AI consulting services that turn ideas into a costed plan
We help leadership teams decide where AI is worth using, what it will take in data, budget and people, and how to move from pilot to production without wasted spend.
Our AI consulting services are for leaders who know AI matters but need a clear answer to three questions: where it will help, what it will cost and what could go wrong. We review your processes, data and systems, identify use cases with a realistic payoff, and give you a prioritized, costed roadmap you can act on with us, with your own team or with another vendor.
ZedBlu is an engineering company first, so our advice is grounded in what it takes to build and run AI in production. We have built generative AI into platforms we run, including AI-generated SEO for the news-agency stories imported into The Hawk and Dainik Hawk, and we bring that delivery view to every recommendation: integration effort, model costs, data quality and the change in daily work for your staff.
Engagements range from a short AI readiness assessment to an ongoing advisory role alongside your product and data teams. Every engagement starts with an NDA, and every deliverable is written in plain language for both executives and engineers.
What our AI Consulting & Strategy team delivers
AI opportunity assessment
We run workshops with business and technical stakeholders, review workflows and pain points, and build a long list of AI use cases. Each one is scored on value, feasibility, data availability, risk and time to impact, so leadership can see which ideas deserve investment and which should wait or be dropped.
AI readiness assessment
An AI readiness assessment of your data sources, quality, access rights, infrastructure, security posture and team skills. You get a gap analysis with concrete fixes, such as consolidating data into a warehouse, cleaning master data or setting up access controls, ranked by how much they matter for the use cases you chose.
AI strategy and roadmap
AI strategy consulting that turns priorities into a phased AI roadmap: which use cases to pilot first, build-versus-buy decisions, model and vendor options, budget ranges, staffing and milestones. We tie each phase to a business measure, so progress is judged on outcomes like cycle time or service quality rather than on the number of pilots launched.
Generative AI consulting
Generative AI consulting for teams evaluating assistants, copilots and content automation. We compare hosted models such as OpenAI and Claude with open models like Llama, design retrieval and guardrail approaches, estimate token costs at your expected volume, and set policies for how staff may use public AI tools with company data.
AI governance and risk
We help you set up AI governance that fits your size: an acceptable-use policy, a register of AI systems, review steps for high-risk use cases, data protection checks and human oversight rules. We map it to rules that may apply to you, such as GDPR or the EU AI Act, and work alongside your legal advisors, so compliance is planned rather than retrofitted.
Proof of concept and implementation support
When a use case is approved, we can run an AI proof of concept on your data in a few weeks to test the assumptions behind the roadmap. If it works, our engineers build the production version, or we support your in-house team as an AI implementation partner through architecture reviews, code reviews and vendor evaluation.
What you get
Advice from people who build
Recommendations come from engineers who ship AI systems, so estimates for effort, cost, timelines and risk reflect real delivery experience rather than slideware or vendor marketing.
Vendor-neutral recommendations
Model, platform and tool choices are based on your use case, data rules and budget rather than a preferred vendor, and we explain the trade-offs behind each option so you can decide with confidence.
Less wasted spend
Scoring use cases before building, and testing them with small proofs of concept, means budget goes to projects with evidence behind them instead of pilots that never reach users.
A plan your team can own
Deliverables include the roadmap, architecture options, cost model and policies in editable form, so your managers can update them as priorities, regulations and models change.
Our process
Tools & tech we use
Use cases
- Board-level AI strategy for mid-size and enterprise companies
- Build-versus-buy decision for an AI customer support tool
- AI usage policy for staff who use public chat tools
- Data readiness review before a forecasting project
- Vendor evaluation for document AI or chatbot platforms
- Second opinion on an AI project that has stalled
Ready-made solutions to launch faster
Related services
AI Consulting & Strategy: FAQ
What do AI consulting services include?
Typically an opportunity assessment, a data and infrastructure readiness review, a prioritized roadmap with cost and effort estimates, and governance guidance such as an AI usage policy. Depending on your needs, we can also evaluate vendors, review architecture, run a proof of concept and support your team during implementation. You choose the modules; we do not push a fixed package.
How long does an AI strategy engagement take?
A focused AI readiness assessment for one department typically takes two to three weeks. A company-wide opportunity assessment with a roadmap usually takes four to eight weeks, depending on the number of business units, stakeholders and systems involved. Ongoing advisory support can continue for months after that at a pace that suits your team.
How is AI consulting priced?
Most consulting work is quoted as a fixed price for a defined scope, such as a readiness assessment or a roadmap, so you know the cost upfront. Price depends on the number of departments and stakeholders involved, the systems and data to review, and whether a proof of concept is included. Ongoing advisory can be billed on time and material. We confirm the exact figure after a free consultation.
Do we need clean data before starting with AI?
Not before the conversation, but usually before the build. Many generative AI use cases work with documents as they are, while forecasting and scoring models depend on consistent historical records. Part of our assessment is telling you which use cases your data supports today and which need groundwork first, so you can start with the ones that are ready.
Will you also build what you recommend?
We can, but you are not obliged to. Our roadmap is written so any capable team can execute it. If you want us to build, our engineers deliver the solution with weekly demos and full source code ownership. If you prefer your own developers or another vendor, we can stay on as an independent reviewer of architecture and progress.
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