Blueprints and engineering strategies for scaling enterprise AI from pilot to production.
74% of enterprise RAG teams have no re-ranking layer. Cross-encoder re-ranking delivers 28-31% better answer relevance with under 120ms added latency. Here is how to evaluate and implement it.
Autonomous AI agents now deploy infrastructure, retry failed tasks, and spend against a budget with no approval step in between. Most enterprises still have no automatic way to halt one mid-task before a loop or a bad decision turns into a five-figure bill.
71% of RAG teams start with fixed-size chunking. 61% change their chunk size within 90 days of launch. Here is the chunking strategy evaluation framework that prevents the rework.
Most QA programs test AI agents in isolation, skipping agent-to-agent handoffs. Here is how to test multi-agent pipelines before production.
AI-led delivery pods have already made some GCCs 30% flatter, and could remove half of middle management by the end of 2026. Job descriptions, pay bands, and employment records rarely catch up. Here is where that gap turns into real legal and retention risk.
67% of enterprise RAG teams select a vector database based on team familiarity or vendor marketing rather than use-case benchmarking. 54% switch within 18 months. Here is the evaluation framework that prevents the switch.
Enterprises run AI agents on persistent API keys, tokens, and service accounts, and most security teams cannot say where all of them live. Here is where agent credentials actually go missing in 2026.
Most performance test suites still simulate one request and one response. Production AI agents fail through rate limits, retries, and tool-call fan-out that most QA pipelines never test for before launch.
71% of enterprise agentic AI deployments have no formal access control policy governing what agents can read, write, or execute. When an agent with production credentials makes an unintended action, the investigation takes 3.8x longer than a human-triggered incident. Here is what to lock down before deployment.
No 50-page proposals. We'll tell you which level fits your situation, what a realistic engagement looks like, and what it would cost — in one direct meeting.
Thomas leads 10decoders' AI engineering practice and sits in on the scoping call himself — so the person mapping your engagement is the one who has shipped it before. His teams build and deploy agents for mid-market healthcare and fintech companies, with enterprise grade build experience for clients like IBM, Dedalus and Harris Healthcare. He'll be straight with you about what's worth doing and what isn't.
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