AI · Scoped
AI engineering on cloud-hosted frontier models
The frontier models from the leading providers are now capable enough to automate real work: answering from your knowledge base, processing documents, drafting and triaging, and running multi-step tasks as agents. The gap is engineering: grounding the model in your data, keeping it private and secure, measuring whether it actually works, and controlling cost. We build AI on Azure AI Foundry, Azure OpenAI, and direct frontier-model APIs, with the same rigor we bring to cloud security.
What's included
- AI opportunity assessment: which workflows are a fit, the expected value, and the risk
- Solution architecture on Azure AI Foundry / Azure OpenAI or a direct frontier-model API
- Retrieval-augmented generation: ingestion, chunking, embeddings, vector store, and grounding
- Copilots and assistants over your content in SharePoint, Teams, and line-of-business data
- Document and data automation: extraction, classification, summarization, drafting
- Agentic workflows and tool use, including Model Context Protocol (MCP) integrations
- Evaluation harness: test sets, scoring, and regression checks before and after every change
- Guardrails: prompt-injection defenses, content filtering, PII handling, human-in-the-loop
- Identity, private networking, logging, and data-residency design, and no training on your data
- Cost modeling, caching, model routing, and token budgets
- Deployment, monitoring, and a handover runbook
How we work
Scope
A fixed statement of work: what we will do, what you receive, and the timeline. Agreed before any work starts.
Execute
We do the work in your tenant with least-privilege access, with updates at defined checkpoints, not radio silence.
Hand off
Documentation, runbooks, and a walkthrough so your team can operate what we built.
AI Engineering on Frontier Models: common questions
Which models do you use?
Whichever fits the task and your constraints, the leading frontier models are available through Azure AI Foundry, Azure OpenAI, and provider APIs. We design so the model can be swapped as the field moves, and route simple steps to cheaper models.
Will our data be used to train the model?
No. We deploy on enterprise API tiers where your prompts and data are not used for training, with private networking and data-residency controls where required.
How do you know the AI is actually accurate?
We build an evaluation set from your real cases and score every change against it. "It seemed to work in the demo" is not a deliverable, a measured pass rate is.
Do we need Microsoft 365 Copilot licenses?
No. This is custom engineering on the model APIs, separate from Microsoft 365 Copilot. If you also use Copilot, the two are complementary.
Related services
Talk to a senior architect about ai engineering on frontier models
A short call to understand your environment, then a fixed-scope proposal. Based in Denver, Colorado; we work with clients across the US remotely.