AI is only as intelligent as the world it can see.

Klimso turns your fragmented marketing data into one warehouse a machine can reason over, then places it inside a world model of your business and your market.

Ad platforms were built to spend your budget. Explaining it was never their job.

Each platform models the world in its own shape: its own objects, its own hierarchy and its own attribution logic. Klimso translates those systems into a shared model of your business so that all the business metrics can be explained in one language.

Today that translation lives in somebody’s head, rebuilt by hand every time a spreadsheet is refreshed and lost the moment that person leaves. Klimso writes it into the warehouse instead, so the same definitions apply to every channel, every report and every agent.

Meta Google TikTok Amazon Ads Klaviyo Shopify KLIMSO TRANSLATION LAYER KLIMSO WORLD MODEL FUNNEL AWARE WAREHOUSE The same customer, creative and product keys run through every stage of the funnel. ReachEngagementSite visitFirst orderRepeatLTV impressionshook ratesessionsnew ordersrepeat rate90d LTVreach, spendthumbstopsPDP viewsAOV, CACdays to nextcontribution Any question about CAC or LTV becomes a query your team can run.

Five layers, from your systems up to the agents.

AgentsResearch, content, measurement, optimisation and automation, wrapped into one workspace.
Klimso ML modelsContent decomposition, asset scoring, media mix, incrementality, predicted LTV. Fitted on your own history.
World modelYour products, audiences, need spaces, goals and constraints, and how they relate.
Semantic warehouseOne definition per number, with the grain, the period and the source it came from.
Your systemsAd platforms, commerce, analytics, CRM, catalogue and content, each in its own shape.
Shared context

All the agents, wrapped into one workspace.

Klimso agents do not each keep a private memory. They sit in one workspace and read one world model, one warehouse and one record of what your team has already decided. A finding from the content agent is available to the measurement agent. A constraint set once holds everywhere.

That saves a growth team its most expensive hours. Nobody re-pulls the same numbers or re-explains what a segment means, and every answer is consistent with the last one. A general purpose AI assistant cannot do this. It has no durable model of your business underneath, so every session starts cold and nothing one thread learns reaches the next.

Researchreads the market
Contentreads every asset
Measurementreads what worked
Optimisationmoves the budget
Automationruns it on a schedule
Shared context
World modelwhat your business is
Semantic warehousewhat is true right now
Decision memorywhat you already tried
Policieswhat is off limits

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