AI consulting services from engineers who build what they recommend
AI strategy grounded in timed workflows, real data and the governance your compliance team will ask about, including EU AI Act readiness for systems that touch European users.
- AI system inventory and risk register
- Evaluation and regression harnesses
- Process timing and workflow instrumentation
- Commercial and open-weight LLMs
- Cloud AI services on AWS and Azure
- AI use-case ideas
- Pilots and embedded AI
- Current workflows
- 01 Inventoryevery AI system, including bought ones
- 02 Time workflowswhere cost and delay actually sit
- 03 Classify riskprovisional EU AI Act category
- 04 Rank use casesvalue, feasibility and risk
- Build, buy or leave it
- Governance design
- Sprint-sized roadmap
What you get
7 deliverables, all yours to keep
- AI readiness assessment
- Ranked use-case portfolio with timed baselines
- Build, buy or do-nothing call per use case
- AI system inventory with provisional risk class
- Governance design for oversight and change control
- Documentation pack structure
- Delivery roadmap sized in sprints
Sound familiar?
Who needs AI consulting services, and what they are usually stuck on
Most organizations that come to us for AI consulting services do not lack ideas. They have a long list, a few pilots and a board asking what the plan is, but no way to tell which ideas will survive production. Our AI strategy work produces a decision for each use case, sized in sprints and team roles because we also build.
On a large-scale energy enterprise’s partner program, timing the onboarding cycle first showed where the days went and set the build order for the whole program. Board-level strategy above technology choices is the work of our sister firm Dezaris. Where an assessment moves into delivery, the work continues as AI development.
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Twenty proposed AI use cases and budget for two
Ideas arrive from every department with no common way to compare them.
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A pilot that stopped when compliance asked about errors
It worked in a demo, then stalled on the question of what happens when it is wrong.
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AI features inside SaaS products nobody approved
There is no inventory of where models touch customer or employee data.
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Selling into Europe without knowing what is in scope
A US or UK company has heard the EU AI Act applies to it and does not know which systems are covered.
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A strategy deck nobody can fund
A previous engagement left recommendations with no costing, no data assessment and no named owner.
The work
Inside a AI Consulting engagement.
What an AI consulting company should hand you
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A timed baseline per use case
How the current process actually runs, so value is estimated from evidence rather than a vendor benchmark.
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A data and integration check
Whether the data exists and can be reached, tested against a sample of your real data rather than a description of it.
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The governance each use case needs
Oversight points, logging and model change control, sized to the risk of the use case.
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Build, buy or leave it alone
A recommendation per use case. When the delay sits in queueing or hand-offs, a workflow fix with no model in it will do more.
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Plans sized in sprints and team roles
Because we also build, every recommendation is sized so it can actually be funded.
How we approach AI strategy
- 01
Inventory before ambition
Shadow AI inside SaaS tools is often the largest unmanaged risk, and it stays invisible until someone lists it.
- 02
Evidence per use case
Value is estimated from a timed baseline, not a vendor benchmark. Feasibility is tested against a sample of your real data.
- 03
Governance sized to risk
A marketing summarization tool and a credit decision assistant do not need the same controls. Over-governing low-risk uses burns goodwill you need for high-risk ones.
- 04
Plan the first build in detail
The first use case leaves with a team shape, a sprint plan and the evaluation set it will be tested against. A roadmap that stops at "phase two: scale" is not a plan.
Where AI consulting goes wrong, and how we avoid it
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A strategy nobody can execute, chosen for how it sounds in a board paper
We test data access during the assessment, not after the budget is approved.
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Treating regulation as a later problem
Retrofitting logging and oversight into a live system costs far more than designing them in, so classification happens before build decisions.
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Consultancy that never meets engineering
Our assessors are the architects and engineers who deliver, so nobody recommends what they would not build.
How it runs
From first call to production.
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Inventory
Every AI system in use or planned, including the ones bought inside SaaS products, with owner, purpose and the data it touches.
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Time the candidate workflows
Measure where the cost and delay actually sit before proposing AI for any of them.
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Assess readiness
Data access and quality, integration effort, skills and the governance each use case would need.
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Classify and design governance
Provisional risk classification per system, then oversight points, logging and documentation matched to that classification.
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Rank and roadmap
Use cases ranked on value, feasibility and risk, with a sprint-level plan for the first one and a clear list of what not to do.
EU AI Act readiness
EU AI Act readiness for US and EU buyers
The Act regulates AI by the risk of its use and can reach companies outside Europe. Obligations phase in over years, so confirm dates with counsel. We give no legal advice. We prepare the engineering groundwork your legal team needs.
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Risk classification
An inventory of every AI system you provide or use, with a provisional class and whether you act as provider or deployer. Counsel reviews and confirms.
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Documentation and record-keeping
A structured documentation pack and, where we build, records generated during delivery: grounding data, evaluation, which versions ran when and what each decision was based on.
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Human oversight
The model proposes, a rule or named person decides, the reviewer sees the source behind each finding, and every override is logged with an accountable owner.
Team and timeline
1–2 weeks
for a readiness assessment of one business unit
Indicative. Actual duration depends on requirements and complexity, and can change.
Who works on it
- Principal consultant
- Solution architect
- Business analyst
Engagement model
Fixed Cost for a scoped assessment of 1–2 weeks. Time and Materials when advisory continues alongside delivery or across several business units.
Good to know
Your side provides an executive sponsor, process owners for the candidate workflows and a legal or compliance contact for classification review. If the work moves into delivery, the architect usually stays on.
Proof
Where we have built this.
How a leading energy enterprise reduced partner onboarding time by 45%
faster partner onboarding
How a large global financial services company reduced travel processing time by 38%
faster travel processing
How a global AI SaaS company improved conversion rates by 18%
enhanced KPI visibility
FAQ
AI Consulting: common questions.
What do AI consulting services include?
At minimum: an inventory of where AI is used or planned, an assessment of data and system readiness, a ranked set of use cases with the evidence behind each rank, and a governance design covering oversight, logging and model change control. Good AI consulting services also say which ideas should not proceed, and size the first build in weeks and team roles so the plan can actually be funded.
How do we choose an AI consulting company?
Ask what they have put into production, not what they have presented. Ask how they would catch a wrong model output in your process, how they handle model version changes, and whether they have ever recommended against automating something. An AI consulting company that also builds will size recommendations realistically, because it expects to be held to them.
Does the EU AI Act apply to US companies?
It can. The Act reaches providers that place AI systems on the EU market and, in many cases, organizations whose AI system outputs are used in the EU, regardless of where the company is based. A US company selling software with AI features to European customers, or using AI to make decisions about people in the EU, should check its position. Your legal counsel should confirm the specifics.
Can you make us compliant with the EU AI Act?
We do not give legal advice or certify compliance. What we do is the engineering and operational side of readiness: inventorying AI systems, preparing provisional risk classifications for counsel to review, building logging and human oversight into the systems, and structuring the technical documentation. Compliance decisions stay with your legal and compliance teams, who get far better material to decide from.
How long does an AI readiness assessment take?
A focused assessment across one business unit typically takes 1–2 weeks: inventory and interviews first, then workflow timing, data and integration checks, classification and governance design, and a ranked roadmap. Several business units take longer, mostly because interviews and data access approvals multiply with each one.
Related services
- AI DevelopmentLLM applications, generative AI features and document agents built for production: structured outputs, pinned models, regression sets and a person on every consequential decision.
- AI Workflow AutomationApprovals, onboarding and verification rebuilt as governed workflows. AI handles volume, rules keep decisions repeatable, and people own the exceptions.
- Data Engineering & AnalyticsData analytics consulting and data engineering for teams that have the data but cannot answer the question. One definition per metric, reporting that runs itself.
