Research from the build floor.
What we have learned shipping 200+ production AI systems since 2014. Every paper is free to read in full. The designed PDF is yours for a work email.
Compliance-grade RAG: Retrieval systems a regulator can audit
Retrieval-augmented generation looks finished in a demo and falls apart in an audit. Independent evaluations find that commercial retrieval-based systems still produce false or misgrounded answers on 17 to 33 percent of hard queries, while the EU AI Act and FDA draft guidance now specify logging, oversight, and credibility evidence that consumer-grade architectures cannot produce. We set out the five engineering disciplines that make a retrieval system auditable, and we show how they run in production inside the compliance scanner we built for a European pharmaceutical regulator.
Read the paper →Past the stall: Why M365 Copilot rollouts plateau and the governed path through
Enterprises bought Microsoft 365 Copilot seats faster than any workplace technology in recent memory, then watched usage flatten within a quarter. The published evidence and our own delivery work point to the same diagnosis: the stall is an implementation problem, rooted in ungoverned data and generic training, not a licensing problem. This paper reads the real adoption studies, names the stall points, and lays out the two-to-three-sprint path we use to take a plateaued rollout to measured, governed adoption.
Read the paper →The agentic enterprise, without the hype
Agent pilots are everywhere and agent production is rare. We draw on McKinsey's 2025 research, Gartner's cancellation forecast, Carnegie Mellon's benchmark evidence, and our own deployment record since 2014 to show that agents succeed in workflows with clear inputs, verifiable outputs, bounded blast radius, and a named human owner, and that they fail as open-ended copilots. This paper gives COOs and CIOs a two-question grid that sorts every candidate workflow into a deployment posture before money is spent.
Read the paper →The enterprise AI pilot paradox: Why pilots multiply while production stalls
Enterprise AI adoption is near universal, yet measured profit impact remains rare, and project abandonment more than doubled in a single year. We argue the cause is structural rather than technical: most pilots are scoped so that nothing about them is falsifiable, so they can neither pass nor fail, only continue. From more than 200 production deployments since 2014, we show how executable acceptance criteria, backed by payment that is owed only when they pass, convert pilot theater into shipped systems.
Read the paper →The EU AI Act operator playbook: What deployers must actually do, and by when
The EU AI Act reached deployers before most of them noticed: prohibited practices and AI literacy duties have applied since February 2, 2025, and penalty provisions have been enforceable since August 2, 2025. The May 2026 Digital Omnibus agreement moved high-risk obligations to December 2, 2027, but Article 50 transparency duties keep their August 2, 2026 date, and Article 26 will hold deployers, not vendors, to oversight, logging, and disclosure duties. This playbook maps each obligation to a concrete system capability a compliance officer or CIO can specify, build, and test before the dates arrive.
Read the paper →The mid-market AI advantage: How $50M to $500M companies out-ship the Fortune 500
MIT researchers count 95 percent of corporate generative AI pilots as failures: no measurable impact on profit and loss. We have built AI systems since 2014, and the failed programs we inherit share one trait, long approval chains. This paper lays out the evidence that mid-market companies hold a structural advantage in getting AI to production, and the operating model that converts that advantage into working systems.
Read the paper →The six-layer AI governance stack: Governance that ships, not governance that stalls
AI governance fails in most enterprises for one reason: it is written instead of built. This paper presents the six-layer governance stack we install in client environments, one two-week sprint at a time, with each layer mapped to its EU AI Act and GDPR duties. It is written for technology and compliance leaders who need governance that holds up in an audit, not a binder that holds up a shelf.
Read the paper →The acceptance criteria sheet we sign before every build
A usable version of the acceptance criteria sheet QueryNow signs with every client on day one, before any code is written. Copy it to turn your next AI project into a pass-fail contract, whoever builds it.
Get the template →The AI build scoping checklist: 20 questions we ask before quoting
The 20 questions QueryNow asks in every scoping session before quoting a fixed-price build, grouped by the six drivers that actually move the price. Answer them before you talk to any vendor and you will know whether the quote you get back is a price or a guess.
Get the template →