Real deployments. Measured results.

The Problem

A 200-person insurance company's adjusters were spending 45 minutes per claim on document review, classification, and data extraction — before they could begin actual claims assessment.

The Solution

An autonomous AI agent system that reads incoming claims, classifies by type and complexity using ML signals, extracts 23 structured data fields, and routes to the correct adjuster team — with full audit trail and human escalation for edge cases.

The Problem

Associates were spending 4-6 hours per matter researching relevant case law, precedents, and contract clauses across 1.8 million pages of archived documents.

The Solution

A RAG system with hybrid search (vector + BM25), cross-encoder re-ranking, and source attribution — giving associates the top 5 relevant precedents with exact citations in under 2 minutes.

The Problem

A financial services firm was evaluating a $300K fine-tuning project to 'teach' their LLM proprietary risk assessment frameworks across 12 scoring dimensions.

The Solution

A 3-week RAG POC over their existing risk documentation — achieving the same output quality as the proposed fine-tuned model at $40K total cost, with the ability to update frameworks daily without retraining.

The Problem

BD and strategy teams were spending 3-4 hours every Monday on manual market research: checking hiring signals, tracking funding, assessing capability fit, and scoring leads — all from scattered sources.

The Solution

The Xenith Intelligence Suite: four connected modules (Xenith Radar, Xenith Verdict, Xenith Capture, Xenith Pursuit) running an automated weekly pipeline that delivers a scored market report, GO/NO-GO verdicts, and a prioritized lead queue by 9am Monday.