Focused AI platform engineering first release
A scoped engagement that ships the core outcome first — then iterates with stakeholder feedback and written next steps.
Service
MLOps & AI Platform Engineering
Deploy, evaluate, and govern AI systems in production — with cost, quality, and security controls.
What this means in plain terms
Mak IT Solutions delivers AI platform engineering as a structured studio engagement — discovery, design, implementation, QA, and handover — not a loose collection of freelance tasks.
Deploy, evaluate, and govern AI systems in production — with cost, quality, and security controls.
Stakeholders receive a written plan, demoable milestones, and documentation so internal teams can operate the result confidently after go-live. When adjacent technologies are a better fit, we say so; when this capability is the right path, we staff and deliver it with clear ownership.
Who this is for
If any of these sound like your organization, this engagement is a fit.
- Teams with AI features already in production
- Platform groups standardizing AI delivery
- Leaders who need governance and FinOps
- Companies scaling beyond a single prototype
Problems we solve
Common blockers we address up front in structured delivery.
Unclear starting point
We define the first release and success criteria before build begins.
Delivery risk
Milestone demos and quality gates keep progress visible to stakeholders.
Handover gaps
Documentation and training so your team is not dependent on a single contact.
Tooling confusion
We recommend (or fit) technologies your organization can own long term.
What you get
Concrete delivery areas typically included when organizations engage Mak IT Solutions for MLOps & AI Platform Engineering.
- Scoped AI platform engineering roadmap and success criteria
- Solution design and implementation
- Integrations and data wiring as needed
- QA, launch readiness, and stakeholder demos
- Documentation and team handover
- Optional post-launch iteration support
How we work
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1
Discover goals, constraints, and success metrics for AI platform engineering
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2
Design the approach, UX (where relevant), and delivery plan
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3
Build in iterative releases with quality gates and demos
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4
Launch, measure outcomes, and hand over with clear ownership
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Samples
How engagements usually look
Representative engagement patterns for MLOps & AI Platform Engineering — scope and timeline vary by goals and constraints.
AI platform engineering modernization
Upgrades to performance, UX, and integrations that make an existing capability easier to operate and grow.
AI platform engineering for scale
Architecture and delivery work to support higher usage, clearer admin operations, and safer iteration.
FAQ
Questions about MLOps & AI Platform Engineering
Clear answers before you kick off.
Discovery, solution design, implementation, QA, and launch support tailored to AI platform engineering. Decisions are documented and handed over so your team can operate the result confidently.
Packages
Add AI to your business
Give your team (or customers) a smart assistant that knows your company — without replacing the people who still need to make the final call.
AI Trial
Try AI on one real business problem before you invest more.
From $2,500 · 2–4 weeks
See what’s different →
Company Knowledge Assistant
Ask questions in plain language — get answers from your own files, policies, and FAQs.
From $5,000 · 4–8 weeks
See what’s different →
AI Helper for Your Team or Customers
A helpful AI inside your website or tools — drafts, answers, and guides people step by step.
From $6,500 · 6–10 weeks
See what’s different →
Customer Chatbot
A chatbot that answers from your knowledge base — and connects customers to a real person when needed.
From $4,500 · 4–8 weeks
See what’s different →
Ready to discuss MLOps & AI Platform Engineering?
Share your goals and timeline — our team will propose a clear delivery path with milestones.