Most companies don't need an AI vision — they need to know which two or three AI use cases are worth building first, whether their data can support them and what it will take to get them into production. That is what our AI consulting services deliver.
We work through your workflows and systems with your team, look at real samples of your data, rank candidate use cases on value, data readiness and risk, and give you an honest build-vs-buy view. The result is a roadmap with architecture, team, budget drivers and a first release you could start right away.
Our consultants are the same senior engineers who build and run AI in production, so recommendations are grounded in what actually works — and if a commercial tool or a simpler non-AI solution is the better answer, we'll say so.
Advice grounded in AI we've delivered
Our recommendations come from shipping AI, not from slide decks. These projects are documented in our case studies.
Generative AI chatbot for aviation customer service
A generative AI chatbot for aviation customer service on Google Cloud, handling quotes, service requests and technical questions with a security layer and integration with internal systems.
Read the GA Telesis case study →
Generative AI platform for pharmaceutical R&D
Generative AI models that simulate and predict molecular interactions, embedded into Amgen's existing R&D infrastructure and validated against known outcomes.
Read the Amgen case study →
AI development for Rakuten Institute of Technology
Machine learning for sales forecasting and real-time buyer segmentation over large-scale commerce data, with ongoing model evaluation against live data.
Read the Rakuten case study →AI strategy & roadmap
Ranked use cases, sequencing and a delivery plan tied to business outcomes.
AI readiness assessment
Data quality, access, systems, skills and governance reviewed against each use case.
Use-case discovery workshops
Working sessions with your teams to find where AI removes real effort.
Build vs buy evaluation
Commercial AI tools, platform features and custom builds compared honestly.
AI governance & risk
Data rules, model choices, human review and quality measures for responsible AI.
Proof of concept
A small working prototype with an evaluation set to test the top use case before you commit.
Typical ai consulting services engagements
First AI initiative
Leadership wants to use AI but doesn't know where to start or what's realistic.
Stalled pilots
Proofs of concept that impressed in demos but never reached production.
Generative AI strategy
Deciding where LLMs, RAG and AI agents fit — and where they don't.
Data readiness review
Understanding what data work is needed before AI can be trusted.
Vendor & tool selection
Comparing AI platforms, model providers and agencies before a contract.
Regulated-industry AI
Planning AI in healthcare and financial services around privacy and review requirements.
Consultants who build
The people advising you design, build and run production software and AI.
Specific, not generic
Named use cases, measures of success and a first release — not a maturity model.
Vendor-neutral advice
We recommend off-the-shelf tools when they're the better answer.
Regulated-industry experience
Healthcare and financial work shapes how we handle data, risk and review.
Strategy that ships
If you choose, the same team moves straight into delivery without a handoff gap.
Yours to keep
Roadmaps, assessments and prototypes are yours, usable with any delivery partner.
How to choose an AI consulting company
AI consulting ranges from inspiring workshops to plans that actually ship. These questions help you compare any AI consulting company, including us.
Expect specific use cases, not an AI vision
A useful AI strategy names a small number of specific tasks — answering a class of support questions, extracting fields from documents, forecasting demand — each with the value at stake, the data it needs and a way to measure success. If the output is mostly trends and maturity models, you'll still need someone to tell you what to build.
- Which use cases are ranked first, and why?
- What would success look like in numbers for each one?
- What is the smallest version worth shipping?
Check the data before the model
Most AI plans fail on data: it's scattered, incomplete, locked in systems without APIs or restricted by privacy rules. A good AI consultant looks at real samples of your data early, checks access and permissions, and tells you plainly which use cases are ready and which need data work first.
Get an honest build-vs-buy view
Off-the-shelf AI tools are often the right answer for generic tasks. Custom development is worth it when your data, workflow or integrations are the advantage. Ask whether the consultant has anything to sell you either way — and whether they've recommended buying instead of building before.
Ask who will deliver the plan
Strategy that stops at a document often stalls. Ask whether the people advising you have built and run production AI, and whether the roadmap includes architecture, team, budget drivers and a first release you could start next month — with us or with anyone else.
- Have your consultants shipped AI to production?
- Does the roadmap include architecture and team, not only priorities?
- Can we take the plan to another vendor if we choose to?
Cover risk, governance and cost early
Responsible AI is practical work: deciding what data can go into which models, who reviews AI output, how quality is measured and what running costs look like at scale. Settle these in the strategy phase, especially in healthcare and financial services, so they don't block launch later.
- Healthcare & life sciences
- Financial services
- Aviation
- Automotive
- Media
- SaaS
- 01DiscoveryGoals, workflows, systems and constraints gathered with your stakeholders.
- 02Use-case mappingCandidate use cases listed and scored on value, data readiness and risk.
- 03Data & systems reviewReal data samples, access and integrations checked for the top candidates.
- 04Build vs buyCommercial tools, platform features and custom options compared.
- 05RoadmapArchitecture, team, governance, budget drivers and a sequenced plan.
- 06Prove itOptional prototype with an evaluation set for the first use case.
What do AI consulting services include?
Identifying and ranking AI use cases, reviewing data readiness, comparing build-vs-buy options, defining governance and risk controls, and producing a roadmap with architecture, team and budget drivers — optionally with a working prototype.
What is AI strategy consulting?
AI strategy consulting connects AI to business goals: deciding which problems AI should solve first, what it takes to solve them and in what order. Ours ends with a plan specific enough to start building.
How is an AI consulting company different from an AI development company?
Consultants advise on what to do; developers build it. We do both, so our recommendations are grounded in delivery — but the roadmap is yours to take to any team.
Do we need clean data before talking to AI consultants?
No. Reviewing your data is part of the work. We'll show you which use cases your current data can support and what needs fixing first.
How much do AI consulting services cost?
Cost depends on the number of teams and use cases involved, how much data review is needed and whether a prototype is included. We agree a fixed scope and price after an initial call.
Will you recommend off-the-shelf AI tools?
Yes, when they fit. Many tasks are best served by commercial AI tools or features already in your software. We recommend custom development only where your data, workflow or integrations are the advantage.
Do you offer generative AI consulting?
Yes. We help you decide where LLMs, retrieval-augmented generation and AI agents fit, how to handle data and quality, and what a safe first release looks like.
Can you also build what you recommend?
Yes. The same team can move into delivery — generative AI, AI agents, machine learning and the software around them — or support your in-house engineers.
AI strategy guides and related services
- Custom AI Development vs Off-the-Shelf AI Tools: What Should Your Business Choose?When custom AI development beats off-the-shelf AI tools, and when it doesn't.
- AI Development Services in 2026: The Buyer's Guide Most Vendors Don't Want You to ReadWhat AI development services include, how to budget and red flags to avoid.
- How Artificial Intelligence Is Reshaping the Future of Software DevelopmentHow AI is changing the way software is planned, built and tested.
- AI Implementation & DevelopmentOur full AI practice, from strategy to production and MLOps.
- Generative AI DevelopmentLLM applications, chatbots and RAG systems over your own data.
- AI Agent DevelopmentAI agents that carry out multi-step work inside your systems.
AI Consulting Services in production
All case studies →
Find the AI use cases worth building first.
Tell us your goals, systems and where work slows down. We'll come back with a scoped AI consulting engagement and what you'll have at the end.