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Applied AI & Agents

AI that does the work, not just the demo.

We build AI agents and assistants grounded in your data, connected to your systems, and measured against real outcomes, so they make it past the pilot and into daily use.

Find your first AI use case See how we work

// the problem

Where AI projects get stuck

We don't know where to start.

There are a hundred ideas and no way to tell which will pay off.

The pilot worked, but nobody uses it.

It isn't connected to the tools people already live in.

We can't trust the answers.

There's no grounding in company data, no evaluation, and no guardrails.

Security and compliance said no.

Data handling, access control, and auditability weren't designed in.

// what we deliver

What you get

AI opportunity assessment

A short engagement to find and rank AI use cases by value and feasibility. You leave with a prioritized roadmap and a recommended first build.

AI agents & copilots

Agents that take actions: they look things up, fill in forms, route requests, and draft responses, using your systems' APIs with a human in the loop where it matters.

Knowledge assistants

Assistants that answer questions from your documents and data, with citations, so people can check the source.

Integration into existing tools

AI delivered where your team already works: Teams, your web apps, your ticketing system, or your back-office workflows.

Evaluation, guardrails & monitoring

Test sets, quality metrics, content safety, access control, and usage monitoring, so you know it works and keeps working.

Cost & performance tuning

The right model for each task, caching, and usage limits, so quality goes up while cost per request stays predictable.

// examples

What it can look like

Customer support

Triage tickets and draft replies, escalating to a person when it's unsure.

Operations

Pull data from several systems and prepare a daily summary or exception report.

Sales

Research accounts and draft proposals from your past wins.

Internal knowledge

Answer policy, HR, or IT questions from your own documentation.

// how an engagement runs

From first call to production

  1. 01

    Discover

    Pick one high-value use case and define what “working” means in numbers.

  2. 02

    Prototype

    A working version on your real data in 2–4 weeks.

  3. 03

    Harden

    Add evaluation, guardrails, security, and integration with your systems.

  4. 04

    Launch & improve

    Roll out to users, monitor quality and cost, and iterate.

// technologies

Tools we use every day

  • Microsoft AI Foundry
  • Azure OpenAI
  • Microsoft Agent Framework
  • Azure AI Search
  • Anthropic Claude
  • Model Context Protocol
  • Python
  • .NET
  • TypeScript

// faq

Questions we hear

Is our data used to train the models?
No. We use enterprise model services where your prompts and data aren't used for training, and we keep data inside your cloud tenant where possible.
Which AI model do you use?
Whatever fits the job. We choose models based on quality, cost, latency, and your compliance needs, and design so they can be swapped later.
How do we know it's working?
We define success metrics up front and build an evaluation set before launch, so quality is measured, not assumed.

// also from enligh10

Most projects touch more than one of these

Cloud Infrastructure

Azure environments that are secure, cost-aware, and built to scale. Landing zones, migrations, infrastructure-as-code, and the monitoring to keep it all healthy.

Explore cloud infrastructure

Development & Integration

Custom applications, APIs, and integrations that connect your systems so data moves where it needs to, without manual re-keying or brittle scripts.

Explore development & integration

Ready to move past the AI pilot?

Bring one process that eats your team's time. We'll tell you whether AI can realistically help, and what it would take.

Find your first AI use case or email info@enligh10.com