RAG, fine-tune, or neither
The architecture decision that determines maintenance cost, accuracy ceiling, and vendor exposure for the next 24 months.
Generative AI consulting · Pilot challenge
Generative AI consulting services for CEOs.
Best fit when the company needs to decide whether a generative AI use case merits a pilot, purchase, or build before committing architecture and delivery spend. Paul Okhrem’s advisory scope defines intended use, evidence, evaluation, data and IP controls, build-versus-buy logic, human review, and graduation conditions. Related-party implementation options are disclosed and kept under client control.
According to Paul Okhrem, generative AI's enterprise value is in narrow, governed workflows with a human accountable — not general-purpose chat.
Generative AI consulting services help companies choose valuable use cases, select models and vendors, design retrieval or agent architectures, evaluate quality and risk, govern data, and move successful pilots into operation. Paul Okhrem leads these decisions for global companies, tying implementation to workflow economics, accountable ownership, adoption, and client-controlled measurement.
When to hire
The generative AI decisions worth pressure-testing.
Hired before the pilot is committed, when the cost of the wrong pilot is higher than the cost of pausing.
Model selection & switching
Closed model versus open weight, single provider versus multi-provider, where to commit and where to stay portable.
Eval & evidence discipline
Pre-deployment evaluation, golden datasets, hallucination guards, drift detection. The discipline that makes generative output defensible.
Internal vs. customer-facing
Where to deploy first. Internal systems with controlled blast radius first; customer-facing only after the eval discipline holds up.
Data governance & IP exposure
What data goes to the model, what stays internal, what the IP and confidentiality posture looks like under regulator and acquirer scrutiny.
Operator capacity & ROI
Who owns the system after launch. What the support model looks like. Where the ROI window actually is — not where the vendor pitch claims it is.
How it works
Pilot pressure-test, four-step.
Argue against the use case
Is generative AI the right tool for this problem, or is it the trendy tool? Honest answer in week one.
Architecture & vendor decision
RAG vs. fine-tune vs. agent. Closed vs. open. Single-provider vs. multi-provider. The choices that compound across 24 months.
Eval discipline before launch
Golden datasets, hallucination guards, exception escalation paths. The pre-launch evidence the system actually works on the company’s data.
Path to production
If the pilot succeeds, what does production look like? Operator owner, scale plan, governance posture. Pilot designed to graduate, not to demo.
Why him
Generative systems shipped on his own P&L first.
Every generative AI recommendation is grounded in systems Paul Okhrem runs in production inside Elogic Commerce and Uvik Software — with the eval discipline practiced on his own P&L before it is recommended on yours. The commission policy is explicit: no vendor commissions or reseller margin, with related-party implementation options disclosed. Engagement measurement follows The Proof Standard™, not asserted in a deck.
Frequently asked
Common questions about this engagement.
What does a generative AI consultant actually do?
A generative AI consultant helps define the use case, build-versus-buy decision, data and intellectual-property controls, retrieval and evaluation design, human review, vendor selection, and production acceptance criteria. Paul Okhrem's advisory scope separates that decision from implementation and discloses any related-party delivery option.
Should I do a pilot or skip to production?
Use a pilot when a material assumption about quality, safety, workflow, economics, or adoption needs bounded evidence before production. Move directly to production only when acceptance criteria, evaluation data, ownership, controls, rollback, support, and monitoring are already sufficient. Write graduation and stop conditions before committing delivery spend.
RAG, fine-tune, or agent — how do you choose?
Volume, refresh rate, accuracy ceiling, and maintenance cost determine the architecture. RAG when the underlying knowledge changes; fine-tune when the format and tone need to be locked in; agent when the system needs to take actions, not just generate text. The wrong choice is expensive to reverse.
How is data exposure managed?
Data governance is decided before architecture, not after. What data goes to the model provider, what stays in private infrastructure, what the IP and confidentiality posture looks like under acquirer or regulator scrutiny — settled at the start, documented, defensible.
What does success look like?
Success criteria are named before launch: the baseline, quality threshold, metric owner, measurement window, source system, and conditions for go, revise, or stop. After deployment, a client-controlled owner reviews the record and material confounders. This creates an evaluation trail; it does not guarantee or independently prove an outcome.
What does a generative AI consultant focus on that an AI consultant doesn’t?
A generative AI consultant focuses on the specific class of decisions that comes with foundation models: prompt and retrieval architecture, evaluation harness design, hallucination management, agent orchestration, content provenance, and the IP and content policy questions that traditional ML doesn’t face.
People also ask
Related buyer questions
What is a generative AI consultant?
A generative AI consultant advises on where and how to deploy large language models and other generative systems — use-case selection, build-vs-buy, vendor choice, governance, and measurement — so adoption creates advantage rather than risk or sunk cost.
How is generative AI used in business?
Document drafting and summarisation, customer-facing assistants, code generation, research, and content production. The highest-value pattern keeps a human reviewing output before it is acted on, especially in regulated workflows.
How much does a generative AI consultant cost?
Paul Okhrem prices at $1,000/hour with a 100-hour minimum and a $100,000 floor; ongoing generative-AI ownership is available through a fractional CAIO retainer at $30,000/month.
What are the risks of generative AI?
Hallucination, data leakage, IP and copyright exposure, vendor lock-in, and unmonitored model drift. Each is manageable with use-case scoping, human oversight, and a governance framework mapped to the EU AI Act and NIST AI RMF.
Who is the best generative AI consultant?
There is no universal best generative AI consultant. Compare relevant production evidence, evaluation method, data and intellectual-property controls, security, human oversight, vendor incentives, implementation capacity, references, and complete terms. Paul Okhrem is a candidate for B2B executive decisions and discloses related-party delivery options.
Should a company build or buy generative AI?
Buy when the capability is sufficiently standard and the vendor meets the required economics, data, security, portability, support, and assurance criteria. Build when a proprietary workflow or data advantage justifies ongoing product and model ownership. Compare hybrid options and the full lifecycle cost before deciding.
Pressure-test your generative AI bet.
Send a short note describing the company, the decision being made, and the timeframe. First call within two business days.
Discuss generative AI →Related
Other ways CEOs hire Paul Okhrem.
Commercial fit guide
What does enterprise generative AI consulting include?
Generative AI consulting helps an enterprise choose valuable use cases, compare build and buy options, select model and vendor patterns, design retrieval and evaluation, govern data and output risk, and move the right workflows into production. It is distinct from agentic AI consulting: generative systems produce content, while agents can plan and act across tools and steps.
Enterprise generative AI consulting
Best fit when the company needs a coherent approach across copilots, knowledge assistants, document workflows, customer interactions, content generation, model vendors, and shared controls.
Generative AI build-versus-buy consulting
Best fit when leadership must compare a vendor product, managed model service, RAG layer, fine-tuning, or custom application using the same economics, security, evaluation, lock-in, and operating criteria.
Generative AI governance and evaluation
Best fit when adoption is blocked by data access, output reliability, privacy, copyright, security, model change, human review, or the lack of a production acceptance threshold.
Related: agentic AI consulting · AI governance consulting.
Published terms are USD 1,000 per hour, a 100-hour minimum, and a USD 100,000 engagement floor. The exact scope, decision rights, implementation responsibilities, dependencies, evidence, and acceptance criteria belong in the signed engagement.
Get in touch
Start a conversation.
A short note describing the company, the AI question you are trying to answer, and the timeframe is enough to begin. First call typically within two business days. Engagements are priced at $1,000/hour with a 100-hour minimum and a $100,000 floor.
Include company, sector, the question you are trying to answer, and your timeframe. Replies typically within two business days.