Our Approach

Most AI programmes start in the wrong place. They begin with a technology choice — a model, a vendor, a platform — and work outward, hunting for processes that might benefit. By the time someone asks what this is worth on the income statement, the engagement is six months in and the answer is "it's complicated."

We start at the other end. The first artefact of every AIPIVT engagement is a one-page map of where your money actually goes — by process, by FTE-equivalent, by unit economics. From there we work backward into the workflows that, if rebuilt AI-first, would move the P&L the furthest, the fastest. Everything else — model selection, integration architecture, change management — falls out of that decision, not into it.

This is not a methodology we invented for AI. It is the same operating playbook the founder applied across fifteen years in PE-backed FinTech and trading platforms — ESW Capital portfolio, Exness, Deutsche Bank, Pepperstone. AI changes the toolkit. It does not change how operators get paid: by moving the number.

Read our take on calculating AI ROI for the framework we apply during the diagnostic phase.

P&L-first

Every recommendation we make is tied to a line on your income statement before we propose it. If we can't quantify the impact in your currency, we don't ship it. Vague "digital transformation" is someone else's product.

Operator-led, not consultant-led

The founder is in every engagement. We deliberately work with a small number of clients per quarter so the people doing the diagnosis are the same people accountable for the result. No analyst pyramid, no handoff to delivery.

Weeks, not quarters

First measurable impact lands inside the first 30–60 days. We design every phase to produce a shippable artefact, not a deck. If a workstream cannot show a P&L-visible outcome in six weeks, it is the wrong workstream. More on this in why speed matters more than perfection.

80/20 on cost drivers

We do not transform everything. We find the 20% of processes that absorb 80% of the cost, and we rebuild those. The remaining 80% gets left alone or absorbs the freed capacity. This is the single most expensive lesson most transformations learn too late.

Production-grade, not demo-grade

Our deliverables run in your production environment, against your live data, integrated into your existing systems. We do not build pilots that need a second project to operationalise them. If it cannot survive a Monday morning, we have not finished.

Compounding, not one-off

Each phase is designed so the next one starts cheaper and faster. The diagnostic instruments stay in place. The shipped automations become the platform the next ones build on. By the third workstream, marginal cost of delivery is a fraction of the first.

Phase 1 — Diagnose

We map your operating cost base by process, not by department. Two to three weeks, working alongside your operators, not in a war room. The output is a ranked list of cost drivers with a P&L-visible impact estimate against each, and a defended top three.

Typical deliverables: process-and-cost map, FTE-equivalent allocation, ranked opportunity register with $/year estimates, recommended top-3 workstreams.
Typical duration: 2–3 weeks.
You know we're done when: you can point at the top three opportunities and explain why they are the top three to someone who was not in the room.

Phase 2 — Cut TCO

We rebuild the highest-impact processes AI-first. Not "AI-assisted" — AI-first, meaning the workflow is redesigned around what AI can now do reliably, and human work is the exception path, not the default. This is where the margin shows up.

Typical deliverables: redesigned workflow specs, AI-first reference implementation in production, telemetry on cost-per-transaction before vs. after, written runbooks.
Typical duration: 6–12 weeks per workstream, parallelisable.
You know we're done when: the new unit cost is visible in your monthly close, not in a project status report. Explore the full scope of this work in our AI process optimization service.

Phase 3 — Ship SaaS

Where it makes sense, we productise. Internal capabilities that other businesses would pay for become standalone offerings — initially as features, eventually as their own P&L. This is how a cost centre becomes a profit centre, and it is the phase most transformations never reach because they stopped at "we automated it."

Typical deliverables: product spec, GTM-ready packaging, pricing model, first paying customer (internal or external).
Typical duration: 8–16 weeks from decision to first paying customer.
You know we're done when: someone outside the original team is paying for the capability. See our enterprise agent governance product for an example of this pattern.

Phase 4 — Unlock Revenue

The same AI capabilities that compressed cost in Phase 2 unlock revenue when pointed outward: faster onboarding, higher conversion, products you could not previously staff. We instrument the revenue side with the same rigour we used on the cost side.

Typical deliverables: revenue-attribution model on AI-enabled flows, conversion telemetry, capacity plan for scaling the wins.
Typical duration: runs concurrently with Phase 3, typically 8–12 weeks.
You know we're done when: there is a revenue line item attributable to capabilities that did not exist at the start of the engagement.

Phase 5 — Exit

We design every engagement to make ourselves replaceable. The capability stays with you — the team you trained, the playbooks we wrote down, the telemetry that keeps it honest. We do not run multi-year retainers. We run engagements that end. The AI Squad delivery & training service is purpose-built for this phase.

Typical deliverables: operational handover pack, trained internal owners, monitoring dashboards, six-month roadmap your team owns.
Typical duration: 2–4 weeks of structured handover.
You know we're done when: your team is shipping the next workstream without us, and the only thing we are doing is taking the call when they want a second opinion.

AIPIVT

Operator-led; founder in every engagement
P&L-first scoping; every workstream tied to an income-statement line
Weeks-to-impact, fixed-scope phases
Production-grade deliverables, ownership transferred at exit

Traditional AI consulting

Partner sells; analyst pyramid delivers
Technology-first scoping; "AI strategy" decks before P&L analysis
Quarters-to-impact, retainer-shaped engagements
Pilots and PoCs that require a second project to operationalise

Internal IT-led transformation

Pulled from existing roadmap capacity; AI is one of many priorities
Technology and vendor selection drives scope
Multi-year programme cadence; impact deferred to "phase 2"
Strong on integration, weaker on operating-model redesign

We are not arguing the other two columns are wrong. They are the right shape for some problems. We are arguing they are the wrong shape for a business that needs margin expansion this year, not a five-year strategic plan.

No PoC theatre

We do not build pilots whose purpose is to justify the next pilot. If a workstream cannot ship to production, we do not start it.

No headcount-multiplier billing

Our economics do not improve when we put more bodies on your account. We staff for the work, not for the invoice.

No rip-and-replace

Your existing systems are not the enemy. We integrate with them. The bar for replacing infrastructure is a P&L case, not an architectural preference.

No model-of-the-month

We choose the model that meets the spec at the lowest defensible cost and stick with it. Re-platforming because a new model shipped last week is somebody else's hobby.

No vendor lock-in we cannot defend

Where lock-in is the right call (and sometimes it is), we say so and show the maths. Where it isn't, we design for portability from day one. Our sovereign AI on-premise service exists precisely for clients where this matters most.

This methodology is not theoretical. It is the operating sequence the founder, Alexey Zolotarev, has applied across fifteen years in PE-backed FinTech and high-growth platforms — ESW Capital portfolio (2016–2020), Exness (2020–2023), Deutsche Bank (2011–2016), Pepperstone (2024–present). Across those engagements, the same five phases — applied to operational redesign, platform consolidation, and SaaS productisation — delivered more than $200M in documented cost reductions.

AIPIVT exists to apply that same playbook to AI transformation. The methodology has not changed; the toolkit has. Track record is verifiable at azolotarev.com.

More on the founder and senior practitioners on the About page.

How long does a full engagement take?

A complete five-phase engagement typically runs six to nine months, end to end. The first measurable P&L impact lands inside the first 30–60 days; we deliberately structure Phase 2 to ship before Phase 1 is fully closed.

How are engagements priced?

Fixed-scope, fixed-fee per phase. We publish the scope and the deliverables in writing before the phase starts. We do not run open-ended T&M arrangements; the incentives are wrong for both sides.

What if our data is not clean?

It usually isn't. Data quality is a Phase 1 finding, not a Phase 0 prerequisite. We have not yet met a process whose data was clean enough; we have also not yet met one where we could not start. The diagnostic phase is designed to work around it.

How is this different from a Big-4 consultancy?

Three things: the founder is in every engagement (no partner-sells/analyst-delivers split); we ship production code, not decks; and we are explicitly designed to end. Our economics work because the next engagement comes from a recommendation, not a retainer renewal.

Do you take equity or outcome-based fees?

Selectively, on engagements where the unit economics are clear enough to define an outcome both sides will sign to. The default is fixed fee.

Will you train our team, or do you keep the capability?

We train your team. Phase 5 exists precisely for this. We measure success partly by how quickly you stop needing us.

Why don't you publish case studies?

Our work has historically been inside PE-backed FinTech where client confidentiality is contractual. The founder's track record is independently verifiable at azolotarev.com. We would rather be honest about what we can and cannot show than dress up generic anonymised summaries as proof.

Why most AI initiatives stall

How we work