Engineering Insights

Technical Thinking From
The Trenches

Deep dives into the architecture decisions, AI techniques, and engineering principles behind our enterprise AI products.

AI EngineeringMar 2025

Building Production RAG Systems: Lessons from 50,000 Legal Documents

A deep dive into the retrieval-augmented generation pipeline behind LEXIS AI — covering chunking strategies, embedding model selection, re-ranking architectures, and the evaluation framework that got us to 96.3% accuracy on blind legal review tests.

RAGLLMVector Search

14 min read

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AI ArchitectureFeb 2025

Agentic AI in the Enterprise: From Chatbots to Autonomous Workflows

How we design multi-agent systems for enterprise clients — covering the Model Context Protocol, agent orchestration patterns, tool use, guardrails, and the human-in-the-loop architectures that make autonomous AI workflows safe for production.

Agentic AIMCPMulti-Agent

12 min read

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AI StrategyFeb 2025

Why RAG Beats Fine-Tuning for 90% of Enterprise Use Cases

A practical decision framework for choosing between RAG, fine-tuning, and prompt engineering for enterprise LLM applications — with cost analysis, latency comparisons, and production considerations from real deployments.

RAGFine-TuningLLM Strategy

10 min read

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AI EngineeringJan 2025

Building a Legal AI That Lawyers Actually Trust

The full technical story behind LEXIS AI — from the multi-layer verification architecture and clause-level RAG pipeline to the evaluation methodology that convinced a top-20 law firm to put AI in their contract review workflow.

The Hybrid Architecture: Why We Pair Spring Boot with Python ML Services

A detailed look at our standard enterprise architecture — Spring Boot 3 for the application backbone, Python services for ML/AI workloads, connected via gRPC. Covering deployment patterns, service boundaries, and when to use each runtime.

Spring BootPythongRPC

11 min read

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AI EngineeringDec 2024

Evaluating LLMs for Enterprise: Beyond Vibes-Based Testing

How we systematically evaluate LLM quality for enterprise deployments — covering custom benchmark design, automated evaluation pipelines, regression testing, A/B frameworks, and the cost/latency/quality tradeoff analysis that drives model selection.

LLM EvaluationTestingBenchmarks

12 min read

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InfrastructureNov 2024

From 12,000 Alerts to Actionable Intelligence: ML-Powered Anomaly Detection

The ML methodology behind HALO — our anomaly detection platform that reduced alert noise by 94%. Covering feature engineering, model architecture, real-time inference, and the correlation engine that transforms 12,000 daily alerts into actionable incidents.

Anomaly DetectionMLHALO

13 min read

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Data EngineeringNov 2024

Vector Databases in Production: Pinecone vs pgvector vs Qdrant

A practical comparison of vector databases for production RAG systems — benchmarking Pinecone, pgvector, and Qdrant on latency, throughput, cost, filtering, and operational complexity. Based on our experience deploying all three across different client projects.

Vector DatabasePineconepgvector

11 min read

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Technical Thinking FromThe Trenches

Building Production RAG Systems: Lessons from 50,000 Legal Documents

Agentic AI in the Enterprise: From Chatbots to Autonomous Workflows

Why RAG Beats Fine-Tuning for 90% of Enterprise Use Cases

Building a Legal AI That Lawyers Actually Trust

The Hybrid Architecture: Why We Pair Spring Boot with Python ML Services

Evaluating LLMs for Enterprise: Beyond Vibes-Based Testing

From 12,000 Alerts to Actionable Intelligence: ML-Powered Anomaly Detection

Vector Databases in Production: Pinecone vs pgvector vs Qdrant

Technical Thinking From
The Trenches