AI Business Integration
Put AI to work on your data, under your rules. We help businesses and engineering teams choose, deploy, and secure AI tools, from Copilot and ChatGPT to private models on hardware you own, without sending sensitive information somewhere it was never approved to go.
- Service
- AI Business Integration
- Covers
- Assessment, platform selection, deployment, security, and ongoing support
- Serves
- Onsite in New Mexico; remote engineering nationwide
Four Directions helps design and implement technology that supports security and compliance objectives. We do not provide legal advice, and we do not assess or certify compliance.
The question isn’t whether to use AI. It’s who controls it.
Your staff are probably using AI tools already, some approved and some not. The tools themselves are easy to get. The hard part is knowing what data is going into them, where it ends up, who can see the answers, and whether any of it fits the contracts and regulations you already work under.
Good AI integration starts with those questions, then picks the tools. That order is what keeps a productivity project from turning into a data-handling problem.
Where AI earns its keep
Most of the value is in ordinary work your people already do. These are the areas where businesses tend to see a real return, with a person staying responsible for the result.
Drafting and summarizing
Proposals, reports, meeting notes, and RFP responses get a fast first draft. Someone who knows the subject reviews it before anything leaves the building.
Answers from your own documents
Search policies, procedures, specifications, and past project files in plain language, limited to what each person is already allowed to see.
Engineering and development
Code assistance, documentation, test generation, and analysis scripts, set up so source code and design data stay where your policies say they belong.
Data and reporting
Turn exports and spreadsheets into summaries and first-pass analysis, with the numbers checked by someone accountable for them.
Service and support
Draft responses and sort incoming requests so your team spends its time on the problems that need a person.
Workflow automation
Connect AI to the systems you already run, with firm limits on what it can read and change, and human approval where it matters.
Where it doesn’t
Decisions that need a named person to be accountable. Output nobody can check. Processes that were broken before AI touched them. We’ll say so when a use case falls into one of these, because automating a bad process only makes it fail faster.
The right platform for the workload
There is no single right AI platform. We are not tied to one vendor, and the recommendation follows your data, your budget, and what your people actually need to do.
| Approach | Where your data is processed | Effort to set up | How costs scale | Usually fits |
|---|---|---|---|---|
| AI built into your productivity suite | The suite vendor’s cloud, under your existing agreement | Low to moderate; file permissions often need cleanup first | Per user, per month | Organizations already standardized on Microsoft 365 or Google Workspace |
| Business AI assistant | The AI vendor’s cloud, under business terms | Low | Per user, per month | General drafting, research, and analysis |
| AI connected to your systems | The AI vendor’s cloud, called from your applications | Moderate to high | By usage | A specific, repeated workflow worth automating |
| Private models on your hardware | Your hardware, on your network | High | Hardware up front, then power and upkeep | Data that can’t leave your environment, or steady heavy use |
| Hybrid | Split by data type and task | Moderate to high | Mixed | Teams with both routine work and sensitive work |
Regulated data needs a different conversation
If you handle Controlled Unclassified Information, export-controlled technical data, or protected health information, most consumer AI tools, and many business ones, are not an appropriate place for it.
Whether a particular service is appropriate depends on its government authorization, its contract terms, where it processes and stores data, and how it fits inside the system boundary you’ve documented. This is the kind of environment we help defense and engineering organizations build as they work toward CMMC and NIST SP 800-171.
We help you decide what can go where, and we can build environments, including fully on-premises options, where sensitive work can use AI inside the boundary you already protect. Your assessor and your counsel make the compliance determinations; we design and implement the technology that supports them.
| Consumer AI (free or personal plans) | Business AI plan | Government-authorized cloud service | Private model inside your boundary | |
|---|---|---|---|---|
| Public information | Allowed: Yes | Allowed: Yes | Allowed: Yes | Allowed: Yes |
| Internal business information | Not appropriate: No | Conditional: Yes, with a written policy | Allowed: Yes | Allowed: Yes |
| CUI, export-controlled data or PHI | Not appropriate: No | Not appropriate: Generally no | Conditional: Possibly, if its authorization and contract cover your data | Conditional: Possibly, inside your documented boundary |
Security comes before rollout
AI tools inherit whatever access and habits already exist in your environment. These are the controls we put in place before anyone is handed a new tool.
- Fix oversharing first. AI search surfaces everything a user can technically open, including files that were never meant for them. Cleaning up permissions on shared drives and sites is usually step one.
- Company identities only. Single sign-on and multifactor authentication on every AI tool, and no personal accounts for company work.
- Rules people can follow. A short written policy that says which kinds of information may go into which tool.
- Vendor terms, read before purchase. Data retention, use for model training, processing location, and the administrative controls you actually get.
- Visibility. Records of who used what, wherever the platform supports it.
- Limits on automation. AI that can take actions gets only the access it needs, and a person approves anything consequential.
- Designed for hostile input. Email, web pages, and documents can carry hidden instructions aimed at AI tools. Integrations are built with that in mind.
How an engagement works
- Assess How your team works, what data you handle, and which AI tools are already in use, approved or not.
- Pilot A small group, one defined use case, and a clear test of whether it actually helps.
- Integrate Roll out what worked: licensing, identity, configuration, and connections to your systems.
- Secure Permissions, policy, logging, and documentation that fit your obligations.
- Support Ongoing administration, help for your users, and keeping up as the tools change.
Every engagement is scoped to your environment. The first conversation is short and doesn’t cost anything: it’s for understanding what you want to accomplish and whether we’re the right fit. Assessment, design, and implementation are scoped and quoted before work begins.
Hardware for AI workloads
Cloud AI tools run fine on the computers you already have. Running models yourself is different: it takes GPU memory, fast storage, and cooling that standard business machines don’t have. We specify and source workstations and servers for inference and development work, sized to the models you actually plan to run rather than the biggest box available.
Questions we hear
Will our data be used to train AI models?
It depends on the product and the plan. Many business and enterprise plans exclude customer data from model training by default; many free and consumer plans don’t. Terms change, so we review the current terms for the specific products you’re considering.
Can we use AI with CUI?
Possibly, in an environment that is appropriate for it. That depends on the service’s authorization and contract terms, your documented system boundary, and how your assessor views it. Public consumer tools are generally not appropriate. We can help you identify options, including models that run entirely on your own hardware.
Our staff already use ChatGPT on their own. What should we do?
That’s common, and it’s a useful starting point. Find out what people are using and what for, then give them an approved option that’s at least as convenient, along with clear rules about what information can go into it.
Should we buy an AI tool or build something custom?
Usually buy first. Off-the-shelf tools cover most drafting and research needs. Custom integration makes sense when a specific, repeated workflow justifies the cost of building and maintaining it.
Do we need new hardware?
Not for cloud-based AI tools; a reasonably current computer and browser are enough. Running models on your own equipment does need capable hardware, and we size it to the workload.
Do you work outside New Mexico?
Yes. Onsite work is centered in New Mexico. Assessment, design, and most implementation can be done remotely for organizations anywhere in the United States.
Start with a conversation
Tell us what you want AI to do and what kind of information your business handles. We’ll tell you plainly what’s practical, what it involves, and whether we’re the right people to help.