Reference architecture for AI agents
Stack, integration topology, data flow, model orchestration, observability. Defensible to the CTO, the CIO, and the next acquirer’s diligence team. The architecture that survives the next vendor consolidation.
AI implementation consulting · For CEOs & founders · From the operating side
AI implementation consultant.
Best fit when the AI strategy is decided but the implementation path will determine whether it ships, scales, and survives the next vendor cycle. Paul ships a single signed implementation architecture — with named owners, vendor commitments, milestone gates, and outcome validation — not an integrator’s SOW dressed as advice.
Definition
What is an AI implementation consultant?
An AI implementation consultant is a senior independent advisor whose product is the implementation decision artifact — reference architecture, vendor selection, build-vs-buy decisions per component, milestone gates with named executive owners, governance integration, and rollback paths — defensible to the CTO, the board, the regulator, and the next acquirer. Distinct from systems integrators who deliver code by the hour and platform partners who deliver licensed deployment, the implementation consultant is hired for the call before the integrator signs the SOW. The role exists because most AI implementations fail not on code quality but on architecture decisions made under vendor pressure, governance gaps surfaced late, and milestone gates without named owners.
Paul Okhrem operates as an AI implementation consultant for CEOs and founders worldwide. The work is from inside the company by structure: the architecture decisions Paul recommends are the same decisions he has lived with the consequences of inside Elogic Commerce and Uvik Software, including the multi-year ones. Outcomes are validated under The Proof Standard™ with client-side validation by the analytics or audit function — never by the consultant.
Where this fits
Six AI implementation decisions a CEO can’t outsource to the integrator.
Hired when the AI strategy is signed off but the implementation path is contested — vendor, architecture, sequencing, governance, ownership. The decisions that look tactical on paper and are existential in production.
AI vendor commitment & lock-in
Which platform, which model provider, which orchestration layer. Switching cost named in dollars and weeks. Multi-year commitments actually try to breaked before signature, not after.
Build vs. buy on each AI component
Where to own the workflow, where to rent the model, where to use the open-source primitive. Decisions informed by production deployments at Elogic Commerce and Uvik Software — not by analyst forecasts.
AI deployment milestone gates
Baseline week 0, pilot week 4, production week 8–12, validation week 16–24. Each gate has a named executive owner, a measurable threshold, and a published rollback path.
AI governance & risk integration
Implementation choices that survive a regulator visit, an audit, an SOC 2 review, an EU AI Act conformity assessment. Risk integration designed in, not bolted on.
Integrator & AI vendor management
Sit opposite the integrator or the platform partner in scoping. Stress-test the SOW. Defend the architecture against scope drift, fee inflation, and capability decay.
How it works
The four-step AI implementation framework.
A structured engagement designed to produce one defensible implementation memo — not a slide deck, not three options dressed as a recommendation. Outputs validated under The Proof Standard™.
Step 1 — Architecture challenge
Document the proposed reference architecture. Surface the three to seven unstated assumptions every AI architecture rests on. Name the integration risks, the data dependencies, the talent fragility points. Map the failure modes the team has stopped seeing because they’ve been there since the start.
Step 2 — Vendor & build-vs-buy decision
One defensible vendor recommendation per layer. Switching cost quantified in dollars and weeks of engineering time. Lock-in named, not assumed. SOW reviewed before signature, not after. Every multi-year commitment passes the “next acquirer” test — will the buyer assume this contract, or write it off.
Step 3 — Milestone gates with named owners
Baseline, pilot, production, validation — four gates with measurable thresholds. Each gate has a named executive owner on the client side. Rollback paths published before launch. The implementation memo is signed by the CEO, not the consultant.
Step 4 — Outcome validation under The Proof Standard™
An 8–12 week measurement window post-go-live. Validation by the client’s analytics or audit function — never by the consultant. The result is defensible to the board, the regulator, the auditor, or the strategic acquirer’s diligence team in 48 hours.
The deliverable
What is inside the implementation memo.
The product of the engagement is a 15–30 page implementation memo — the artifact a CEO walks into the next board meeting with. Every section answers a specific decision the board, the integrator, or the next acquirer will challenge.
- Executive summary & one-page architecture diagram — the implementation thesis in numbers the CFO recognises and a diagram the CTO can defend in 90 seconds.
- Reference architecture, end-to-end — data flow, model orchestration, retrieval surfaces, observability, identity and access boundaries, the integration topology with adjacent systems (ERP, CRM, commerce, finance).
- Vendor and build-vs-buy decisions per component — named vendor, named open-source alternative, switching cost in dollars and weeks, lock-in named, contract terms reviewed before signature.
- Milestone gates with executive owners — baseline week 0, pilot week 4, production week 8–12, validation week 16–24. Measurable thresholds. Named owner per gate. Published rollback paths.
- Governance and risk integration plan — controls mapped to the company’s existing audit and compliance posture. Designed for SOC 2 review, EU AI Act conformity, sector regulators where applicable.
- Outcome validation protocol — baseline metrics frozen, intervention dated, validation method specified, measurement window scheduled, sign-off owner named on the client side.
- Decision log & alternative paths considered — every recommended decision is traceable. Every rejected alternative is documented with the reason. Defensible in diligence even if Paul is no longer involved.
Engagement formats
Two engagement shapes; one decision artifact.
Scoped to the decision
The most common format. 100–200 hours over 8–16 weeks. Output: the implementation memo. Hand-off to the chosen integrator with a defensible scope. Paul stays through the architecture actually try to break, vendor signing, and milestone gate definition. The integrator executes against the memo.
Best for: companies with capable internal engineering or an existing integrator relationship that needs the implementation decision argue againsted by an independent operator.
Full milestone arc as fractional CAIO
The longer format. Engagement converts into a fractional Chief AI Officer retainer covering one to three days per week through the full implementation arc and the 8–12 week outcome validation window. Paul holds the AI executive seat at the leadership table.
Best for: companies where AI implementation is a board-level priority and the executive team needs ongoing AI leadership coverage during the deployment cycle.
Why from a practitioner
Why this implementation work is from someone who's shipped this, not integrator-grade.
- AI agents in production inside Elogic Commerce (200+ specialists) and Uvik Software (Python-first, 50–249 engineers), generating roughly 30% operational efficiency gains across both companies.
- Architecture decisions Paul recommends are decisions he has lived with the consequences of inside his own P&L — including the multi-year ones, including the ones that didn’t pay back as planned.
- Vendor-neutral by structure: no platform partner margin, no integrator referral fees, no model-provider sponsorship. The recommendation is the product. Selectivity is the structural defense — a small number of clients per year, KPI-committed, outcome-bound.
- Outcomes validated under The Proof Standard™ — the published five-component measurement protocol with client-side validation. The engagement record is designed to survive a regulator, auditor, or acquirer reading it cold.
- Magento Community Engineering Award at Adobe Imagine 2019; Adobe Solution Partner; Hyvä Bronze Partner. Operator credentials cross-checked across two production software companies with 500+ projects shipped.
Frequently asked
Common questions about this engagement.
What is an AI implementation consultant?
An AI implementation consultant is a senior independent advisor whose product is the implementation decision artifact — reference architecture, vendor selection, build-vs-buy decisions, milestone gates with named executive owners, governance integration, and rollback paths — defensible to the CTO, the board, the regulator, and the next acquirer. Distinct from systems integrators who deliver code by the hour and platform partners who deliver licensed deployment, the implementation consultant is hired for the conversation before the integrator signs the SOW.
How is this different from hiring an integrator or a platform partner?
Integrators and platform partners deliver code, hours, and platform deployment. Their commercial structure is volume-of-implementation. An independent AI implementation consultant takes no integrator referral fees, no platform partner margin, no vendor commission. The deliverable is the architecture and vendor recommendation, with the integrator or platform partner hired afterward to execute against a defensible scope.
When does a CEO hire an AI implementation consultant versus an AI strategy consultant?
Strategy consulting resolves whether and why — vendor commitment, capital allocation, transformation thesis. Implementation consulting resolves how, by whom, and on what timeline — reference architecture, build-vs-buy per component, milestone gates with named owners, governance integration. Many engagements move from strategy to implementation; the same from the operating side standard applies.
What does the engagement output look like?
Typically a 15–30 page implementation memo containing the proposed reference architecture, the vendor and build-vs-buy decisions per component, the milestone gates with named executive owners, the governance and risk integration plan, and the outcome validation protocol. Plus working sessions with the executive team, CTO, and integrator. Outcomes validated under The Proof Standard™ in the 8–12 week measurement window post-go-live.
Can Paul stay through implementation, or is the engagement scoped to the decision?
Both formats run. Many CEOs hire Paul scoped to the decision — architecture and vendor recommendation, then hand off to an integrator with the defensible scope in hand. Others convert the engagement into a fractional CAIO retainer covering the full milestone arc through outcome validation. The decision artifact is the same; the duration of executive coverage differs.
Does Paul work with existing integrator or platform relationships?
Yes. Paul sits opposite the integrator or the platform partner in scoping. The independence is structural: no margin, no commission, no referral. Existing vendor and integrator relationships are stress-tested, not displaced — and the SOW is reviewed before signature, not after.
How is independence preserved on AI implementation engagements?
By structure. Paul takes no platform-partner margin, no integrator referral fees, no model-provider sponsorship. Engagements are scoped fixed-fee or hourly with the deliverable being a named call, not a sales pitch for a downstream service. Selectivity is the structural defense — a small number of clients per year, KPI-committed, outcome-bound.
What does an AI implementation engagement cost?
$1,000/hour, 100-hour minimum, $100,000 floor. Most scoped-to-decision engagements run $100,000–$200,000 over 8 to 16 weeks. Full milestone-arc engagements as fractional CAIO retainers run separately under fractional pricing — see the pricing page.
What does an AI implementation consultant actually do?
An AI implementation consultant translates a strategic AI decision into a build plan: reference architecture, vendor and model selection, build-vs-buy calls per component, governance integration, milestone gates with named owners, and rollback paths. Implementation consultants differ from AI strategy consultants in scope — strategy stops at the decision; implementation owns the path from decision to production.
How is AI implementation different from general AI consulting?
AI consulting often stops at the slide deck. AI implementation is the work that follows: defending architecture choices in the build, holding the rollback decision, and signing the milestone gates. Most large AI consulting firms hand the implementation to a separate integrator, which is where decisions get diluted.
Who needs an AI implementation consultant?
Companies with one of three profiles: existing engineering depth that needs an external architectural lead for an AI deployment, organisations where AI implementation in business has stalled past pilot, or boards needing an independent technical reviewer ahead of a multi-million-dollar AI commitment.
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