Services
QueryNow leads AI transformation for enterprises in the US and Europe, from strategy to agents in daily use. Since 2014 we have delivered more than 200 production deployments on AI and the Microsoft platform for companies including Bayer, Takeda, Rockwell Automation and Burckhardt Compression.
Our practices
Where AI pays off first, and the roadmap and operating model to scale it.
We work with your leadership and IT teams to decide which workflows get AI first and what each one has to prove. The result is a ranked roadmap and an operating model your teams can build from.
Agents that work inside your own systems, with a named person approving what they produce.
We build AI agents for one recurring job at a time, such as checking marketing assets against regulations or answering staff questions from your own documents. Each agent runs in your Microsoft 365, Azure or AWS environment under its own identity.
Data that agents can reach with the right permissions, the platform on Azure or AWS, and the security and governance around it.
An agent is only as useful as the data it can reach and the controls around it. We build that layer once on Azure or AWS, and each new agent reuses it.
We run, monitor and improve your agents after launch.
After launch we run your agents in your own environment and report on them every month against the acceptance criteria you signed. When a model or one of your rules changes, we retest before the change reaches your users.
Data controls, AI policy, agent rules and training before Copilot goes company-wide.
A package within our AI Foundation practice, for companies whose Microsoft Copilot rollout is waiting on governance and legal. We do the work in your own Microsoft 365 tenant and hand it to your IT team with a runbook.
An audit of the agents you run, a security foundation for new ones, and ongoing testing.
For companies with agents in Copilot Studio, Microsoft Foundry or custom code. We find out what each agent and its MCP servers can reach and do, set up the controls new agents inherit, and keep testing them as models change.
Planning your first agents? Our enterprise AI agents guide explains how agents work and how to govern them in production, with dated sources on every page.
Enterprise AI agents: the complete guideAlso from our team: a keep, redeploy, right-size or replace verdict for every managed device, from the Intune or Configuration Manager export you already have.
Endpoint refreshHow we engage
Strategy, roadmap and readiness for an AI program.
One scoped workflow at a time, with acceptance criteria agreed in writing before the build starts.
Managed operations after launch, measured against those criteria.
Commercial terms can be tied to the acceptance criteria you sign.
Every engagement starts with one scoped workflow and a written definition of done. Commercial terms are set in the statement of work.
How we engageTalk to the team that builds it and runs it after launch.