AI and workflow automation
We apply AI and automation where they remove repetitive work and improve decisions, and we build the controls that let you trust them with real systems.
Where AI belongs in production software
Language models are good at reading, summarizing, classifying, and drafting. They are not a system of record, a permission model, or an audit trail. The useful engineering is in connecting the two: letting AI do what it does well while the surrounding software decides what it is allowed to change.
Document and request intake
Extract, classify, and route incoming documents and requests, with people reviewing what matters.
Drafting and summarization
Summaries, first drafts, and suggested responses inside the tools your team already uses.
Triage and classification
Sort tickets, emails, and requests by type, urgency, and owner before a person picks them up.
Agent and tool integrations
Let AI agents call your systems through narrow, well-defined operations instead of broad credentials.
Hosted or local models
Use hosted LLM services or locally run models, depending on data sensitivity, cost, and latency.
Automation without AI
Many workflows need dependable rules and integrations, not a model. We will tell you when that is the case.
Consequential actions need controls
When an AI agent or automated process can change data, infrastructure, or money, access alone is not enough. Knowing who is allowed to act does not tell you whether a specific change should happen, or whether it did what was intended. We design automation with:
- Least-privilege permissions scoped to the task, not the whole system
- Human approval for the actions that warrant it
- Policy checks before execution, not after
- Verification that the intended outcome actually happened
- A record of who or what requested each change, and why
- Limits that stop a process from widening its own scope
It is also the problem our own product, Resource Shell (resh), is being built to address. Learn about resh
How an automation project runs
Map the process
Document how the work happens today, where the time goes, and where errors come from.
Decide what to automate
Separate what software should do on its own, what AI should assist with, and what should stay with people.
Build with controls
Implement the automation with permissions, approvals, logging, and verification in place from the start.
Measure and adjust
Compare the results with the original process, and expand what is automated as confidence grows.
“The automation solution Miller Technology Group built for our accounting department has reduced processing time by 78% and virtually eliminated errors. Their team took the time to truly understand our business processes.”
AI in our own engineering
We use AI-assisted development every day, under the same code review, automated testing, and CI/CD discipline as any other code. Much of our thinking about how to govern AI comes from that practice.
- Automation and AI
- Hosted LLMs
- Local LLMs
- Workflow automation
- Governed execution
- Languages
- Python
- TypeScript
- C# and .NET
- Rust
- Integration
- REST APIs
- Authentication
- Authorization
Related services
Systems integration
APIs and integrations that connect applications, databases, cloud services, and business platforms into one dependable workflow.
Custom software development
Business applications, portals, and SaaS products designed around your real workflows, so the business does not have to bend to generic software.
DevOps, hosting, and support
CI/CD, containerized deployment, hosting, monitoring, and ongoing support that keep software healthy long after launch.
Considering AI for a real business process?
Tell us about the workflow and the systems it touches. We'll help you find where AI helps, where plain automation is better, and what controls the result needs.