reliability. It transforms scattered research into connected intelligence.