AI Engineering & Forward-Deployed Engineers

Forward-deployed AI engineering that gets your AI pilot into production.

Most AI pilots stall between the demo and production, on data access, security review, and the question of who owns the thing afterwards. A forward-deployed engineer from Query Minds works inside your team, finds the workflow where AI actually pays off, and ships it on Azure with evaluation, guardrails, and documentation your staff can run.

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A free 30-minute working session. We review your environment and send a prioritized action plan you keep, whether or not we work together.

Two people working through a problem together at a laptop
Forward-deployed: working inside your team, not beside it.

When you need a forward-deployed AI engineer

These are the sparks that usually bring us into the room.

  • An AI pilot impressed everyone in a demo and never reached production.
  • Security and compliance cannot approve it because nobody can say what data the model sees.
  • Staff paste sensitive documents into consumer chatbots because there is no approved alternative.
  • Vendors sell AI features, but nobody connects them to your own systems and data.

Looking for governed Power Automate flows and Copilot Studio rather than custom AI? See our Power Automate consulting and copilots program.

What forward-deployed AI engineering delivers

Every engagement ships measurable impact, documentation, and enablement.

  • A ranked list of AI use cases scored by value, data readiness, and risk.
  • Production agents, copilots, or document pipelines on Azure OpenAI, grounded in your own data.
  • An evaluation harness, guardrails, and human approval for anything consequential.
  • Code, prompts, evaluations, and runbooks in your tenant, with your team trained to change them.

Delivery method

How a forward-deployed engagement runs

Every week includes demos, telemetry, and regulator-ready documentation.

  1. 01

    Embed & discover

    Sit with the people doing the work, map the workflow, and pick the use case with the clearest payoff.

  2. 02

    Prototype on real data

    A working version against your documents and systems within the first weeks, not slides.

  3. 03

    Production hardening

    Identity, logging, evaluation, cost limits, and human review added before anyone relies on it.

  4. 04

    Handoff & enablement

    Your team learns to run, evaluate, and extend it; we stay available while it beds in.

What a forward-deployed engineer does

A forward-deployed engineer works on your side of the table, inside your workflows and alongside your staff, instead of taking requirements away and returning months later. The first week is spent with the people doing the work: reading the documents they read, watching the steps they repeat, and finding where an AI system would save hours rather than seconds.

From there the engineer builds, demos weekly, and adjusts to what your team learns as they use it. The same person takes it to production, which is where most AI projects fail: connecting to real systems, passing security review, handling the cases the demo skipped, and leaving behind something your team can operate.

Where AI pays off first in nonprofits and regulated teams

The best first projects are high-volume, document-heavy, and checked by a person anyway: intake forms and referrals read and routed, case notes summarized for supervisors, eligibility and screening checks prepared for review, policy and procedure questions answered from your own documents with sources cited, and grant or contract reports drafted from the data you already hold.

These share a pattern that keeps them safe: the AI prepares the work and a person approves it. That keeps accountability where regulators expect it while removing most of the manual effort.

Keeping AI safe with regulated data

Azure OpenAI runs in your Azure tenant, and Microsoft states that prompts and completions sent to it are not used to train its foundation models. We add Entra ID sign-in, private networking where it matters, Purview sensitivity labels on source data, logging of every request, and evaluation tests that run before each change ships.

Consequential actions, such as sending, approving, or updating a record, always wait for a person. The evaluation harness measures accuracy on your own examples, so you know how well the system performs before staff depend on it.

Platforms & accelerators

The AI stack we build on

Azure OpenAIAzure AI FoundryAzure AI SearchCopilot StudioMicrosoft 365 CopilotAzure FunctionsPythonTypeScriptEntra IDPurview

FAQs

AI engineering FAQs

What is a forward-deployed engineer?

A senior engineer who works inside your team and workflows, rather than taking requirements away, and who takes a solution from the first prototype all the way to production. The term comes from AI companies that embed engineers with customers to make their technology work on real problems.

How is this different from your Power Automate and copilots program?

The Power Automate program builds governed automation and Copilot Studio agents on the Power Platform. Forward-deployed AI engineering is for work that needs custom code: agents that call your own systems, document pipelines, retrieval over large or sensitive data sets, and evaluation of model accuracy.

Is our data used to train AI models?

We build on Azure OpenAI in your own Azure tenant. Microsoft states that prompts and completions sent to Azure OpenAI are not used to train its foundation models. We also log access, limit what data each system can reach, and keep sensitive sources labeled with Purview.

Do we need Microsoft 365 Copilot licenses?

Not necessarily. Microsoft 365 Copilot is useful for everyday document and email work, and we help you decide where it is worth paying for. Custom AI on Azure OpenAI is billed by usage instead of per user, which often suits targeted workflows better.

How long is a forward-deployed engagement?

Most run 6-12 weeks from discovery to a production system your team owns. Some clients keep the engineer on a monthly cadence afterwards to add the next use case.

Ready for AI that reaches production?

Tell us about your priorities and we will share a playbook within one business day.

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