Perspectives from the front lines of enterprise AI.
Research-backed insights, implementation patterns, and lessons from real-world transformation programs.
From Days to Minutes: Automating Prior Authorization with Hybrid AI Agents
Prior authorization is still stuck in fax-driven, manual workflows that delay patient care. We built a hybrid AI agent system that automates key steps and cuts turnaround time from days to minutes for straight-through cases.
Proprietary or Self-Hosted LLMs: Which Is Right for Your Business?
Learn when proprietary LLMs or self-hosted models are the right choice for your business, from cost and security to scalability and customization.
Testing Multitask AI Performance in Real Mortgage Workflows
Mortgage work is not one clean task at a time, it is a chain of checks, calculations, and validations on the same file. This piece tests whether one model can handle that full sequence reliably, and shows where accuracy drops when reasoning-heavy steps enter the mix.
Benchmarking AI for Real-World Mortgage Tasks
MortgageBench evaluates how well AI models handle real-world mortgage tasks, from income verification to DTI calculations, highlighting strengths and gaps.
The Fast-Pass to Accelerated Claims Processing: A Case Study in Workflow Intelligence and Automation
Claims do not have to crawl through spreadsheets, emails, and re-keyed forms. A multinational insurer redesigned the full claims journey and is aiming for lower processing costs, faster resolutions, and a smoother customer experience.
Mastering Document Intelligence: Raw LLM Vision vs. Specialized Approaches like OCR
Blurry photos, bad lighting, half-handwritten forms, document extraction gets messy fast. See what changes when teams rely on vision-only LLMs versus an OCR plus LLM approach, and how the tradeoffs show up in cost and accuracy.
Navigating the Unknown: Three Principles for Realizing AI Ambitions
AI success is rarely linear. This article breaks down three practical principles for ensuring progress: committed executive sponsorship, a single metric that matters, and a team that can adapt quickly.
3 Principles for a Successful Legacy Data Stack Migration
Most migrations fail for predictable reasons, misalignment, shallow domain knowledge, and delayed validation. These three principles focus on getting clarity early, understanding the business logic before rewriting it, and validating continuously so you do not discover problems at the finish line.
AI You Can Trust: How to Mitigate Hallucinations and Keep LLMs Grounded
Confident-sounding wrong answers can cause real problems in production. Learn practical ways to reduce hallucinations by grounding responses in trusted sources and validating outputs before they reach users.
Is Your Data Strategy Future-Proof? How to Take Full Ownership of Your Data with Open Table Formats
Platform lock-in gets expensive when your data cannot move with your business. Open table formats like Iceberg, Delta Lake, and Hudi help teams keep ownership, stay interoperable, and scale governance over time.
AI benchmarks, what do they mean for businesses?
A model can score high on benchmarks and still struggle with real workflows. This breaks down what benchmark results do and do not tell you, and what to check before committing to a model in production.
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