Marina Rakhimzhanova · AI + Human Lab

Real AI value for real business

We test hypotheses on real-world projects and build AI solutions for business growth

For investors and strategic partners
Real customersReal processesMeasurable outcomes
MARINA RA LAB / 02 ACTIVE

Projects in focus

Human × AI × Business

METHOD7 STAGES
OUTPUTEVIDENCE
REGIONCENTRAL ASIA

Business has enough AI presentations. What it needs is evidence.

Enquiries come in, but too few turn into payments. Customers buy once, and revenue does not translate into enough profit. We find where results are lost and choose one change to test.

“

We do not start with technology. We start where a customer or a business loses time, money, trust, or an opportunity to grow.

01The growth constraint is unclear

Point A analysis

Your challenge
Sales or profit are not growing, and the cause is unclear.
What we will do
We will review the customer journey, data and seven GRI areas, identify the main constraint and choose what to test first.
What you will get
A priority challenge, baseline metrics and a 30 / 60 / 90-day plan with a first experiment.
What we review and the plan you receive

02You have a workflow to improve

AI pilot

Your challenge
Your team spends time on manual work. You need to understand where AI can help.
What we will do
We will compare one AI use case with the current workflow on quality, time and costs.
What you will get
Test results and a recommendation to continue, change or stop.
What we test and how we assess value

6 AI-powered
growth levers

Where is money lost: during product choice, payment or the customer’s return? We connect GRI findings with an action plan and test one change in your business.

AIStart360 methodology · Marina Rakhimzhanova ↗

AIStart360 PUBLIC LAB / CUSTOMER GROWTH LOOP

From interest to repeat purchases.

We connect the psychology of choice, product experience and repeat-purchase economics. We find a weak step and test one improvement.

One customer journey. Five points for growth.
  1. Hook
  2. Aha
  3. Habit
  4. Repeat
  5. Expand
  1. 01

    InterestHook

    The customer understands which job your product solves.

    Measure
    Paid conversion
  2. 02

    First valueAha

    The team completes a first useful work task.

    Measure
    Time to first value
  3. 03

    UseHabit

    The solution becomes part of the normal workflow.

    Measure
    Share who continue using
  4. 04

    Repeat purchaseRepeat

    The customer buys again or renews a contract.

    Measure
    Share of repeat buyers
  5. 05

    More valueExpand

    Another team uses your product to solve its own job.

    Measure
    Incremental contribution
06 · Cash flow across the customer journey
How we test the result

We use GRI findings to identify where customers get stuck. We test one change and compare it with the baseline.

Interest

The customer takes a meaningful next step.

First value

The customer achieves an outcome, not merely opens the product.

Use

The cohort returns to a useful activity.

Repeat purchase

There is a new payment, not just another visit.

More value

Customer contribution grows after costs and rewards.

Customers return when a new need arises. A return visit and a new purchase are different outcomes.

  1. 01What does the customer need?
  2. 02What blocks the next step?
  3. 03What will we change with AI?
  4. 04Which action will we measure?
  5. 05Is there profit after costs?

Customer signal → Hypothesis → Human × AI pilot → Cohort evidence → Economics → Decision

Before launch, we define a metric, timeframe and success rule. We compare with a control or similar group and account for seasonality, discounts, returns, team time and AI costs. Timing alone does not prove an AI effect.

We measure returns, new payments and profit after costs separately. We compare customers over the same period and label forecasts of future customer value as assumptions.

Scale

Evidence supports the effect, with acceptable economics and quality.

Iterate

There is a signal, but evidence or economics are not yet sufficient.

Stop

The decision rule is not met or guardrails are breached.

We communicate with customer consent and make opting out easy. Targets are benchmarks, not growth guarantees.

Need → value → repeat purchase → profit

Guides and examples

HONOR’s customer journey provides context for a Customer Lifecycle hypothesis, not proof of this model’s impact.

Target economics from the GTM modelLTV / CAC ≥ 3CAC payback 1–2 monthsRepeat revenue 40–60%MoM growth 15–25%

From a business challenge to proven impact.

Four phases. Each produces a clear deliverable and a decision about the next step.

  1. 01
    About 7 days

    Point A analysis

    We analyse the challenge, customer journey and business baseline.

    • Interviews with the owner and team
    • Data, constraints and growth priority
    DeliverableChallenge brief and baseline metrics
  2. 02
    1 day · intensive

    GRI working session

    We connect GRI findings to a growth hypothesis and a test plan.

    • Key constraint and growth lever
    • Hypothesis, metric and team roles
    DeliverableGrowth roadmap and first pilot plan
  3. 03
    From 21 days + support

    Business pilot

    We test one use case with real customers and business processes.

    • Prototype, implementation and short cycles
    • Baseline comparison and cost tracking
    DeliverableMeasured effect and a pilot decision
  4. 04
    Planning guide · 9–12 months

    Repeatable growth

    We embed effective solutions in processes, the team and management.

    • Standards, ownership and monitoring
    • Economics and a scaling plan
    DeliverableA repeatable system with clear economics

Timings are planning guides. Scope, metrics and team involvement are agreed before starting. Each phase informs the decision to proceed.

Seven steps within the methodology
  1. 01

    Challenge

    Where does the customer or business lose value?

  2. 02

    Diagnosis

    Journey, data, constraints, and baseline metrics.

  3. 03

    AI hypothesis

    What does AI do, and what remains human?

  4. 04

    Prototype

    The smallest solution capable of testing behavior.

  5. 05

    Pilot

    Work with real customers and business processes.

  6. 06

    Measurement

    Compare the outcome with the baseline.

  7. 07

    Scale

    A playbook to improve, stop, or scale the solution.

SUCCESS CRITERION

An experiment succeeds not when the AI works, but when a customer or financial outcome changes.

Discuss your starting point

A library for your business challenge.

Want to work through a challenge yourself? Choose your problem, explore a guide or company case study and plan your first test.

1 MLN KM: researching the travel experience.

Our own lab: we test demand, tour selection and the path to payment.

1 MLN KM project illustration: travellers, mountains and a route02 / TRAVELTECH

Owned project · validating demand and business model

1 MLN KM — building an intelligent travel ecosystem

Developing a curated travel marketplace connecting travellers, vetted organisers and confirmed sales. The first pilot segment is women’s curated travel for ages 33+.

An independent project and an owned laboratory: testing demand and a commission model through travel, product and technology expertise.

AI advisorRoutesGeodataCommunity

Growth hypotheses. Tested in reality.

We investigate where AI changes customer behaviour, team performance and business economics. Every experiment begins with a real problem and supports a decision: scale, iterate or stop.

Research agenda: proposed hypotheses, not completed experiments or validated outcomes. An implemented feature does not prove its economic impact.

EXP 01Proposed hypothesis

RetailTech · HONOR

Better decisions. Better sales?

Can context-aware AI recommendations increase paid conversion without compromising margins or recommendation quality?

Primary metric
Paid conversion
HONOR case study →
How we will test it
Supporting metrics
Gross profit per visitor · Decision time
Business problem
Customers struggle to choose a suitable kit for their activity, weather and body measurements. Recommendations must help a purchase, not just sound convincing.
Comparison and test design
We will compare ordinary selection with AI-assisted selection on comparable HONOR traffic. Before launch, we will define the allocation unit, baseline conversion, sample size and rules for recording online and offline payments. We will account for inventory, seasonality and discounts; without a valid comparison, we will not attribute growth to AI.
AI and human roles
AI proposes a kit using verified product data. A consultant checks complex recommendations, availability and fit for the customer's needs.
Guardrails
Recommendation errors, returns and margins. Stop if errors or returns increase beyond the agreed limits.
Decision after the test
We expand selection only when paid conversion improves meaningfully while quality and economics hold. If evidence is insufficient, we extend observation or change the hypothesis.
EXP 02Proposed hypothesis

Customer lifecycle · HONOR

From first purchase to lifetime value.

Can AI personalisation grounded in real customer needs increase profitable repeat purchases?

Primary metric
Repeat purchase rate
HONOR customer system →
How we will test it
Supporting metrics
Incremental margin · Reactivation cost
Business problem
A history of purchases and fittings does not itself bring customers back. We need a useful offer at the right moment.
Comparison and test design
We will compare similar buyer cohorts receiving personalised offers or standard service. We will define the repeat-purchase window in advance and account for returns. Observation will last 90–180 days or longer, depending on the purchase cycle.
AI and human roles
AI proposes the next relevant contact. A staff member checks the offer and contact frequency. Communications require appropriate customer consent.
Guardrails
Unsubscribes, complaints, returns and discount costs. Unprofitable discount-driven purchases do not count as success.
Decision after the test
We identify which personalised offers create additional profitable purchases and justify their costs.
EXP 03Proposed hypothesis

TravelTech · 1 MLN KM

From inspiration to trusted itineraries.

Can AI turn travel inspiration into a verified itinerary that users are willing to adopt?

Primary metric
Validated itinerary acceptance
1 MLN KM laboratory →
How we will test it
Supporting metrics
Planning time · Critical error rate
Business problem
Interest in a destination still leaves questions about time, budget, accessibility and reliable information.
Comparison and test design
Once a working prototype is confirmed, compare independent planning and AI assistance on identical tasks. Count an itinerary as accepted only if the user selects it and it passes material constraint checks.
AI and human roles
AI assembles and helps revise the plan. A person checks safety, constraints and important local facts; material claims need traceable sources.
Guardrails
Critical factual errors and infeasible constraints. Accepting an itinerary does not prove a completed trip or revenue.
Decision after the test
We decide whether the use case is useful enough for further development and a separate monetisation test.
EXP 04Proposed hypothesis

GRI × GTM · AI economics

Which AI is worth the investment?

Can diagnosing a business constraint help select AI use cases with measurable economic value after all incremental costs?

Primary metric
Incremental profit contribution
Methodology and library →
How we will test it
Supporting metrics
Payback period · Pilot cost
Business problem
Choosing technology before defining the business problem can produce working automation with no clear economic contribution.
Comparison and test design
We record the baseline, main business constraint and one GTM hypothesis. The manager approves the budget, primary metric and stopping rules. Where feasible, we use a control group or phased rollout. We include integration, data, AI usage, training and human review in costs.
AI and human roles
AI supports analysis and proposed actions. A manager owns the budget, measurement quality and final decision; an accountable specialist checks financial conclusions.
Guardrails
Data quality, operational risk and budget overruns. Where incremental impact cannot be estimated reliably, report uncertainty rather than attributing all process revenue to AI.
Decision after the test
We scale, change or stop the selected use case. One pilot does not establish the superiority of the entire GRI methodology; long payback periods require continued observation.
EXP 05Proposed hypothesis

Human × AI · B2B and services

When does AI make humans better?

Can the right division of work between AI and people improve productivity while preserving customer trust and service quality?

Primary metric
Quality-adjusted tasks per hour
Business challenges in the library →
How we will test it
Supporting metrics
Resolution time · Error rate
Business problem
Automation may speed up a response while creating errors, repeat contacts and additional work for the team.
Comparison and test design
We will compare ordinary work with AI-assisted work on similar B2B or service tasks. Before the test, we will define the quality standard, task complexity and cases requiring a human handoff.
AI and human roles
AI prepares a response or action; a person checks consequential decisions. People own customer commitments, exceptions and final execution.
Guardrails
Errors, repeat contacts, customer satisfaction and policy violations. Speed without quality is not an improvement.
Decision after the test
We identify what can be automated, what to leave to specialists and where human review is mandatory.
NEXTOpen call

Your business challenge could be our next experiment.

Where is your company losing customers, time or profit? Together, we can define a testable hypothesis, meaningful metrics and the right pilot approach.

Propose a business hypothesis →

The outcome determines the observation window

A 21-day test can assess a bounded workflow change. A 90-day business pilot includes baseline measurement, implementation and economic evaluation. Repeat purchases may require 90–180 days or more. These are planning horizons, not promises of results.

What we will publish after testing

Baseline, comparison method, period, sample size, costs, limitations and the decision. Before launch, we define the primary metric, minimum meaningful effect and stopping rules. A null or negative result can still support a useful decision.

Strong people.Bold hypotheses.Real results.

We bring specialists together around complex business challenges. We test ideas in real projects and build solutions whose value can be demonstrated.

We start with a challenge, a role and a way to contribute.
  1. 01Business challenge

    We find where value is lost

  2. 02People × hypothesis

    We connect expertise and a testable idea

  3. 03Test → value

    We measure the effect and make a decision

HONOR · 1 MLN KM · YOUR CHALLENGE

Marina Rakhimzhanova

Venture Builder · Founder of AIStart360

Portrait of Marina Ra
AI × GTMCentral Asia

We integrate GRI and GTM diagnostics, sales, analytics, and AI into a unified system for measurable business growth.

More than 15 years of experience across retail, SaaS, FMCG, finance, pharma, logistics and technology projects in Central Asia.

+300%year-over-year growth in one project
×2revenue growth to ≈ ₸2.5B
25people in the sales team built
More about Marina

Regional ecosystem

Choose a location for a future Bootcamp.

We are developing six locations and gathering interest from companies and local partners. Dates, venues and participation terms will be published once a launch is confirmed.

Learning and professional resources
11 /Kazakhstan and Central Asia contextWe account for the region’s market, teams and customer behaviour.Read moreShow less

AI implementation grounded in Kazakhstan’s real business context.

We work with companies in Kazakhstan and Central Asia to move from AI interest to a measurable operating result—through customer evidence, workflow redesign and controlled field experiments.

01

AI readiness × growth

We diagnose the customer, data, process and organizational conditions that determine whether an AI use case can create value.

02

90-day business pilot

We select one high-value hypothesis, define a baseline and KPI, test the Human × AI workflow and make a Scale / Iterate / Stop decision.

03

GTM and customer experience

We connect AI to a real customer mission, adoption mechanism, commercial metric and go-to-market plan.

Almaty · Astana · Kazakhstan · Uzbekistan · Central Asia

Let’s discuss your challenge in 20 minutes.

Tell us about one problem: enquiries that do not convert, too few repeat purchases or cash shortages despite revenue. In 20 minutes, we will clarify where to look for the cause, what data is needed and where to start.

An introductory conversation about one issue. A full audit and implementation plan are a separate engagement. Choose a time and receive confirmation through TidyCal.
Ask a question

RA LAB / LIBRARY

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