Discuss a use case

Services

What we build, and how we engage.

Four kinds of systems, three ways to work together. Every engagement is scoped around a specific use case and ends with something you can inspect.

01What we build

Four kinds of systems.

01

AI agents & workflows

Agents that take actions inside business processes: preparing, checking, routing and drafting work, with the same permissions and approval steps a person would have.

For example

  • An agent that prepares field-team briefings from CRM data and approved content
  • A workflow that triages incoming requests and routes them with a drafted response
  • An assistant that checks documents against an internal checklist before review
02

Custom AI applications

Purpose-built applications for when an off-the-shelf copilot is not enough: a specific interface, a specific workflow, or logic that has to live outside a chat window.

For example

  • A document review workspace for regulatory or medical-legal review teams
  • A decision-support tool that combines structured data with written guidance
  • An internal tool that turns long source material into structured, reviewable outputs
03

Enterprise knowledge

Assistants that answer from your own documents and data (often called retrieval-augmented generation, or RAG), with sources cited and access that follows your existing permissions.

For example

  • A knowledge assistant over SOPs and policies that respects document permissions
  • A search and answer layer over internal reports and research
  • An onboarding assistant grounded in approved training material
04

Integration & deployment

The engineering that makes AI part of real work: connecting to APIs and SaaS tools, data platforms and identity, then deploying with monitoring, logging and a way to roll back.

For example

  • Connecting an agent to CRM, ticketing or document management systems
  • Moving a working prototype into a governed production environment
  • Adding evaluation, monitoring and logging to an AI system already in use

02How we engage

Three ways to work together.

01

Production Readiness Sprint

“Should this be built? And exactly how?”

For
Teams with an approved AI use case, or a stalled pilot, that need to know whether and how it reaches production.
Duration
Typically 2 to 3 weeks
Format
Fixed scope, fixed fee
What happens
  • Interviews with business, IT, security and data owners
  • Process mapping and data source inventory
  • Access, identity and integration analysis
  • Platform choice and target architecture
  • A thin prototype when it removes real uncertainty
You get
  • Target architecture with written decisions
  • Data & access map
  • Risk register
  • Evaluation plan
  • Build plan with effort and team profile
Ends when
You have a clear recommendation: build, adjust or stop.
02

Build & Deploy

“Put it into production.”

For
Teams with an architecture, from a sprint or their own, who want the system running in production.
Duration
Typically 6 to 12 weeks, in milestones
Format
Project with milestones and acceptance criteria
What happens
  • Working software early, inside your environment
  • Integration with your identity, data and business systems
  • Evaluation harness built alongside the system
  • Deployment through your change and approval process
You get
  • A running system in your environment
  • Evaluation harness
  • Monitoring & logs
  • Operating and handoff documentation
  • Knowledge transfer session
Ends when
The system is in production and meets the acceptance criteria in the evaluation plan.
03

Embedded AI Engineering

“Keep it improving.”

For
Teams with AI systems in production, or a queue of use cases, who want senior capacity without hiring.
Duration
Monthly, three-month minimum recommended
Format
Retainer with defined capacity, subject to availability
What happens
  • Improving and extending systems already live
  • Production support and incident follow-up
  • Architecture reviews for new use cases
  • Working sessions with your internal team
You get
  • Senior engineering capacity that owns outcomes
  • Production support
  • A more capable internal team
Ends when
Either side can end it with notice. Everything built stays with you.

Staff augmentation adds hours. This adds someone who owns the result and leaves your team more capable.

03Technology

Platforms we work with.

We go deep in the Microsoft enterprise stack because that is where many regulated organizations run. We choose other tools when the problem calls for them.

Microsoft ecosystem

  • Copilot Studio
  • Power Platform (Power Automate, Dataverse)
  • Azure AI services
  • Microsoft 365

Data platforms

  • Databricks
  • Azure data services
  • Enterprise APIs and databases

Models & engineering

  • OpenAI and Anthropic models
  • Python
  • REST APIs and webhooks
  • Vector search

Listing a platform describes our experience with it. It does not imply a partnership or certification.

04Fit

What we don’t do.

  • Customer-service chatbots at scale
  • Marketing content generation
  • No-code automation packages
  • Legal, regulatory or compliance advice
  • Staff augmentation or body shopping

Have an AI use case stuck between prototype and production?

Tell us what you’re trying to ship. We’ll reply with honest next steps.

Discuss a use case