AI consulting
AI Consulting Services to Plan Your AI Projects
AI consulting services for leaders who need a practical AI plan, a credible budget and a clear decision date. Meronym runs focused, fixed-fee engagements that produce a recommendation your board and delivery teams can act on. Each engagement below includes a fee and timeline, and the readiness check can help you choose a sensible starting point before a conversation.
Four ways in, each one a complete purchase
- AI readiness assessment2 weeks
A scored view of your data, systems and approvals, and the two use cases worth starting on
- AI strategy consulting3–4 weeks
A costed roadmap with a business case per initiative and a named owner against each
- AI proof of concept4–6 weeks
One use case built on your own data, measured, ending in a go or no-go
- AI implementationSet after scope
The system in production, run by your team or by ours under an agreed service level
Enter at any rung. Nothing here requires the one before it.
- Years in AI
- 6
- AI engineers
- 30+
- Industries covered
- 8
- Priced engagements
- 6
Options we weigh
The models, clouds and data platforms a roadmap chooses between
OpenAIHosted API
Hosted API
GeminiHosted API
Mistral AIOpen weights
Hugging FaceOpen weights- OpenAIHosted API
- Hosted API
- GeminiHosted API
- Mistral AIOpen weights
- Hugging FaceOpen weights
AWS BedrockCloud platform
Vertex AICloud platform
Azure MLCloud platform
SnowflakeData platform
DatabricksData platform- AWS BedrockCloud platform
- Vertex AICloud platform
- Azure MLCloud platform
- SnowflakeData platform
- DatabricksData platform
What we do
What our AI consulting services actually produce
Our AI consulting services produce documents and a decision, and both are named before an engagement starts. A scored assessment of what you can actually build on. A shortlist of use cases with a cost and an owner against each. A written recommendation that includes what not to do. The deliverables section below names all six artefacts and who each one is written for.
Strategy work is most useful when each recommendation has a cost, an owner and a realistic path through procurement, security review and day-to-day operations. We include those details so the work supports a decision rather than creating another planning exercise.
So our AI consulting services scope the decision first, not the programme. One question you actually need answered, a fixed fee against it, a date it lands, and the named artefact that answers it. If the answer turns out to be that you should not build the thing, that is the engagement working rather than failing.
- Scope
- One decision, fixed fee
- Every engagement names the question it answers, and the fee is agreed before the work starts.
- Ceiling
- We say what not to build
- A useful recommendation includes the options that are not worth pursuing and explains why.
- Proof
- Named artefacts, named readers
- Six deliverables, each written for a specific person: the board, the CFO, the engineering lead, the security reviewer.
- Ownership
- Yours at handover
- Documents, models, scoring sheets and the evidence behind them, in your systems, with nothing held back to keep you tied to us.
01 · Sub-services
AI consulting services, with the fee on each one
Six AI consulting services, each a separate engagement with a timeline, fee, practical deliverables and a clear description of when it is useful.
AI readiness assessment
We look at the data, the systems, the approval path and who would own the work, and score each against what a production AI system actually needs.
What you get
- A scored readiness report with the evidence behind each score
- A data and systems inventory
- A shortlist of two or three use cases worth starting on
- A blocker list with a named owner against each
Fee
$4k–$12k
Best for
A board has asked what AI work is worth pursuing and the team needs an evidence-based answer.
AI strategy consulting
Turning a list of ideas into a sequence the organisation can fund: what to do, in what order, at what cost and with a named owner.
What you get
- A costed roadmap across four quarters
- A business case per initiative
- A build, buy or wait call on each one
- An operating model and owner map
Fee
$6k–$24k
Best for
You know roughly what you want to do and need it costed, sequenced and owned before budget.
Agentic AI consulting
Setting the boundary before anyone builds: what an agent may do alone, what it must escalate, and in what order the pieces get built.
What you get
- An autonomy and approval design
- A tool and permission inventory
- An escalation rule table
- A build sequence with its dependencies
Fee
Quoted after scope
Best for
You have decided on agents and need the approvals and the sequence settled before a line is written.
AI proof of concept
One use case built on your own data and measured against your own cases, so the decision to fund it rests on evidence rather than on a vendor demo.
What you get
- A working system on your own data
- A measured result against a set of your cases
- A production cost estimate, build and run
- A go or no-go recommendation
Fee
$8k–$35k
Best for
The case looks plausible on paper and decision-makers need evidence from real data before funding it.
AI implementation
Building and shipping the thing that was decided on, with the integrations, the controls and the monitoring that make it survivable.
What you get
- The system running in production
- Integrations and access controls
- Monitoring and an evaluation gate on release
- Runbooks and training for the team that inherits it
Fee
Quoted after scope
Best for
The decision is made and you need it built and running rather than advised on.
AI managed services
Keeping a live system accurate: monitoring, evaluation reruns on every change, drift response and tuning, under an agreed service level.
What you get
- Monitoring and incident response
- Evaluation reruns on every model and prompt change
- Drift response and tuning
- A quarterly performance review with the numbers
Fee
$9k–$25k per month
Best for
Something is already live and your team would like ongoing help keeping it accurate and dependable.
02 · Readiness
Score your AI readiness in two minutes
Five questions, with no form or email address. It reviews the conditions that help an AI project reach production and suggests the first issue worth addressing. If consulting would be premature, the result says so clearly.
Your readiness score
25out of 100
Not readyReady to planReady to proveReady to build
Not ready yet
The blockers here are organisational rather than technical, and building now would produce a pilot with nowhere to go.
Ready to plan
Enough is in place to plan properly, but not enough is decided for anyone to start building against a budget.
Ready to prove
The data, the owner and the access are there. What is missing is evidence that the case works on your own material.
Ready to build
Nothing in this list is blocking you. Another assessment would tell you what you already know.
What we would recommend
Start with an AI readiness assessment. It is the cheapest way to turn the blockers into a list with owners and dates against them.
$4k–$12k
An AI strategy engagement: a costed roadmap, a business case per initiative, and a build, buy or wait call on each.
$6k–$24k
A proof of concept on one use case, measured against a set of your own cases, ending in a go or no-go.
$8k–$35k
Go straight to implementation, or to the build hub for the system you want. Scope takes two weeks and ends in a fixed quote.
Quoted after scope
Fix this first
Start a data access request now, in parallel with everything else. It is almost always the longest lead time on the project, and it does not need the project to be approved first.
Name one business owner with decision rights before scoping anything. A project with a sponsor but no decision maker stalls at the first real trade-off.
Get a written answer on write access to the two systems that matter most. Read-only changes what can be built, not just how long it takes.
Find someone who has taken something through your production approval recently and ask how long it actually took. Plan against that number, not the documented one.
Agree who owns the system after launch before building it. Clear ownership helps the team notice quality changes early and respond with confidence.
03 · Deliverables
What lands on your desk
A roadmap is useful only when people know what it contains and how to use it. Here are the six artefacts an engagement produces and the person each one is written for.
D1
Readiness scorecard
Your data, systems, approvals, ownership and skills scored against what a production AI system requires, with the evidence behind each score attached.
The executive sponsor
D2
Use case register
Every candidate we found, with an estimated cost, an expected benefit, the systems it touches and the person who would own it.
The sponsor and the finance lead
D3
Costed roadmap
The initiatives worth doing, sequenced across four quarters, with dependencies, a build or buy call on each and a cost against each quarter.
The board
D4
Business case
One model per initiative: what the work costs today, the expected change, the build and run cost, and the assumptions that would make it wrong.
The CFO
D5
Technical design and integration map
The architecture, the systems it reads from and writes to, the access each one needs, and how each component gets evaluated.
The engineering lead
D6
Governance and controls pack
Data handling, access, human oversight, audit trail and regulatory position, in the form a security review expects to receive it in.
The security reviewer
Process
How a first AI consulting engagement runs
Four steps inside the first engagement, whichever of our AI consulting services it is. The ladder at the top of the page says which engagement to buy; this is what happens inside one.
- 01Days 1–3
Frame the question
We agree the one decision this engagement has to produce, the fee against it and the date it lands. Anything outside that is written down as out of scope rather than quietly absorbed.
A scope note and a fixed fee
- 02Weeks 1–2
Look at what is actually there
Data, systems, access, approval paths and the people who would own the work. We talk to the people doing the job today, not only to the ones sponsoring the project.
A readiness scorecard
- 03Weeks 2–3
Cost the options
Each candidate gets a build cost, a run cost, the systems it touches and a build, buy or wait call. The options we would not take are listed with the reason we would not take them.
A use case register and a business case
- 04By the agreed end date
Hand over and present
The artefacts, presented to the people who have to act on them, with the assumptions that would change the answer named on the page rather than buried in an appendix.
A costed roadmap and a recommendation
04 · Governance
Governance, and the evidence it produces
Governance is part of all six AI consulting services, not an extra. A control is worth something only if it leaves behind a document a reviewer can read, so every row below names what it produces and who owns it on the engagement.
| Area | What we do | Evidence it produces | Owner |
|---|---|---|---|
| Data handling and residency | Your data stays in your cloud account and your region. Retention and deletion are agreed before anything is ingested. | A data flow map and a retention schedule | Engagement lead |
| Access and least privilege | Every integration gets the narrowest credential that does the job, and read access and write access are approved separately. | A reads and writes sheet, per system | Platform engineer |
| Model and vendor choice | Models are chosen on measured accuracy against your own cases, and each vendor's terms on training and retention are checked before use. | A model selection note with the terms attached | AI engineer |
| Human oversight | The decisions a person must approve are named up front, and the queue those decisions land in is designed as part of the system. | An approval and escalation table | Your business owner |
| Audit and traceability | Every run is logged with its inputs, its tool calls and its sources, and the log is queryable by your own team. | Run logs and a retrievable trace per decision | Platform engineer |
| Security review | Before anything goes live, the system is tested against five gates: prompt injection, tool call authorisation, data egress, retention and audit completeness. The threshold on each is agreed before the testing starts. | A threat model and a gate report you can hand to a customer's security team | Security reviewer |
| Regulatory position | We map the system against the obligations that apply to it, including the EU AI Act where it is in scope, and list the gaps rather than the strengths. | A regulatory mapping with an open gap list | Engagement lead |
The pod
Who is on an AI consulting engagement
Five roles staff every one of our AI consulting services. The same people from the first conversation through to the handover, and you have their names before you sign anything.
| Role | Owns | Allocation |
|---|---|---|
| Engagement lead | The scope note, the fixed fee, the dates and what stays out of scope | Set in scope |
| AI engineer | The technical design, the integration map and the evaluation approach | Set in scope |
| Data engineer | The data and systems inventory, and what it would take to get more access | Set in scope |
| Business analyst | The use case register, the business cases and the interviews behind them | Set in scope |
| Security reviewer | The governance pack, the threat model and the regulatory mapping | Set in scope |
05 · Industries
AI consulting services by sector
- InsuranceDecisions that have to be explainable to a regulator years later, which rules out most of what gets demonstrated.
- BankingModel risk governance already exists here, so the question is how an AI system fits it rather than whether one is needed.
- HealthcarePatient data, clinical accountability and procurement cycles that outlast the technology being procured.
- ManufacturingPlant systems that predate the cloud, and a shop floor where an unreliable system is switched off in a week.
- Supply chainData that belongs to counterparties, so the constraint is contractual before it is technical.
- LogisticsThin margins and high volumes, where the business case turns on cost per transaction rather than on headcount.
- RetailSeasonal peaks that shape the architecture, and customer-facing experiences where answers need careful review and clear escalation.
- ConstructionProject-based work with no shared system of record, so the first question is usually where the data would even come from.
06 · Work
AI consulting services, in two example projects
- Example project · AI governanceEvery AI feature and internal tool in one register, each with a named ownerAn example of the governance work this page describes: an inventory of product features and internal AI tools, named owners, change approvals, and the evidence a customer's security review asks for.Read the example project
- Example project · AI implementationAn order entry pilot taken into productionAn example of an implementation engagement: the pilot gets its own ERP identity, tested exception handling, and a handover to IT with runbooks, monitoring and a rollback path.Read the example project
Read before you commit a budget to AI consulting services
All guides- GuideAI agent costWhat agents, assistants and automation cost to build and to run, by tier, and the factors that move the number.
- GuideAgentic AI companiesHow providers compare on published client evidence, human review and the practical questions worth asking before a shortlist becomes a contract.
- GuideEnterprise AI agentsEnterprise platforms compared by what they require, how people approve actions, their billing unit and publicly available pricing.
FAQs
Questions buyers ask about AI consulting services
Straight answers on what AI consulting services cost, how long they take, what they deliver, who does the work and what happens after the strategy lands.
How much do AI consulting services cost?
A readiness assessment is typically $4k–$12k, a strategy engagement $6k–$24k, a proof of concept $8k–$35k, and managed support $9k–$25k per month. Implementation is quoted after scope because integration and control requirements vary. Each fee is fixed against a written scope before work starts, and the readiness assessment's fee is credited against the build if you go ahead.
What is an AI readiness assessment, and what does it cost?
It reviews your data, systems, approval path and ownership against what a production AI system needs, then returns a blocker list and two or three use cases worth considering. It usually takes two weeks and costs $4k–$12k, credited against the build if you go ahead. The free check on this page is a short version that can help you decide whether the paid assessment would be useful.
How long do AI consulting services take?
A readiness assessment usually takes two weeks, a strategy engagement three to four weeks, and a proof of concept four to six weeks. Each has an agreed end date. If the question or scope changes materially, we discuss a new scope and fee with you before extending the work.
What do AI consulting services actually deliver?
Documents, named before we start. A readiness scorecard, a use case register, a costed roadmap, a business case per initiative, a technical design and integration map, and a governance pack. The deliverables section on this page says what is in each one and who it is written for. They are yours, in your systems, with nothing held back.
How should we choose an AI consulting company?
Ask five things early: the fee for the first engagement, its end date, the artefacts it produces, who will deliver the work, and how the team handles a recommendation not to build. Clear, specific answers make comparison easier for business, technical and procurement reviewers.
Should we hire outside AI consulting firms or build the capability in-house?
In-house is usually right for the long run and usually too slow for the first decision. The pattern that works is a short outside engagement to get the decision made and the design settled, with your people in the room so the capability transfers. If your team already has the data access and someone who can own evaluation, say so and we will tell you what is genuinely worth outsourcing.
What is the difference between AI consulting services and AI implementation services?
Consulting decides what to build and whether to build it. Implementation builds it. They are separate engagements here with separate fees, so you can buy the decision without committing to the build, and take the decision elsewhere if you want to. Most firms bundle them, which makes a recommendation to stop building an unlikely outcome.
Are you a generative AI consulting company too?
Yes. The readiness assessment, the strategy work and the proof of concept treat a generative AI use case like any other: what it would answer from, how its answers would be checked, who would own it, and what it would cost to run. When the answer is to build it, the build is quoted as generative AI development. See our generative AI development services
Who does the work, and will we meet them before signing?
A pod of up to five: engagement lead, AI engineer, data engineer, business analyst and security reviewer. The team table on this page says what each one owns. The people in the first meeting are the people on the engagement, and you have their names before a contract exists.
What happens to our data, and how do you handle governance?
Your data stays in your cloud account and region, retention is agreed before anything is ingested, and every integration receives the narrowest credential it needs. The governance table lists seven control areas and the evidence each produces. We provide current security and compliance documentation during scope for your reviewers to assess directly.
What happens after the strategy lands?
One of three things, and we will say which we think it is. You build it with your own team, using the technical design and the integration map. We build it, under a separate implementation engagement with its own fixed quote. Or nothing gets built, because the business case did not hold, which is a result worth paying for rather than a failed engagement.
Bring one question. Leave with a fee, a date and a named artefact.
Tell us the decision you have been asked to make and when you have been asked to make it. An engineer replies within two business days with the engagement that fits, the fee, the end date and the name of the person who would run it.
Reply within two business days · Clear fees and timelines · Meet the people who would deliver the work