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Marketplace
One hub. Every building block.
Marketplace lists every component a modern data and AI strategy needs — data, skills, connectors, providers, packages, and workflows — typed, versioned, and ready to install. Pick the parts. Compose the work. Run it wherever your team builds.
Data and AI work shouldn’t force a trade-off
Single-category marketplaces sell access and stop there. Vertical AI platforms bundle every layer and lock the stack. DIY stacks rebuild the same plumbing every time. The build cost stays high — and architectural freedom stays low.
Single-category marketplaces stop at sale
Buying data is just the start. Connecting it to a harness, a model, or a workflow is still a custom project.
Vertical platforms lock the stack
Model, harness, agents — bundled. Swapping any layer means leaving the platform.
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DIY stacks reinvent the plumbing
Open source gives flexibility, but every team rebuilds the same components from scratch.
When every building block is a click away
Here’s what work looks like when the components are listed, typed, and ready to install.
Compose end-to-end
Snap data, skills, connectors, providers, packages, and workflows together. One step’s output feeds the next with no glue code required.
Bring your own everything
Your cloud, your harness, your model. Components plug into the stack you already run.
Skills that travel
MCP-native skills run inside Narrative, inside Claude, or any AI tool your team already uses.
Buy, sell, or exchange
Source what you need, publish what you have — governed by the same Access Rules end to end.
Stop rebuilding what should already exist
Ready for deployment inside your cloud, with every contract, every rule, and every control already wired in
Every attribute and step, aligned on arrival.
Marketplace lists every component your stack needs — data, skills, connectors, providers, packages, workflows — under one Listings API. Built on open standards including MCP, so nothing you pick up gets locked into a single harness, model, or cloud.
How it works
From browsing to building in a few steps.
Shop the Marketplace by component type and see how the pieces fit. Chain components end-to-end and use it wherever you work.
Different categories, one hub
Data, skills, connectors, providers, packages, and workflows — listed, typed, versioned, and queryable under one Listings API, with media, availability, and bundle relationships in-line.
Marketplace of providers
Source the exact raw data your use case requires — governed, normalized, and ready to query.
Unified contracts + Access Rules
Buy or monetize on your own terms. Access Rules manage every transaction, governed and tracked end to end — so procurement drag disappears, and so does monetization drag.
AI-Ready
The same skill runs inside Narrative, inside Claude, or in any MCP-compatible harness your team already uses. Install once. Run anywhere.
One marketplace. Every side of the work.
Three ways to put Marketplace to work — whether you’re composing the stack, publishing into it, or operating it for clients.
For data and AI leaders
Stop choosing between flexibility and leverage.
Compose the stack you need from listed, typed, versioned components — without committing to one vendor’s harness, model, or cloud. Every component is there to pick up, snap into your workflow, and swap when something better lands.
For buyers
Source the attributes, skip the bulk license.
Find the exact raw data, skills and connectors your use case requires. Activate it against your own data inside your cloud — no procurement lag, no engineering ticket, no big commitment before the use case proves out.
For sellers
Monetize on your own terms.
Package your data by attribute, set the rules that govern how it’s used, and earn without shipping a single row. Access Rules enforce your pricing, usage rights, and retention at the query layer. You keep the source, buyers get the signal, and the revenue model stays yours.
WHY IT’S DIFFERENT
Marketplace Truths: The Principles Behind Composable Data & AI Work
Composability beats integration.
Marketplace lists every component independently under one Listings API, so you can swap the model, change the harness, or move clouds without re-platforming.
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Install beats build.
Single-category marketplaces sell access and stop there. DIY open-source stacks rebuild the same plumbing every time. Our Marketplace closes the gap: components are typed, versioned, and listed with what they pair with — so the assembly happens before you commit, not after.
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Open standards beat lock-in.
Skills are MCP-native. A skill listed in Marketplace runs inside Narrative’s tool-calling harness, inside Claude via the Claude Skills Marketplace, and inside any other MCP-compatible runtime. The model layer is versioned separately, so swapping LLMs doesn’t mean rewriting skills.
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A marketplace should go both ways.
Most “marketplaces” only let you buy. Marketplace lets you source, publish, and exchange, governed by the same Access Rules, the same contracts, the same cloud.
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Proven results
Composable. Every component.
6
component categories in one hub
30+
providers in one marketplace
100%
MCP-native, runtime-portable, governed at the query layer
“Partnering with Narrative.io has empowered us to seamlessly scale our offerings across diverse social platforms. Ultimately, this collaboration has been key to achieving our objective: engaging with our customers exactly where they are."
Dennis O'Donnell, Head of Ad Product
The Weather Company
“What I am looking for is a #RosettaStone. I don’t have the resources to pick through endless data sets and clean and harmonize them. I am calling it the great marketing emergency. We’ve got all this data, but we need #AI to stitch it together as a means to help our clients drive growth. We have the ability to have a fluid conversation with the consumer at the different points in their journey.”
Domenic Venuto, Chief Product & Data Officer
Horizon
“Traditional commerce media models often expose brands to unnecessary privacy risks by moving data into third-party environments. Our work with Narrative eliminates that risk while unlocking sophisticated audience-building capabilities that deliver real outcomes.”
Marni Schpario
Block
Resources
Insights, stories, & resources for the teams building modern data infrastructure.
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Ask Us Anything
Straight answers to real customer questions.
Most teams are normalizing live data within days of connecting their first sources — not months. There's no multi-quarter implementation, no professional services dependency, no bespoke build required. You connect your sources, define what coherence looks like for your use case, and Narrative does the translation work. The timeline question is usually less about setup and more about how quickly your team can act on data that's finally consistent.
Your warehouse stores data. Your CDP activates it. Narrative normalizes it — resolving the semantic inconsistencies that make data from different sources incompatible before it ever reaches those tools. We don't replace your stack. We fix the layer underneath it that your stack assumes is already solved.
Most teams do, at first. The problem isn't the initial build — it's everything after. Every partner schema change breaks it. Every new data source requires rebuilding it. Every team transition means relearning it. The engineering debt compounds faster than the business value accrues. Narrative replaces a perpetual maintenance burden with infrastructure that's designed to absorb that complexity so your team doesn't have to.
Those tools move data and act on it. They don't normalize it. They're built on the assumption that the data arriving is already clean, consistent, and semantically coherent — and in most real-world data partnerships, it isn't. Narrative is the layer that makes your existing collaboration and activation infrastructure work the way it was designed to. The teams getting the most from their data stack are typically the ones who've solved normalization first.
Data and analytics teams at companies where external data is a core business input — not a supplement. Typically organizations that are buying data at scale, monetizing their own data assets, or running structured data partnerships with other companies. If your team is spending meaningful engineering time just making external data usable, that's the problem Narrative is built to eliminate.
AI models don't tolerate inconsistency. When a "user" in one dataset isn't recognized as the same "user" in another — different schemas, different taxonomies, different identifiers — your models train on noise and your outputs reflect it. Narrative normalizes data at the source so the AI layer above it is working with signal. Garbage in, garbage out isn't an AI problem. It's a normalization problem.