As part of AI consulting and advisory services, ITRex helps you assess whether your data and IT infrastructure are ready for artificial intelligence. Next, we prioritize use cases, choose between buying, embedding, or building AI, run discovery workshops, validate ideas through PoCs, and set up the governance your organization needs to adopt and scale AI responsibly.
ITRex’s AI consulting practice is led by Kirill Stashevsky (CTO, 20+ years in software engineering and intelligent enterprise transformation) and Benjamin Aubron (Gen AI Evangelist, hands-on across RAG systems, agentic architectures, and rapid prototyping).
A focused AI readiness assessment typically takes 2–6 weeks and costs $15,000–60,000 depending on scope and company size. A strategy engagement with a PoC falls in the $40,000-120,000 range and runs 6–12 weeks. For organizations exploring a specific use case, an outcome-led discovery workshop takes half a business day and costs $7,000-15,000.
92% faster sales onboarding. Lead qualification time cut by up to 80%. 60+ analyst hours saved per month. 100M+ verified contacts added to a global intelligence platform. These are results from real ITRex AI consulting engagements—across manufacturing, financial services, healthcare, and retail.
Before you invest in AI, you must know exactly where you stand. Our AI consultants audit your data infrastructure, tech stack, team capabilities, and compliance posture—and give you a clear picture of what’s ready, what’s missing, and what to fix first. No guesswork, no wasted budget.
A working AI strategy is a prioritized roadmap tied to business outcomes. Our consultants work with your leadership team to identify the highest-value AI opportunities, set measurable goals, and build a phased plan your organization can execute—either with a vendor’s help or on its own.
Have an AI product idea but not sure where to start? Our AI consulting team helps you define the right scope, validate assumptions with end users, and produce a technically grounded spec—so your development team starts with clarity, not confusion. The same output works as the foundation for investor pitches and funding rounds. Typical discovery: 2–4 weeks.
Strategy done, ideas validated—now make them real enough to show. ITRex’s AI consultants build working AI software prototypes using proven frameworks and pre-built components, cutting development time without cutting corners. The result: a functional, demo-ready artifact your team can test, iterate on, and use to secure stakeholder buy-in.
Not sure if your AI idea will work at scale? Our AI consulting firm builds focused, time-boxed PoCs that validate your concept against real data and real workflows before you commit to full development. We deliver most PoCs within 4–8 weeks. You get evidence, not promises.
Before building a custom AI solution, we determine whether you need one. ITRex’s AI consultants evaluate your existing systems, data foundation, and workflows to identify what’s already available—off-the-shelf AI tools, open-source models, or infrastructure that just needs connecting. You get a clear recommendation: buy, integrate, or build—and what either path will take.
If your focus is Gen AI, we have a dedicated practice for that—with artificial intelligence consultants who have helped companies across manufacturing, logistics, financial services, and healthcare move from Gen AI PoCs to full-fledged systems operating company wide. We can help you with:
Before devising an AI strategy, we need to understand what you’re actually working with. That means auditing your data infrastructure, current AI maturity, team capabilities, and compliance posture—and telling you clearly what’s ready, what isn’t, and what needs fixing before AI implementation begins. If a simpler automation technology solves your problem, we’ll say so. That’s what separates a serious AI consulting firm from one that just sells projects.
Most businesses have ten AI ideas—and a budget for two. As an AI consulting agency, we help you prioritize those ideas by data readiness, implementation risk, and ROI potential. Next, we validate the top candidates through a focused proof of concept, which takes 4–8 weeks. You get a working proof point, not a generic slide deck.
Unlike most AI consulting companies, ITRex also develops, integrates, and deploys AI systems—and we do that with your people in the room. We document decisions as they’re made, train internal champions on what was built and why, and make sure the handover isn’t a cliff edge. Every project deliverable, from code to documentation and runbooks, belongs to your company.
Data cleanup, licensing, legacy integrations, IT security reviews, and model selection—these are the costs that blindside businesses mid-project. Defaulting to a flagship LLM for every task burns tokens fast; a smaller, locally deployed model often does the job at a fraction of the cost. Our AI consulting agency names every risk in writing before you sign.
Post-launch, ITRex’s AI consultants monitor your systems, support your internal champions, and flag issues before they affect users. For generative AI consulting engagements specifically, that includes RAG pipeline evaluation and output quality monitoring. The benchmark for a successful engagement is your team being able to run, maintain, and extend what we built independently.
AI consulting is the practice of helping organizations identify where artificial intelligence creates real business value, determine whether their data and infrastructure can support it, and build a path from concept to working system. In practice, that means assessing whether a company is ready for AI, prioritizing use cases by ROI and feasibility, selecting the right technology—available off the shelf, integrated, or custom-built—and supporting development, deployment, and governance. A successful AI consulting engagement ends with a system your employees can use every day and maintain independently. A failed one ends with a roadmap nobody executes.
AI consulting firms worth working with are easy to identify. They’ll tell you what the engagement produces in concrete terms—a prioritized use-case list, a gap analysis, a phased roadmap, and written exclusions—before you sign the contract. They’ll introduce you to the people who will actually do the work. And six months after the engagement ends, their clients have something running in production, not a strategy document gathering dust.
Hire external AI consultants when your internal team has strong domain knowledge but limited AI engineering depth—or when you need to move faster than in-house hiring allows. The right moment is before you make architecture decisions, not after a pilot stalls. Bringing in artificial intelligence consultants mid-project to rescue a struggling initiative costs significantly more than starting with the right guidance.
Reputable AI consulting firms price by scope and deliverables, not by the hour. A discovery workshop may cost between $7,000 and $15,000. An AI/Gen AI readiness assessment typically costs $15,000-60,000 depending on your company’s size and the complexity of your data and IT ecosystems. AI strategy consulting with a proof of concept typically falls in the $40,000-120,000 range. Any AI consulting firm that gives you a fixed price without a scoping conversation first is doing guesswork—and you’ll pay for that later.
A structured AI advisory engagement typically covers four things: a current-state assessment of your data, infrastructure, and team capabilities; a prioritized list of use cases with feasibility and ROI estimates; a phased implementation roadmap with effort, cost, and risk flagged at each stage; and governance recommendations covering data access, compliance, and model oversight. The output is an executable plan—not a vision document—that your in-house engineering team or technology partner can act on immediately.
A working AI consulting engagement produces more than a slide deck. Expect a prioritized use-case list with feasibility and ROI estimates, a gap analysis across data, infrastructure, and team capabilities, a phased roadmap with effort estimates and risk flags, and—for the top-priority use case—a PoC or prototype with documented assumptions. Everything in writing before engineering begins.
The most effective AI consulting services for business process optimization start with a workflow audit—identifying which processes are high-volume, rule-bound, and fed by structured data. From there, AI consultants recommend the right tool: RPA for rigid rules, intelligent automation for semi-structured inputs, or Gen AI agents for knowledge-intensive tasks. That recommendation also covers the build vs. buy dilemma: whether a configured off-the-shelf solution covers your needs, a custom build is justified, or—as in our work with a global haircare brand—the right answer is already inside your existing data platform, just waiting to be activated.
AI advisory is how you avoid spending six months building the wrong thing. Before any development begins, a structured advisory engagement clarifies which use cases your data can actually support, which compliance requirements affect your architecture, and whether buying an off-the-shelf tool is a smarter move than going custom. It’s the step that separates AI programs that scale from ones that stall.
Vendor selection in AI consulting starts with your constraints, not a preferred tech stack. A good AI consultancy maps your compliance requirements, existing infrastructure, data residency rules, and budget before any model or platform enters the conversation. The output is a clear recommendation—build, buy, or integrate—with the trade-offs documented. ITRex is an OpenAI Select Partner with certified Claude engineers on the team, but those relationships don’t drive our recommendations. When open-source fits better, we say so.
ROI in artificial intelligence consulting is only meaningful when you define the baseline before development begins. ITRex ties every engagement to specific, measurable outcomes: lead qualification time, onboarding speed, analyst hours saved, or cost per transaction. We then model the full cost picture upfront, including data preparation, infrastructure, integration work, and annual maintenance, so your leadership can make an informed decision before allocating the engineering budget. The business case comes first; the technology follows.
