AI
AI Consulting, Integration, and Innovation
Practical AI for businesses and public agencies across the CSRA: find the work worth automating, connect AI to your systems securely, and prove value with small pilots before you scale. Delivered by the team that already runs your identity, data, and security.
Why security comes first
AI is only as safe as the data and access underneath it
Most organizations are not short of AI tools. They are short of a clear view of which tasks are worth changing, and of an environment where connecting AI to real business data does not create a new way to leak it. Staff paste client details into public chat tools, assistants surface files that were shared too broadly years ago, and pilots stall because nobody decided who owns the result.
We approach AI the way we approach every other system we run: start from the business problem, understand where the data lives and who can reach it, change one thing at a time, and measure whether it worked. Consulting sets the direction, integration does the engineering, and short innovation pilots keep the whole effort honest about what is actually delivering.
For guidance on managing AI risk we align with the NIST AI Risk Management Framework (AI RMF 1.0, NIST AI 100-1, January 2023), a voluntary framework organized around four functions: Govern, Map, Measure, and Manage, along with NIST's Generative AI Profile (NIST AI 600-1, July 2024).
Three ways we help
Consulting, integration, and innovation
Most engagements use all three in sequence: decide, build, prove. Each can also stand alone.
AI Consulting & Strategy
Readiness assessment, use-case selection, acceptable-use policy, and governance, so AI decisions are made on purpose rather than by default.
For leaders deciding where AI fits
Learn moreAI Integration & Automation
Secure rollout of AI assistants, AI over your own documents, and automation that connects AI to the systems your team already uses.
For teams ready to put AI into daily work
Learn moreAI Innovation & Pilots
Short, measured pilots and workshops that test a specific idea against real work, then scale it or stop it on evidence.
For organizations that want proof before commitment
Learn moreThe problem
Where AI efforts go wrong
The failures we see most are organizational and technical, not a shortage of clever tools.
Shadow AI
Staff adopt public AI tools on their own, and business or client data leaves the organization with no policy and no record.
Overshared data, newly searchable
An assistant that respects existing permissions will surface every file that was shared too broadly, which is most of them in a typical tenant.
Pilots that never finish
Experiments start without a measure of success or an owner, so they neither reach production nor get shut down.
Tools before problems
Licenses are bought for a product demo rather than for a task the organization actually repeats every day.
Controlled data in the wrong service
Contract or regulatory data is processed by a service that was never authorized to hold it.
No one owns the result
Automations are built by an enthusiast, then break when that person moves on, because nothing was documented or monitored.
Our approach
How an AI engagement runs
Decide, secure, prove, then scale. Each step produces something you keep.
- 01
Discover
Find the repeated tasks worth changing and the data they depend on, and rank them by value and risk.
- 02
Prepare
Fix the foundations first: permissions, sensitivity labels, identity, and an acceptable-use policy.
- 03
Pilot
Run a small, time-boxed pilot with real users and an agreed measure of success.
- 04
Integrate and operate
Roll out what worked, connect it to your systems, and monitor, document, and support it like any other system.
Fit
Who this is for
- Businesses across the CSRA that want AI to save staff time without creating a data exposure
- Government contractors that need to keep controlled information out of unauthorized AI services
- Cities, counties, and public agencies exploring AI with records and public-trust obligations
- Organizations rolling out AI assistants in Microsoft 365 that need their permissions cleaned up first
- Leadership teams that want a written AI policy and a realistic roadmap
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 or home computers, which we do not provide
- Organizations looking for AI to replace a process nobody has defined yet
What we claim, and what we do not
We do not publish client names, results, or savings figures, and we do not promise that AI will produce a particular return. Pilots exist precisely so that value is measured in your environment rather than asserted in advance.
Platform names on this site are examples of tools organizations commonly use, not statements of partnership or certification. Whether a given AI service may process a given category of data depends on your contracts, regulations, and that service's terms and authorizations; we help you work that out, and your counsel and contracting officers have the final word.
AI questions
What organizations ask us about AI
We are a small organization. Is AI worth our time yet?
Often, yes, but not everywhere. The useful question is which specific, repeated tasks eat staff time: drafting, summarizing, searching for documents, re-keying data between systems. We start there, run a small measured pilot, and keep only what earns its place. If nothing clears the bar, that is a legitimate answer too.
Our staff already use public AI chat tools. Is that a problem?
It can be. Anything pasted into a consumer AI service leaves your control and may be retained under that service's terms. The fix is rarely a blanket ban, which people work around. It is an acceptable-use policy, an approved tool configured with business data protections, and a short explanation of what must never be pasted anywhere.
Will an AI assistant expose files people should not see?
Assistants built into platforms such as Microsoft 365 generally work within each user's existing permissions, so anything a user can already open, the assistant can surface and summarize. Organizations with years of overshared folders discover that quickly. Cleaning up permissions and sensitivity labels before rollout is part of how we deploy.
We handle controlled unclassified information. Can we use AI at all?
Possibly, but only within your contract's rules. Under DFARS 252.204-7012, cloud services that store or process covered defense information must meet security requirements equivalent to the FedRAMP Moderate baseline. That rules out most consumer AI tools for that data. We help you decide which data may touch which AI service, and keep controlled information out of the rest.
Do you build custom AI applications or only configure products?
Both, depending on what the problem needs. Many needs are met by configuring and securing tools you already license. Others need integration work: connecting an AI model to your documents or line-of-business systems through their interfaces, with access control and logging. We recommend the smallest thing that solves the problem.
Find out where AI would actually help
Bring the tasks that eat your team's week. We will tell you which are worth changing, what needs to be secured first, and how to prove it with a small pilot.