AI agents that take one bounded job off your team, and show their work

An agent is software that notices work arriving, decides what to do within rules you set, uses your systems to do it and asks a person when it should. We build agents one job at a time, on Copilot Studio, Azure, Claude or a model running on your own hardware, and every one ships with a safety gateway and a log of everything it did.

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What we cover

What an agent isNot a chatbot: software that acts
How we build themOne job, a pilot on real documents, then production
Where they runCopilot Studio, Azure, Claude or your own hardware
Safety and oversightA safety gateway, and a log of everything

What an agent is

Not a chatbot: software that acts

A chatbot waits for a question. An agent watches an inbox, a folder, a portal or a schedule, and acts when something arrives. It reads the document, pulls out what matters, checks it against rules or other systems, takes the next step, filing, updating, drafting or notifying, and records what it did and why.

The useful agents we have built each do one bounded job. One logs in to five government procurement portals each morning, reads about 2,000 listings and emails a short list of the 20 to 50 worth quoting. One compares two versions of a product label and returns an audit-ready list of every change. One answers questions about FDA guidance, approval letters and labeling rules with a citation to the source passage. None of them replaces the person who decides; each removes the hours of reading and retyping that come before the decision.

How we build them

One job, a pilot on real documents, then production

Every engagement starts with a discovery sprint on one job with a clear measure of success: hours given back, errors caught, a backlog cleared. We then build a pilot that runs on your real documents, with a reviewer checking every result, and we measure it against the way the work is done today.

An agent reaches production only when the pilot's results justify it. In production it has the least access it needs, a person in the loop for the cases it is unsure about, and a full activity log. We keep tuning it as your documents and processes change, and we give it more independence only as its record earns it.

What we do

A discovery sprint that picks the job and the measure
A pilot scored against the way the work is done today
Least-privilege access, and a reviewer for uncertain cases
Tuning after go-live as documents and rules change

Where they run

Copilot Studio, Azure, Claude or your own hardware

We choose the platform from the job, not the other way round. Copilot Studio suits agents that live in Microsoft 365 and Teams and reach business systems through Power Automate. Azure Functions and Azure AI Document Intelligence take over when the processing outgrows low-code, which is why we rebuilt the label-comparison agent on them. Claude suits research and document work that must cite its sources; our Claude partner page shows how a Claude implementation runs from use case to handover.

When documents must not leave your premises, a model on your own hardware can do the work. The procurement agent makes every model call on one desktop AI system in our office, and a 30B model there runs 11 times faster than a 120B model with the same verdicts. Many agents combine tiers: a local model for sensitive extraction, a cloud model for the reasoning that needs it. Our Hybrid and Edge AI page explains how we decide.

What we do

Copilot Studio and Power Automate for work in Microsoft 365
Azure Functions and Document Intelligence for heavy processing
Claude for research and document agents that cite their sources
Local models when the data must stay on your hardware

Safety and oversight

A safety gateway, and a log of everything

An agent that reads email and documents will one day read something written to manipulate it. Every agent we ship sits behind a safety gateway and acts only through the accounts and tools it has been given; the certificate-intake agent, for example, screens every PDF for prompt injection before it reads it. Everything an agent does is logged: what arrived, what it decided, which tools it called and what it changed, so a reviewer or an auditor can follow any result back to its source.

For agents in Microsoft 365, Princeton Sentinel adds oversight across the tenant: a risk score for each agent, alerts on oversharing and stale access, and screening of prompts for injection attempts.

What we do

Input screened before the agent acts on it
Least-privilege accounts and tools
An activity log of every decision and tool call
Tenant-wide oversight with Princeton Sentinel

Case studies

AI Agents in practice

Client names are withheld. We are happy to share details on a call.

All case studies

Internal project
2,000 → 50 a day
Public-sector sales
AI Consulting
Procurement triage on a local 30B model
Logs in to five government procurement portals each morning, reads about 2,000 listings, parses the bid documents and emails a short list of the 20 to 50 worth quoting. Every model call runs on one desktop AI system in our office.
- 2,000 listings down to about 50 a day
- 11× faster than a 120B model, same verdicts
Read the case study →
In production
Hours → minutes
Food manufacturing
AI Consulting
Document intake agent for a national food cooperative
A Copilot Studio agent reads certificates of analysis as they arrive in the quality team's inbox, extracts 14 fields with AI Builder, checks them, files the PDF in SharePoint and posts a summary in Teams. Fields it is unsure about go to a person instead of being guessed.
- Hours of daily filing down to minutes
- 14 fields per document, validated
Read the case study →
Internal project
Cited answers
Food & beverage compliance
AI Consulting
FDA regulatory research agent built on Claude
Food and beverage teams spend hours each week checking FDA sites for regulatory changes. This agent ingests FDA guidance, approval letters and labeling requirements, answers questions with citations to the source passage, compares documents and drafts summaries for review. Built on Claude Sonnet.
- Every answer cites its source
- Submission summaries drafted for review
Read the case study →
Delivered
Every change, flagged
Food manufacturing
AI Consulting
Label comparison agent for a national food cooperative
Compares two versions of a product label, highlights every change, annotates the PDF and returns an audit-ready list of differences. We moved it from Copilot Studio to Azure Functions when the document processing outgrew the low-code platform.
- Audit-ready differences in one chat turn
- Rebuilt from low-code to cloud functions
Read the case study →
Proof of concept
Invoices → rows
Hospitality
AI Consulting
Back-office agent pilots for a luxury resort
A resort with separate food and beverage, retail, spa and golf operations asked where agents could take paperwork off its staff. We built pilots that turn supplier invoices into structured spreadsheets and check supplier documents for missing or mismatched values.
- Invoices turned into clean spreadsheet rows
- Mismatches highlighted for review
Read the case study →

Questions

Common questions

What kind of work suits an AI agent?

Work that arrives in a steady stream, follows rules most of the time and takes a skilled person time to read: certificates, invoices, bids, labels, regulatory documents, customer changes. If the job needs judgment on every item, an agent can still do the reading and leave the decision to a person.

How do you stop an agent from making things up?

By narrowing the job and checking the output. Agents work from your documents and systems rather than from memory, answers cite their source, extracted fields are validated against rules, and anything the agent is unsure about goes to a person instead of being guessed. The pilot measures how often that happens before the agent reaches production.

Which platform should we build on?

If your people work in Microsoft 365 and the job connects to systems Power Automate can reach, Copilot Studio is usually the quickest route. Heavy document processing goes to Azure, cited research suits Claude, and sensitive documents can stay on a local model. We often combine them in one agent.

Can an agent run without sending our documents to the cloud?

Yes. Capable models now run on ordinary business hardware, and our procurement agent sends no document text to any cloud service. We benchmark your task on a local model against a cloud model before recommending where each step runs.

How do we know an agent is working?

You agree the measure in the discovery sprint, and the pilot reports against it. In production the activity log shows every item, decision and exception, and the reviewer's corrections show where the agent needs tuning.

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