Services / AI Services
AI Services
Production AI in your business — Copilot, ChatGPT, Claude, and Gemini, deployed safely.
Every major AI platform — Microsoft Copilot, ChatGPT Enterprise, Claude for Work, Gemini, and the specialized vertical tools — plus the governance, policy, and training that keep your people safe while they use it. We're tool-agnostic; we recommend whatever actually fits your workflow and compliance posture.
Context
AI Services in Houston — what actually matters.
AI moved from novelty to default office tool in about eighteen months. ChatGPT, Microsoft Copilot, Claude, Gemini, and a long tail of specialized vertical tools are now on employee desktops at every Houston business we visit — sometimes with IT approval, often without. The second category is called 'shadow AI' and it's where most of the compliance and data-exposure risk lives. An employee pasting client data into a free ChatGPT account to speed up a task is, in most regulated industries, a documented data transfer to an uncontrolled third party. The compliance consequences of that transfer (HIPAA, GLBA, FCRA, ABA ethics rules, IRS Publication 4557, state privacy laws) arrive long after the convenience benefit is realized.
Mako's position on AI is not the grown-up-skeptic pose most MSPs still take. AI is useful. It's getting more useful fast. Clients who figure out how to use it safely will outcompete clients who don't. The question is not 'should we use AI?' — by the time leadership asks that, the answer is already yes, just unmanaged. The real question is: which platform fits which workflow, what governance keeps regulated data out of the wrong places, how do we train the people, and how do we measure whether any of it is paying off?
We're tool-agnostic. Microsoft Copilot (both Microsoft 365 Copilot and the standalone Copilot Studio for custom agents), ChatGPT Enterprise, Claude for Work (including the 1M-context Opus tier for long-document work), Gemini for Workspace, Dialpad AI, Perplexity Enterprise, and the specialized vertical AI tools for healthcare, legal, accounting, and engineering workflows. Which one to pick depends on your existing stack, your data-residency requirements, and what the tool actually has to do. A marketing team needing brainstorming help has different needs than a legal team drafting privileged matter notes — and different needs than a healthcare practice running HIPAA-safe intake automation.
Behind platform selection is the governance work that most MSPs skip: an AI Acceptable Use Policy tuned to your industry, shadow-AI discovery so you know what employees are actually using, data-flow mapping so regulated data isn't leaking into free-tier tools, role-based access controls on enterprise AI tenants, prompt-injection awareness training, and incident-response procedures for the specific class of AI-related incidents (accidental disclosure, hallucination published as fact, AI-generated phishing targeting your people). This is the work that makes AI safe to use at scale.
Who this is for
Houston businesses whose people are already using AI — whether leadership knows it or not. Especially clients in Healthcare, Professional Services, and regulated industries where an employee pasting the wrong thing into ChatGPT becomes a compliance problem fast.
What’s included
The full picture.
| Service | What’s included | Benefit |
|---|---|---|
| AI Readiness Assessment | Use-case mapping, data-sensitivity review, licensing analysis, ROI framing, risk register | You stop making AI decisions based on a vendor pitch and start making them based on your actual workflows |
| AI Platform Deployment | Tool selection across Copilot, ChatGPT Enterprise, Claude for Work, Gemini, and vertical tools; licensing, environment prep, rollout waves, user training, post-deploy review | The right AI gets picked for the job and actually gets used — not three overlapping SKUs sitting idle |
| AI Security & Governance | DLP for AI tools, Purview configuration, data classification, guardrails for regulated data (PHI, PII, CUI) | Your people can be productive with AI without putting protected data in front of a public model |
| Shadow AI Discovery | Audit of which AI tools are already in use across your org, account and subscription inventory, risk scoring | You find out what's already happening before it shows up in an audit finding |
| AI Acceptable Use Policy | Written policy tailored to your industry (HIPAA, CMMC, SOC 2), employee acknowledgement workflow, incident-response addendum | Clear rules your team can actually follow — and defensible documentation if regulators come asking |
| Employee AI Training | Role-based sessions (leadership, ops, clinical, legal), what-to-do-and-not-do playbooks, hands-on examples | Your team uses AI confidently and stops pasting sensitive content where it doesn't belong |
The details
What each piece actually looks like.
AI Readiness Assessment
A structured look at what AI can do for your business, where the risk sits, and what it actually costs.
Before you buy licenses for everyone, we sit down with your leadership and operations people and map the real workflows. Which tasks waste the most time? Which ones touch regulated data? Which tools are already being used off-the-books? You walk out with a written report: concrete use cases, a matched tool recommendation from across the major AI platforms, risk register, licensing math, and a phased roadmap.
Full details →AI Platform Deployment
Any AI platform — Copilot, ChatGPT Enterprise, Claude for Work, Gemini, vertical tools — licensed right, permissioned right, and actually adopted by your team.
Most AI rollouts fail because nobody prepared the environment first or picked the wrong tool for the job. We use every major platform in-house — we're not a Microsoft-only shop — so the recommendation comes from real experience, not a partner badge. Whether it's Copilot on your M365 tenant, ChatGPT Enterprise for a legal team, Claude for Work for document-heavy analysis, Gemini for a Google-centric shop, or a vertical tool built for your industry, we handle the full deployment: licensing, environment prep, permissions, policy alignment, rollout waves, and the training that makes it stick.
Full details →AI Security & Governance
The guardrails that keep regulated data from showing up in a public AI model.
Whether or not you've officially deployed AI, your employees are already using it. The question is whether your guardrails can keep PHI, PII, CUI, or client-confidential data from leaking into a prompt history you don't control. We deploy data loss prevention for AI tools, configure Purview for AI-aware classification, and write the incident-response plan for the day something slips through.
Full details →Shadow AI Discovery
Find out which AI tools your people are already using — before a regulator or client does.
Every organization we audit has more AI in the building than leadership realizes. A free ChatGPT account linked to a personal Gmail. A browser extension that summarizes the meeting. An automation tool somebody connected to your CRM. We run a structured discovery: subscription audit, browser telemetry, network signals, user interviews. You get a ranked inventory with risk scores and remediation steps.
Full details →AI Acceptable Use Policy
Plain-English rules that your team will actually follow — and that your auditor can point to.
Policy templates you download from the internet don't fit your industry and don't address the real risks your employees are facing. We write yours — tailored to HIPAA, CMMC, SOC 2, or whatever framework you live under — in language your people can read. Paired with an acknowledgement workflow so you have a record of who signed it.
Full details →Employee AI Training
Your people use AI every day — this is how they use it well and safely.
Live, role-appropriate training sessions. Leadership gets a different session than operations, which gets a different session than clinical or legal. We cover the do's, the don'ts, prompt techniques that actually help, and the red lines tied to your written policy. Each session is recorded and delivered as an internal library your new hires can use.
Full details →Our approach
How ai services actually gets delivered.
- 1
AI Readiness Assessment — before any tool decision
We start with a 2-3 week assessment: documented use-case mapping (which roles do what with AI today, or would benefit from it), data-sensitivity classification (what data can safely enter which tier of AI tool), licensing analysis (what you're already paying for and underusing — M365 Copilot is often on the list), and a risk register specific to your industry. The output is a prioritized recommendation, not a vendor brochure.
- 2
Platform deployment — right tool, right tier, right training
Once the use-cases are clear, we deploy. Tool selection across Copilot, ChatGPT Enterprise, Claude for Work, Gemini, and specialty tools. Environment prep — tenant configuration, data connectors, role-based access, prompt libraries tuned to your workflows. Rollout in waves with real training (not a Loom video nobody watches). Post-deploy review to measure usage and refine.
- 3
Shadow-AI discovery — see what your people are actually using
Free-tier ChatGPT, consumer Claude, unmanaged Gemini accounts, niche AI tools an employee saw on Twitter. We run a shadow-AI discovery using a mix of network telemetry, browser-extension inventory (where deployable), and interviews. The report is a 'here's what's happening' document, not a list of people to fire. Most discovery work ends in a paved path — bring the useful shadow tools into the managed environment, shut down the risky ones.
- 4
AI Acceptable Use Policy + governance
A real AUP tuned to your industry (healthcare AUPs differ from law-firm AUPs differ from CPA AUPs). Role-based access to enterprise tenants. Data-classification rules that describe what can enter which tool. Tenant-level DLP where supported. Quarterly policy review as the underlying tools change. The AUP isn't a shelf document — it's linked from onboarding, reviewed in annual training, and enforced via tenant controls.
- 5
Employee AI training — role-based, plain-English
Generic AI training is useless. A clinician needs different guidance than an IT admin needs different guidance than a client-facing attorney. We deliver role-based training with specific examples from your industry: what to paste, what not to paste, what 'hallucination' looks like in practice, how to verify, how to cite. Phishing-simulation programs now include AI-generated phishing samples because attackers are using the same tools your team is.
- 6
Security + incident response for AI-related events
AI incidents aren't hypothetical — we've worked them. Accidental disclosure of client data into a free-tier tool. An AI-generated quote published as fact that turned out to be a hallucination. A prompt-injection exploit that exposed internal data from a tenant-connected chatbot. Each has an established response playbook. For regulated clients, AI-incident response is now integrated into the overall IR plan alongside ransomware and BEC.
Related case study
Healthcare
Woodlands Family Psychiatry
HIPAA posture across multiple locations, nine clinicians, and clinical-trial data.
Multi-location psychiatric practice in Spring and Conroe. Mako runs the IT that keeps patient portals up, PHI protected, and clinical-trial infrastructure compliant.
How switching works
Four steps. No disruption.
The #1 reason businesses stay with a bad MSP is the fear of switching. Here’s how we make that fear unfounded.
01
Discovery
We learn your environment, your people, and your real pain points. No sales-team script — actual technical conversation.
02
Plan
We audit and deliver a written plan — what stays, what gets replaced, what gets hardened, what the monthly number looks like. No surprises.
03
Transition
We take over day-to-day without disrupting your work. Your current provider's runbook, your access, your vendor relationships — we document every piece before anything changes hands.
04
Running
Proactive support, 24/7 monitoring, quarterly strategy reviews. Your people call, a real person answers. Typically 2–4 weeks from signed agreement to fully operational.
Typical timeline from signed agreement to fully operational: 2–4 weeks. We document everything so if you ever leave, the next provider picks up without starting over.
FAQ
AI Services — common questions.
We're just a small shop — is this even relevant to us?
Especially. A 20-person firm with no written AI policy is exactly the profile that gets burned when an employee pastes a client matter into ChatGPT. The smaller the team, the less margin for one bad move.
Do you resell AI licenses?
Where we have a partner relationship we can procure licenses for administrative convenience. More importantly, we use every major AI platform in-house every day — Copilot, ChatGPT Enterprise, Claude for Work, Gemini, agent platforms, and several vertical tools. Our recommendations come from real usage, not a vendor relationship. We don't take spiffs.
We already have IT in-house — do we need you for this?
Often it's a co-managed fit: your internal team runs day-to-day, we come in on the AI governance and deployment pieces. The compliance-heavy lifting tends to sit outside a typical internal IT scope.
How is this different from just hiring an AI consultant?
Most AI consultants don't understand your IT stack, your compliance regime, or your actual data flows. We do both sides. That's the point of an MSP-led AI engagement.
Industries we do this for
Who relies on ai services most
- Professional ServicesCompliance-heavy B2B firms — investigative, legal, insurance, CPA.
- CPAs & Accounting FirmsIRS Publication 4557 and WISP-compliant IT — built to survive tax season and cyber-insurance questionnaires.
- Law FirmsIT that treats attorney-client privilege the way a bar ethics committee expects — and that keeps you running the day a ransomware gang pivots to law-firm season.
Want to talk through ai services?
Real person, real conversation, no pressure.