Pilot Programme

    Validate Signal Quality in Your Environment

    Before we design a pilot, we start with a 20-minute discovery call to understand your current player intelligence stack and see if there's a fit. Most iGaming teams find the conversation valuable on its own — you'll learn what top performers are doing differently with Day 1 intelligence.

    A HumanGraph pilot tests whether early-value, VIP, and churn signals are accurate enough and useful enough to support real operational decisions — using your data, in your environment.

    No full historical depth required. Start from practical inputs. Evaluate results before wider rollout.

    Focused scope · Practical inputs · Controlled validation

    Why Pilot

    Why HumanGraph Starts with a Pilot

    HumanGraph is designed to be evaluated in a focused and low-friction way. A pilot allows iGaming to test signal quality and operational fit before committing to broader deployment — even without a perfect data environment.

    Start Without Full Historical Depth

    Useful early signals can be evaluated without depending on deep historical training data. The system is designed to work in sparse-data environments.

    Use Practical Inputs

    Pilots are designed to begin from data that is already operationally available — no warehouse migration or data-engineering project required.

    Signals Sharpen as Evidence Arrives

    Early outputs become stronger as fresh operational data accumulates. The system calibrates continuously, not just at setup.

    Pilot Scope

    Three Stages of a HumanGraph Pilot

    Each pilot follows a structured path from preparation through validation to expansion — designed to generate decision-useful evidence at every stage.

    Stage 0

    Pilot Preparation

    Clarify the use case, iGaming context, and practical starting assumptions. Define the right scope before any data moves.

    Stage 1

    Early Signal Validation

    Use practical iGaming inputs to test signal quality, interpretability, and workflow relevance. Assess whether outputs are useful early enough to support action.

    Stage 2

    Controlled Expansion

    Use the pilot outcome to determine whether broader deployment, richer data scope, or additional models are justified.

    Lifecycle

    Pilot Lifecycle

    A HumanGraph pilot is designed as a focused sequence of steps that help iGaming evaluate signal quality, workflow fit, and commercial relevance before broader rollout.

    01

    Scope the Use Case

    Define the initial problem area, such as early value detection, VIP prioritisation, or churn prevention.

    02

    Review Practical Inputs

    Start from data that is already operationally available and determine the right pilot starting point.

    03

    Generate Early Signals

    Use HumanGraph logic to produce structured early-value, VIP, or churn-related outputs.

    04

    Assess Operational Fit

    Review whether the signals are interpretable and useful inside real CRM, VIP, retention, or BI workflows.

    05

    Evaluate Commercial Relevance

    Assess whether the signals support better timing, prioritisation, or decision-making in practice.

    06

    Decide on Controlled Expansion

    Use the pilot outcome to determine whether a broader deployment or deeper integration is justified.

    01

    Scope the Use Case

    Define the initial problem area, such as early value detection, VIP prioritisation, or churn prevention.

    02

    Review Practical Inputs

    Start from data that is already operationally available and determine the right pilot starting point.

    03

    Generate Early Signals

    Use HumanGraph logic to produce structured early-value, VIP, or churn-related outputs.

    04

    Assess Operational Fit

    Review whether the signals are interpretable and useful inside real CRM, VIP, retention, or BI workflows.

    05

    Evaluate Commercial Relevance

    Assess whether the signals support better timing, prioritisation, or decision-making in practice.

    06

    Decide on Controlled Expansion

    Use the pilot outcome to determine whether a broader deployment or deeper integration is justified.

    01

    Scope the Use Case

    Define the initial problem area, such as early value detection, VIP prioritisation, or churn prevention.

    02

    Review Practical Inputs

    Start from data that is already operationally available and determine the right pilot starting point.

    03

    Generate Early Signals

    Use HumanGraph logic to produce structured early-value, VIP, or churn-related outputs.

    04

    Assess Operational Fit

    Review whether the signals are interpretable and useful inside real CRM, VIP, retention, or BI workflows.

    05

    Evaluate Commercial Relevance

    Assess whether the signals support better timing, prioritisation, or decision-making in practice.

    06

    Decide on Controlled Expansion

    Use the pilot outcome to determine whether a broader deployment or deeper integration is justified.

    HumanGraph pilots are designed to produce decision-useful evidence, not just technical output.

    Timeline

    Pilot Timeline

    A HumanGraph pilot is designed to move through a focused sequence of stages over a manageable evaluation period.

    Week 1–2

    Scope & Input Review

    Define the use case and confirm available data inputs.

    Week 2–4

    Signal Generation

    Produce structured early-value, VIP, or churn-related outputs.

    Week 4–6

    Workflow Fit

    Review signal interpretability inside real operational workflows.

    Week 6–8

    Commercial Assessment

    Evaluate relevance and decide on controlled expansion.

    Week 1–2

    Scope & Input Review

    Define the use case and confirm available data inputs.

    Week 2–4

    Signal Generation

    Produce structured early-value, VIP, or churn-related outputs.

    Week 4–6

    Workflow Fit

    Review signal interpretability inside real operational workflows.

    Week 6–8

    Commercial Assessment

    Evaluate relevance and decide on controlled expansion.

    Timelines are indicative and adapt to each iGaming's environment and readiness.

    Validation

    What the Pilot Validates

    A HumanGraph pilot is designed to answer practical business and operational questions, not just technical ones.

    Signal Quality

    Do the outputs become useful early enough to support action?

    Operational Fit

    Can the signals be interpreted and applied inside real workflows?

    Commercial Relevance

    Do the outputs support better timing, prioritisation, or decision-making?

    Scale Potential

    Does the pilot justify broader deployment or deeper integration?

    The goal is to generate decision-useful evidence in a controlled environment before wider rollout.

    Data Inputs

    What We Usually Need to Begin

    A pilot does not need to start with a perfect data environment. HumanGraph is designed to begin with practical iGaming inputs and sharpen as fresh evidence arrives.

    Strategic ContextMarket, use-case, and operating assumptions that help frame the pilot correctly.
    Early Activity SignalsPractical session, engagement, and behaviour patterns that are already operationally available.
    Early Financial SignalsDeposits, wagers, and related value signals where available.

    No direct personal identifiers are required.

    HumanGraph is designed to work with pseudonymised iGaming inputs.

    Your data stays in your control. We sign a DPA before integration begins. Questions? [email protected]
    Get Started

    Book a Discovery Call

    Book a 20-minute discovery call. We'll learn about your current player intelligence setup and share what top-performing operators are doing differently — no pitch, no commitment.

    Launch-calibrated · No history required · Graduates into the engines

    Prefer email? Reach us at [email protected]

    Andrey Lagunov · CEO

    [email protected]

    Start with a Discovery Call

    See whether early signals are accurate enough, useful enough, and commercially relevant enough — in your real environment. It starts with a 20-minute conversation.

    Focused scope · Controlled evaluation · Evidence-led expansion

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