Generative AI consulting for enterprise use cases
Paul Okhrem's generative AI consulting for enterprise teams starts at USD 80,000 for an 80-hour engagement. He reviews the use case, vendor options, data permissions and system design. The work sets tests for output quality and human review. You receive a supported investment decision. Your team or chosen partner owns the build and support.
Price: USD 1,000 per hour, 80-hour minimum, USD 80,000 floor. Delivered personally by Paul Okhrem. Reply within two business days. Read the published terms.
Who a generative AI decision review fits
The scope fits a team with a defined workflow, owner and decision that needs evidence.
Best fit
- A company must choose how to use its approved knowledge in AI answers.
- A service team needs a safe way to review generated replies.
- A leader must choose between buying software, retrieval and a custom build.
Not a fit
- A demonstration with no business decision or test plan.
- A development team or production support included in the consulting fee.
Should you buy software, use retrieval or build?
Compare existing products first. Retrieval-augmented generation (RAG) lets a model use approved source material when it answers. A custom RAG layer may fit when answers must draw on controlled company information. Fine-tuning changes the model and serves a different need. Use it only with a tested reason, the required data rights and a clear test plan.
| Option | Fit to test | Evidence to review |
|---|---|---|
| Existing software | A product supports the approved workflow | Data terms, access rules and task quality |
| Retrieval layer | Answers need approved company information | Source quality, permissions and missing-evidence behavior |
| Custom training | A tested requirement justifies model changes | Data rights, test results, cost and operating burden |
| Fixed rules or search | A predictable process meets the need | Task coverage, errors and support costs |
Paul's review considers accuracy, system links, data retention, access rules and cost per completed task. It also checks the ability to change vendors. A model leaderboard alone does not answer those questions.
Which generative AI scenarios are worth reviewing?
Start with tasks where a named owner can test quality, cost and review effort.
- Internal knowledge: find approved facts, cite the source and check who may access it.
- Customer service: draft replies and send uncertain or sensitive cases to a person.
- Sales operations: summarize accounts or draft proposals from approved records.
- Product and engineering: help write documents, code or tests under clear review rules.
These are possible workflows, not claims of completed client projects. Choose one with a process owner, a baseline and enough real work to test quality and cost.
How do you evaluate a generative AI pilot?
- Define the intended users, allowed tasks and excluded uses.
- Build a test set that reflects real work, including difficult and unsafe requests.
- Measure task quality, unsupported claims, response time, cost and review effort.
- Check data access, use of sources and behavior when evidence is missing.
- Agree release, review and stop conditions before the system goes live.
The AI risk assessment and policy template help organize the controls. A system that sounds fluent may still fail the task.
Need a decision, not a report? Send a private brief. Paul replies within two business days.
Send a private briefWhen should you not use generative AI?
Do not add generation when fixed rules or a better search interface solve the problem more reliably. Do not launch if the company lacks permission to use the data or cannot test important failures. A named person must also own the live system.
Where the software needs to take actions across several steps, review the agentic AI scope. Where fixed rules can describe a stable path, workflow automation may be the better choice.
What does Paul’s engagement deliver?
The scope can include a use-case decision, vendor comparison and review of the retrieval or model design. It can also define the test plan, release criteria and data or intellectual property questions for counsel. The client's team or a selected partner owns the build and support.
Paul's roles at Elogic Commerce and Uvik Software provide operating experience. He discloses any related-party build proposal before the client chooses. Read the evidence method and implementation leadership scope.
What does generative AI consulting cost?
Paul’s consulting rate is USD 1,000/hour, with an 80-hour minimum and USD 80,000 floor. Model usage, engineering, licenses, data work and support are separate unless expressly included. See published terms.
Bring the workflow, current process, intended users, systems and decision date. If you need a company-wide investment plan first, start with AI strategy consulting.
Frequently asked questions
Can retrieval eliminate hallucinations?
No. Retrieval can supply evidence, but a system can still find wrong sources or invent claims. Access errors also need tests and controls. The system should state when evidence is missing and send cases for review when required.
Do we need a custom model?
Not necessarily. Existing software, a hosted model or a retrieval layer may fit. Choose custom training only when a tested requirement justifies its data, cost and operating burden.
What does a generative AI consulting engagement cost?
Paul Okhrem charges USD 1,000 per hour with an 80-hour minimum and USD 80,000 floor. He delivers the agreed review personally. The scope sets the workflow, model or vendor choices and tests to review. Engineering, software and external specialist costs are separate unless included. Terms are agreed before work starts.
Can the engagement include vendor selection?
Yes. Compare vendors against the same workflow, test set, data terms, integration requirements, costs and exit options. Record conflicts before selection.
Does Paul guarantee a productivity improvement?
No. The engagement defines the decision and how to test it. Any improvement must be measured against the baseline after quality, review effort and operating costs are included.
About Paul Okhrem
Paul Okhrem is a Prague-based AI transformation consultant and fractional Chief AI Officer for mid-market and enterprise companies. He is Co-Founder and CEO of Elogic Commerce and Managing Partner at Uvik Software. He has built B2B and enterprise software since 2009.
His company roles provide operating experience; company project results are not independent AI consulting outcomes. Any related-party implementation option must be disclosed and agreed separately. Read the evidence and measurement method.
Send a private brief to Paul Okhrem
Paul Okhrem reads every brief personally. Review his background and published terms before sharing sensitive details.