AI Consultants Who Build, Not Brief
We guide tourism boards, financial institutions, telecoms, and regional conglomerates through AI adoption — from discovery to a production deployment in 60-90 days. Strategy and implementation from the same senior team.
Trusted by organizations across the Caribbean & beyond
TourismAuthority
bravasolutions
Founder & CEO, Phoenix AI Solutions
You've read the case studies. You've sat through the vendor pitches. Everyone says AI will transform your business — but nobody tells you how, or where to start, or what it will actually cost. You need an AI consultant who understands your constraints, not just the technology.
Most AI consultancies hand you a 200-page strategy document and disappear. We don't work that way. Our consulting engagements end with something you can actually build — this quarter, with your team, at your budget.
How We Approach AI Consulting
One methodology, four stages. The strategy and the deployment come from the same team, in the same engagement — so the roadmap is written by the people who have to make it work.
1. Discover
Weeks 1-2
Understand where you are and what's realistically achievable. Most AI projects fail because this stage is rushed or skipped.
- Operational audit: processes, pain points, and manual work mapped
- Data, tech-stack, and team readiness assessed honestly
- Opportunity inventory built across every department
2. Prioritize
Weeks 2-3
Not everything that's possible is worth doing. We rank every opportunity by ROI × feasibility and sequence a roadmap that starts with a quick win.
- ROI modeling and feasibility scoring for each opportunity
- Build vs. buy vs. partner decided per candidate — with technical due diligence on any vendor
- Phased roadmap with budget and success metrics agreed upfront
3. Prove
Weeks 4-8
Test the first project against real data before committing to production. Confidence should come from evidence, not slides.
- Scoped proof of concept on your data, in a controlled environment
- Success metrics defined before the build starts, not after
- Honest go/no-go decision — including "don't proceed"
4. Scale
Weeks 6-16 & ongoing
Deploy to production, hand your team the keys, and expand what works. The engagement ends with a running system, not a recommendation.
- Phased rollout integrated with your CRM, ERP, and existing workflows
- Team training and change management so adoption sticks
- Actual ROI measured against projection; expand to the next roadmap item
Why Phoenix
Business-First, Not Tech-First
We don't sell AI for the sake of AI. Every recommendation ties back to revenue, margin, or operational impact — measurable, not aspirational.
ROI × Feasibility Matrix
Not everything that's possible is worth doing. We rank opportunities by the intersection of business impact and implementation reality, so you start where it matters most.
Ship Within Weeks, Not Quarters
Our consulting engagements include a scoped first project you can start this quarter. You'll see working output before the strategy deck goes cold.
Illustrative AI Consulting Engagements
AI consulting delivers measurable outcomes when combined with hands-on implementation support. Here are three illustrative scenarios — not specific clients — showing how we'd work with companies like these and the results you could expect.
Illustrative scenario — not a specific client.
The Challenge
Board pressure to "do AI", with competing initiatives across divisions: customer care wants chat automation, marketing wants churn prediction, network operations wants predictive maintenance. No shared view of value, cost, or feasibility — and a previous strategy engagement that produced a deck nobody acted on.
The Solution
Full-cycle AI consulting engagement: a discovery audit maps AI opportunities across every division, an ROI × feasibility matrix prioritizes the strongest candidates, and the roadmap sequences a first project — often customer-communication automation — that can ship in weeks rather than quarters.
Results You Could Expect
- A prioritized, costed AI roadmap the executive team can actually fund — not a 120-page deck
- A first production deployment targeted inside 60-90 days of engagement start
- Service metrics instrumented from day one: response times, resolution rates, hours returned to the team
- A phased expansion path — prove value on one use case before committing budget to the next
Illustrative scenario — not a specific client.
The Challenge
Evaluating competing AI vendor pitches for document analysis and risk workflows. Internal team lacks the expertise to assess vendor claims, model risk, data handling, or integration feasibility — with a six-figure multi-year contract on the table and regulatory exposure if the vendor gets data handling wrong.
The Solution
AI vendor evaluation consulting: we build an evaluation framework covering technical, security, and business criteria, run technical due diligence on the finalists (including a Phoenix Shield code and data-handling review), manage a proof of concept on real data, and support contract negotiation.
Results You Could Expect
- An evidence-based vendor shortlist — capabilities verified against your data, not the sales deck
- Security, data-residency, and compliance risks surfaced before signature, while you still have leverage
- Contract terms negotiated from technical knowledge: audit rights, data handling, exit clauses
- A validated deployment path — or a documented reason to walk away before signing
Illustrative scenario — not a specific client.
The Challenge
Core operations still run on manual processes — spreadsheet-based forecasting, hand-built reporting across divisions, full-time staff tied up in repetitive work. Leadership knows AI could help but has no internal expertise to define requirements or decide between building and buying.
The Solution
End-to-end AI consulting: an operational audit maps the highest-cost manual processes and captures a baseline, a build-vs-buy analysis determines the right path for each opportunity, and we scope and manage delivery of the first solution — custom-built where integration with existing systems demands it.
Results You Could Expect
- Clarity on where AI genuinely pays back across divisions — and where it doesn't
- A build-vs-buy decision grounded in your systems and integration reality, not vendor marketing
- A scoped first implementation with success metrics agreed before the build starts
- Savings measured against the baseline captured during the audit — not asserted after the fact
Engagements usually land in one of four places: sales and marketing automation with Revenue Engine, technical due diligence with Phoenix Shield, governance frameworks with AI Policy, or a bespoke build via Custom AI Solutions.
For a real example with real numbers, read how a Caribbean tourism authority measures per-influencer EMV ROI with Phoenix Influence.
Frequently Asked Questions
What is AI consulting, and when do you need it?
AI consulting guides an organization through AI adoption from discovery to deployment: readiness assessment, opportunity prioritization, vendor evaluation, implementation planning, and optimization. You need it when you know AI could help but not where to start, when pilots have stalled without delivering results, when you're weighing build vs. buy and need independent technical due diligence, or when data-protection regulation demands governance you don't yet have. Unlike strategy-only consulting, Phoenix engagements end with a working first deployment — not a recommendation. For a vendor-selection framework, see our guide on how to choose an AI implementation partner.
How long does an AI consulting engagement take?
Discovery and assessment: 1-2 weeks. Prioritization and roadmap: 1-2 weeks. Proof of concept: 2-6 weeks depending on complexity. First production deployment: typically inside 60-90 days of engagement start. Full custom builds run 2-6 months. We accelerate timelines by combining strategy with immediate implementation instead of splitting them into separate contracts.
What does AI consulting cost?
Pricing is tailored to each engagement and scoped to your needs — book a call for a quote. Factors include organizational size, project complexity, and how much hands-on implementation support you want. Every engagement is fixed-price or time-capped, never open-ended. Before committing, calculate your AI implementation ROI to build a data-driven business case.
How is AI consulting different from AI strategy or custom development?
AI consulting is the broadest engagement type, covering discovery through deployment. AI strategy is a subset focused on roadmap development and prioritization (typically 4-6 weeks). Custom AI development is hands-on engineering work building bespoke solutions. Most consulting engagements include strategy as a phase, then move to either vendor selection or custom development. Use AI consulting when you need end-to-end guidance. Use AI strategy when you just need the roadmap. Use custom development when you already know what to build.
Will we need to hire AI talent after the engagement?
Depends on the implementation path. If your first project lands on one of our managed platforms — Revenue Engine, Influence, Respond — Phoenix runs it for you, and no dedicated AI hires are needed. If we build custom, you'll eventually want technical ownership in-house. We assess team readiness during the strategy stage, so hiring requirements are known upfront — no surprises.
What industries do you work with?
Core expertise: tourism and destination marketing, telecoms, financial services, hospitality and hotel groups, and multi-division conglomerates — alongside B2B SaaS and professional services. Each industry has its own AI opportunities, regulations, and implementation challenges, so each engagement starts with its own opportunity map rather than a recycled framework. If your industry isn't listed, we assess fit during discovery.
Related Solutions
These solutions work well together or complement this offering
Ready to move on AI?
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