AI Data Layer.

    Data that works, for humans and AI

    This is for you if...

    Your AI is only as good as the data underneath it. Right now, it cannot read your business.

    Your AI gives answers your team quietly fixes by hand before anyone sees them.

    You have data in five different places and no single source of truth.

    Every AI project you start hits the same wall: the data is not there, or it is not right.

    You have built on messy data before. You know how it ends.

    DELIVERABLES
    Operations data map
    Business definition library
    Agent context layer
    One rebuilt workflow
    Maintenance guide

    Your operational data, mapped end to end.

    Your business's definitions, captured authoritatively.

    Definitions, rules and processes made retrievable for AI.

    One workflow rebuilt at the root cause and validated on real data.

    Documentation so any team member can keep it running.

    Operations data map
    FieldSourceTypeAI-ready
    revenue_mrrSalesforceFLOAT
    customer_segmentHubSpot CRMTEXT
    pipeline_stageHubSpotENUM
    churn_risk_scoreInternalFLOAT
    Definition library
    TermOwnerStatus
    MRRFinance
    approved
    Customer segmentSales
    approved
    Churn riskData team
    draft
    Pipeline stageRevOps
    draft
    Agent context layer
    QueryWhat is our definition of churn?
    kb-fin-001MRR is calculated as total monthly recurring revenue excluding one-time payments.
    relevance0.94
    kb-sales-003Customer segments: Enterprise (>500 seats), Mid-Market (50-500), SMB (<50).
    relevance0.87
    kb-ops-012Churn is confirmed when a subscription is not renewed within the 14-day grace period.
    relevance0.81
    One rebuilt workflow
    Before
    Schema mismatch ignored
    No output validation
    Breaks on real data
    No alert on failure
    root cause
    Schema
    drift
    After
    Schema validated on ingest
    Output type-checked
    Tested on real data
    Alert on any failure
    Maintenance guide
    SectionOwnerIntervalDone
    Data schema referenceData teamOn change
    Definition updatesRevOpsMonthly
    Context layer syncEngineeringMonthly
    Integration health checkDevOpsQuarterly
    CASE STUDY

    It works.

    Quick-commerce company // unicorn valuation

    A quick-commerce company at unicorn valuation had scaled faster than its systems. Master data, inventory, supplier ordering, and fulfilment all lived in ungoverned Google Sheets. The data was incomplete, inconsistent, and impossible to build on. Analytics didn't work. Demand forecasting couldn't run. Losses from the gap were estimated at $2.5M per month. We built a commercial ops platform that gave all of it a proper home, readable by both humans and algorithms.

    Key Results

    • $2.5M/month in losses eliminated
    • 80% of fulfilment data entry automated
    • Order errors reduced by 35%
    FAQ

    Frequently Asked Questions

    GET STARTED

    CONTACT US

    Value Iteration