Sports Nutrition Co

    Property Management Co

    Kydosa approach to outcome dream enablement

    Property Management Co stands up a new DBA (doing business as) brand, website, processes, back office, and front office using an AI-native OS.

    Property management outcome dream enablement diagram A property management company case diagram showing human builders, Codex AI FDE, knowledge workers, disposable software, a governed change node, applications and agentic workflows, OpenAI models, the ontology digital enterprise twin, and the stack of OpenAI, Cloudflare, DigitalOcean, Ubuntu, Postgres, and Kanban-style workflow tooling. Humans + digital workers After: governed AI-native operations Human builder ops / data / product Codex / AI FDE digital worker plans, edits, proposes Knowledge workers use the applications Disposable software dashboards, tools, views from ontology context returned to builder Governed Change Node durable changes only branch validate approve + merge no side-door durable updates Applications & agentic workflows portfolio ops, leasing maintenance, reporting Stack: OpenAI + Cloudflare + DigitalOcean OpenAI models run the stack retail OpenAI refined custom Ontology / Digital Enterprise Twin properties, leases, work orders OSS: Ubuntu + Postgres + Kanban + etc. reads ontology

    Appliance Manufacturing Co

    Outcome Deal Example

    The client was behind in retail.com sales at top home-improvement partners. An IT tactical ask would not achieve the outcome. The commercial model pivoted to business sales economics: trade spend, go-to-market execution, and a flexible 3-year partnership.

    $400Kinitial IT ask
    $500M+target annualized revenue lift
    0.95%of in-scope retail.com sales
    3 yearsflexible partnership term

    1. Establish the goal

    Close the online sales gap without treating it like a narrow technology project.

    The company was behind competitors in online sales at major retail partners. The value case was not just e-commerce conversion; better retail.com content and lower-funnel execution would also influence store sales.

    Revenue growth ambition bridge A graphic showing retail.com execution driving both online sales growth and influenced store sales, combining into a five hundred million dollar annualized revenue ambition. Revenue growth ambition The target is total revenue lift, not an online mix math exercise. Retail.com execution Plan Content Media Joint plans, lower-funnel spend, pages, data tower Online revenue Retail.com growth Conversion, share, basket + Store revenue Influenced demand Research online, buy anywhere Annualized revenue lift $500M+ Shoppers are omnichannel: online and in-store sales influence each other. Better online execution drives both in-store and online revenue growth.

    2. Wrong buying path

    IT budget would have locked the work into scope, not value.

    3. Trusted and Incentive Aligned

    Move to bigger trade spend budget line item. Bundle product and services

    IT cost path ~$400K
    1. Tech askNeed tools to improve retail.com sales.
    2. Strategy effortLow budget and no control over outcome.
    3. Fixed scopeRoadmap, integrations, and documents.
    4. Value trappedBusiness does not fund a path it does not trust.
    pivot Change the buyer and budget logic

    From a cost-center project to a GTM operating product.

    Business value path 3 years
    1. Trade spend poolFund from spend meant to drive retailer growth.
    2. Outcome productStrategy, control tower, FDEs, inference, and execution.
    3. Flexible teamCapacity shifts as the bottleneck moves.
    4. Aligned upsideSmall percentage of total in-scope sales.

    Flexible goal aligned operating team

    The team can solve for the constraint instead of defending a statement of work.

    Flexible GTM team Reallocate capacity as the constraint moves
    Joint business planningRetailer plans, priorities, cadence, and governance.
    Marketing spend reviewShift from broad spend to lower-funnel retail media.
    Product page executionTitles, bullets, imagery, A+ content, ratings, and reviews.
    Control tower + dataSales, inventory, content health, share of voice, and signals.
    FDEs + strategyBuild the operating tools while shaping the commercial moves.
    Leadership governanceDecisions, issue escalation, and monthly performance readouts.

    4. Commercial model

    A small percentage of total sales keeps both sides aligned without creating a cost roller coaster.

    A percentage of incremental revenue would be too volatile: either zero payment, huge upside, or a higher client price for the same outcome risk. A small percentage of total in-scope retail.com sales creates steadier economics while still tying compensation to the business result. Simple calculation, no debates about dozens of variables.

    Quarterly value build to end of year three run-rate Quarterly benefit accumulates while annualized run-rate grows to one hundred million dollars at the end of year one, two hundred fifty million dollars at the end of year two, and five hundred million dollars at the end of year three. Value build by quarter Run-rate lands at $500M at the end of Year 3; value is earned during the ramp. $500M annualized run-rate at end of Q12 ~$640M benefit generated during ramp Value earned Annualized run-rate $100M Prove it End Y1 $250M Expand it End Y2 End Y3 Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 0.95% of total in-scope sales Avoid incremental-only volatility

    5. P&L value tree

    A $400K, 3-month tactical IT project becomes a $24M, 3-year business partnership.

    Value tree showing shift in where the effort is funded and where the value is realized.

    P&L value tree and funding shift A public P&L ladder, internal brand product channel cube, and deal logic showing the original IT cost area, the funding shift to trade and GTM spend, and value realized in revenue and gross margin. Value tree / P&L view Built from public FY2025 company financials and an illustrative internal P&L cube. Brand names are scrubbed; channel structure reflects the case context. Public enterprise P&L anchor Where the dollars live Net sales $15.5B Revenue lift shows here. Trade / GTM spend contra-revenue Allowances, co-op, promo, retail media. Cost of products sold ~$13.1B Gross margin $2.4B $500M run-rate converts through margin. SG&A / IT cost area $1.6B Original ~$400K tactical strategy ask. Capex $389M Net earnings $318M Internal commercial P&L cube How the business manages growth Brands Scrubbed portfolio Products Large, small, CPG Channels Builders, retailers, groups Managed as: brand x product x channel Commercial levers The operating product works here JBP Content Retail media Assortment Control tower Governance Deal logic Shift funding, then operate value Original cost area ~$400K IT strategy ask Scoped, narrow, and underfunded. move buying center Funding moved to Trade / GTM value pool Budget source matches retailer execution, media, content, data, FDEs, strategy. operate outcome Value realized in Net sales + gross margin $500M annualized run-rate benefit flows through the commercial P&L. $24M / 3-year partnership priced at 0.95% of total sales

    Problem frame

    Planning is still wired for scarcity, but the constraint moved to demand quality.

    Sports Nutrition Co’s legacy operating ontology treated retailer POs and co-man capacity as the primary planning truth. The new graphic shows the flip in one view: most weeks now need better demand sensing, while summer still carries real supply pressure from seasonal protein and co-man constraints.

    150-180dingredient to shelf
    ~50%RTD capacity at Co-Man 1
    summerseasonal supply exception
    weeklylocked command plan

    Ontology map

    Constraint flip

    The operating constraint moved from scarce supply to noisy demand signals, but summer remains a supply-sensitive exception that the outcome twin has to preserve.

    Current flow

    From noisy demand to weekly command plan

      Outcome twin input gap

      Do not optimize until the decision questions are answerable.

      The first outcome twin tab is intentionally a question system. Once the priority questions are answered, the next version can convert assumptions into objectives, constraints, confidence bands, and weekly scenario recommendations.

      6question domains
      18locked inputs
      Teamshuman loop
      Snowflakesystem of analysis

      Value tree

      Make the right flavor, in the right region, for the right store.

      The outcome twin’s value is not just a better forecast. It protects revenue by preventing avoidable stockouts and protects cost by reducing transfers, markdowns, and trapped inventory when flavor demand splits by geography.

      Outcome twin value weekly feasible plan Revenue capture demand Cost avoid waste + moves On-shelf availability by store, week, flavor Flavor-store match right item, right shelf Promo + LTO capture upside without stockout Transfer avoidance fewer emergency moves Markdown/write-off less trapped inventory Run + freight cost co-man and lane fit Key revenue metric Store-flavor service fill rate at retailer x region x SKU Key cost metric Imbalance cost transfer miles + markdown + lost sale Example mismatch Strawberry sold out East / extra West; Chocolate is reversed.
      Store-flavor serviceAre the right flavors in the right stores?
      Regional imbalanceWhere do stockouts and excess inventory coexist?
      Avoidable transfer milesHow much product movement is caused by bad placement?
      Gross margin savedLost sales + freight + markdowns avoided.