01You need a plan
Point A analysis
Identify a growth constraint and plan the first test.
- Challenge
- Priority
- Plan
Marina Rakhimzhanova · Venture Building
Real AI value for real business
I identify market opportunities, bring specialist teams together and test ideas in real projects. AIStart360 provides the methodology; JOIN LAB connects people for collaborative work.
For investors and strategic partnersReal customersReal processesMeasurable outcomes
MARINA RA LAB / 02 ACTIVE
Projects in focus
Human × AI × Business
01 / WHY
Business has enough AI presentations. What it needs is evidence.
Companies launch assistants and automation, yet often cannot answer which customer problem AI solves, which metric should change, or whether the solution deserves to scale after the pilot.
“
We do not start with technology. We start where a customer or a business loses time, money, trust, or an opportunity to grow.
01 / BUSINESS SERVICES
02You have a process and data
AI pilot
Test one workflow: solution quality, cost and business value.
- Hypothesis
- Test
- Decision
02 / GRI × GTM GROWTH SYSTEM
6 AI-powered
growth levers
We develop the AIStart360 methodology as one connected growth system: GRI identifies the binding constraint, the GTM map turns it into execution, and a field pilot tests the result with real customers.
AIStart360 methodology · Marina Rakhimzhanova ↗GRI03 / ACTIVATE
Growth levers
The mechanisms that turn readiness and go-to-market execution into measurable growth.
Target economics from the GTM modelLTV / CAC ≥ 3CAC payback 1–2 monthsRepeat revenue 40–60%MoM growth 15–25%
Explore the GRI & GTM library →03 / IMPLEMENTED CASE
HONOR: from a business challenge to a connected system.
Our role, implemented modules and the basis for the results. Owner-reported figures and calculations are distinct from proven AI impact.
01 / RETAILTECHImplemented · all described modules · owner confirmed
HONOR — a connected outdoor retail ecosystem
Business diagnostics, GTM, commerce, equipment selection, CRM, inventory and loyalty — organised around one customer need.
Our role with HONOR Group is AI Growth Digital Partner. We work on the AIStart360 platform.
04 / METHOD
From a business challenge to proven impact.
Four phases. Each produces a clear deliverable and a decision about the next step.
- 01About 7 days
Point A analysis
Understand the challenge, customer journey and business baseline.
- Interviews with the owner and team
- Data, constraints and growth priority
DeliverableChallenge brief and baseline metrics - 021 day · intensive
GRI working session
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 - 03From 21 days + support
Business pilot
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 - 04Planning guide · 9–12 months
Repeatable growth
Embed effective solutions in processes, team routines 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
- 01
Challenge
Where does the customer or business lose value?
- 02
Diagnosis
Journey, data, constraints, and baseline metrics.
- 03
AI hypothesis
What does AI do, and what remains human?
- 04
Prototype
The smallest solution capable of testing behavior.
- 05
Pilot
Work with real customers and business processes.
- 06
Measurement
Compare the outcome with the baseline.
- 07
Scale
A playbook to improve, stop, or scale the solution.
SUCCESS CRITERION
Discuss your starting pointAn experiment succeeds not when the AI works, but when a customer or financial outcome changes.
05 / RA LAB / DECISION LIBRARY
From useful reading to your next business decision.
We identify growth constraints, examine company decisions and turn insights into testable experiments. For Retail, B2B and Service — with AI and human oversight.
06 / OWN LABORATORY · TRAVELTECH
1 MLN KM: researching the travel experience.
An AIStart360-owned project with an explicit implementation and hypothesis-testing status.
02 / TRAVELTECHOwned 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.
07 / EXPERIMENTS
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
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
- Compare ordinary selection and AI-assisted selection on comparable HONOR traffic. Define the allocation unit, baseline conversion, sample size and online/offline payment attribution before launch. Account for inventory, seasonality and discounts; without a valid comparison, do 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
- Expand only when paid conversion improves meaningfully while quality and economics hold. If evidence is insufficient, extend observation or revise 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
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
- Compare similar customer cohorts receiving personalised offers or standard service. Predefine the repeat-purchase window and account for returns. Observe for 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
- Identify which personalised offers generate additional profitable purchases that justify their cost.
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
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
- Decide whether user value justifies further product 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
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
- Record the baseline, binding business constraint and one GTM hypothesis. The accountable manager approves the budget, primary metric and stopping rules. Use a control group or phased rollout where feasible. Include integration, data, AI usage, training and human review 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
- Scale, iterate or stop the specific 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
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
- Compare ordinary work and AI-assisted work on similar B2B or service tasks. Define the quality standard, task complexity and mandatory human handoff rules before testing.
- 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
- Determine what to automate, what to keep with specialists and where human review must remain 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.
08 / AISTART360 · JOIN THE LAB
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.
Start with a challenge, a role and a way to contribute.- 01Business challenge
Find where value is lost
- 02People × hypothesis
Connect expertise and a testable idea
- 03Test → value
Measure the effect and make a decision
HONOR · 1 MLN KM · YOUR CHALLENGE
Have a challenge for the team?
Bring a business challenge09 / FOUNDER
Marina Rakhimzhanova
Venture Builder · Founder of AIStart360
My strength is identifying opportunities, connecting people with different expertise and turning hypotheses into testable business solutions. Technology supports this work; customer outcomes determine its value.
We have worked across retail, SaaS, FMCG, banking, pharma, logistics, and technology projects in Central Asia and Eastern Asia.
+300%year-over-year growth in one project
×2revenue growth to ≈ ₸2.5B
25people in the sales team built
Our role is to connect AI with real business constraints and turn experiments into a repeatable growth system.
10 /Learning and professional networkConnecting learning, research and professional relationships with practice.Read moreShow less
LEARNING & PROFESSIONAL NETWORK
Platforms where we learn, research and develop the Human × AI × Business practice.
11 /Kazakhstan and Central Asia contextWorking with the region’s market, teams and customer behaviour in mind.Read moreShow less
AI IN CENTRAL ASIA
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.
AI readiness × growth
We diagnose the customer, data, process and organizational conditions that determine whether an AI use case can create value.
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.
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
12 / QUESTIONS LEADERS ASK
AI implementation: the practical answers.
01How should a company start implementing AI?+
Start with a business constraint or customer problem, not a tool. Define the baseline, the Human × AI workflow, the expected mechanism and one primary business metric before selecting technology.
02What is the difference between AI readiness and AI value?+
Readiness shows whether the organization has the data, process, skills and governance to act. Value is the measurable change in customer, operational or financial outcomes after an intervention.
03Can we begin with one AI pilot?+
Yes. A bounded 90-day pilot is often the best start when it has a clear owner, baseline, control or comparison, primary metric, guardrails and a predefined Scale / Iterate / Stop decision.
04What does the Public Lab publish?+
We publish methods, experiment design, lessons and non-confidential evidence. Company data, personal data and commercial secrets remain private.
13 / FROM AI HYPOTHESIS
Do not implement AI blindly. Prove the value first.
Discuss your challengeHuman-Centered AI × Real Business × Measurable Growth
