AI
AI Integration & Automation
Put AI into the daily work of your organization: assistants rolled out securely, AI that answers from your own documents, and automation connected to the systems your team already uses, all monitored and supported like the rest of your IT.
Integration is an IT and security job
Connecting AI to business data is where the risk and the value both live
An AI model on its own knows nothing about your business. It becomes useful when it can read your documents, your inbox, or your line-of-business data, and that is exactly the point where it can also expose that data. Integration is therefore less about the model and more about identity, permissions, data boundaries, logging, and support.
That is why we treat AI integration as part of managed IT rather than a separate experiment. The same team that manages your accounts, Microsoft 365 tenant, and security controls decides what an AI service can reach, configures it, and watches how it is used.
For organizations with handling rules, the boundary matters most. Under DFARS 252.204-7012, cloud services that store or process covered defense information must meet security requirements equivalent to the FedRAMP Moderate baseline, which excludes most consumer AI tools for that data. Public agencies carry records and privacy obligations of their own. We design integrations so controlled data only reaches services permitted to hold it.
Scope
What we integrate and automate
From configuring tools you already own to connecting AI into your own systems.
AI assistant rollout
Deploy assistants in platforms such as Microsoft 365 after cleaning up permissions, sharing, and sensitivity labels, then pilot and expand.
AI over your documents
Question-and-answer over a defined set of your documents, with citations to sources and the same access rules as the files.
Email and form triage
Summarize, classify, and route inbound messages and requests, with people handling anything that carries consequences.
Document data extraction
Pull structured data out of invoices, forms, and reports into the systems that need it, with review of exceptions.
Drafting assistance
Templates and prompts for routine correspondence, reports, and proposals, reviewed by staff before anything is sent.
System-to-system integration
Connect AI services to line-of-business applications through their interfaces, rather than copying data by hand.
Secure configuration
Business data protections, retention settings, and administrative controls configured on every AI service you adopt.
Monitoring and support
Integrations are documented, monitored, and maintained as part of your managed environment.
The problem
What we prevent
The integration failures that turn a productivity project into an incident.
Assistants surfacing overshared files
Permission problems that were invisible for years become one question away once an assistant can search everything a user can reach.
Restricted data sent to the wrong service
Client, contract, or regulated information reaches an AI service that was never approved to hold it.
Orphaned automations
A workflow built by one person breaks silently when an interface changes and nobody knows it exists.
Unreviewed output
AI-drafted content reaches customers or records without a person checking it where it matters.
Our approach
How an integration project runs
Foundations first, a small group next, then everyone.
- 01
Scope
Define the task, the data it needs, the systems involved, and how success will be measured.
- 02
Secure
Fix permissions and data boundaries, and configure the AI service's protections and logging.
- 03
Pilot
Deploy to a small group, measure against the agreed goal, and adjust.
- 04
Operate
Roll out, train, document, and support it as part of your managed environment.
Fit
Who this is for
- Organizations licensing AI assistants that want them rolled out without exposing data
- Teams spending hours on repeated reading, drafting, searching, or re-keying
- Government contractors and public agencies that need firm data boundaries
- Businesses that want AI connected to their line-of-business systems
When it may not be the right fit
We would rather tell you up front than sell you something that will not help.
- Residents seeking help with personal AI tools, which we do not provide
- Projects that must process controlled data in a service not authorized for it
Integration questions
How it works in practice
What is involved in rolling out an AI assistant in Microsoft 365?
Licensing is the easy part. Because the assistant works within each user's existing permissions, the real work is reviewing who can reach what: overshared sites and folders, broad sharing links, missing sensitivity labels, and stale accounts. We fix those first, pilot with a small group, then expand with training and a way to report problems.
Can AI answer questions from our own documents?
Yes. The common pattern connects an AI model to a defined set of your documents so answers are drawn from them, with citations back to the source. Done properly, it respects the same access rules as the documents themselves, keeps a log of use, and excludes data that must not be processed by that service.
What kinds of automation do you build?
Repeated, rules-heavy work where AI handles the reading or drafting and a system handles the rest: routing and summarizing inbound email or forms, extracting data from documents into a line-of-business system, drafting standard responses for human review, and moving information between applications through their interfaces.
Who supports an automation after it is built?
We do, as part of the environment we manage. Every integration is documented, monitored, and owned, so it does not quietly break when an interface changes or the person who requested it moves on.
Do people still review what the AI produces?
Where the output affects customers, money, compliance, or people's records, yes. We design workflows with human review at the points that carry consequences, and we are explicit about which steps are automated and which are not.
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