ROSETTA STONE NORMALIZATION ENGINE
Normalized data opens doors
Rosetta Stone Normalization Engine rewrites incompatible datasets in a singular, universal language. What used to take weeks of engineering, now happens automatically at query time.
Incompatible data comes at a cost
Somewhere between mapping spreadsheets and stalled partnerships, you pay the price. Every week, across every team, on every new integration.
Constant integrations
Map the fields, build the pipeline, and pray nothing breaks.
Campaign delays
Incompatible data costs you time and partnerships.
Underutilized talent
No time for strategy, or anything that drives the business forward.
What changes when your data can communicate
Here’s what your data can look like on the other side.
No pipeline rebuilds
Upstream changes no longer break everything downstream.
Go live fast
Measure in days instead of engineering sprints.
Auto data mapping
Third-party and partner data works together automatically.
More time on decisions
Redirect attention from monotonous data prep to tasks that matter.
Stop productivity from getting lost in translation
Ready for deployment when you need it without custom pipelines, schemas, or interruptions.
Rosetta sets the standard
Rosetta Stone reads whatever format your data arrives in and maps it to a common language automatically.
Schema changes stop being your problem
When something changes upstream, Rosetta adjusts. Your pipelines stay intact and your team stays focused.
You get back to work that matters
Automating data prep turns hours spent mapping fields and rebuilding pipelines into decisions, strategy, and partnerships.
How it works
The best tech is the kind you don’t need to think about
Rosetta Stone runs inside your existing environment, normalizing data at query time without any upfront setup or ongoing maintenance from your team for consistent, clean data when you need it.
Rosetta Stone Attributes
A universal catalog that acts as a shared language across every source, every partner, every query.
Automated mappings
AI generates field-level mappings at query time, circumventing traditional ETL bottlenecks.
Narrative Anywhere
Normalization runs inside your own cloud environment using Cortex, Bedrock, or fine-tuned models.
Row-level classification
ML and LLM-powered classification handles the edge cases when schema logic can't.
Your team. Your data. Neither belong in silos.
For marketers
Build segments, activate campaigns, and measure outcomes on data that's already aligned to your schema.
For analysts
Query across first-party, partner, and marketplace data the moment it lands with every field already mapped, and every value already aligned.
For engineers
Replace per-source mapping, per-schema reconciliation, and per-integration babysitting with a normalization layer that runs at query time.
Why it's different
Rosetta Stone Truths
- Normalization belongs at query time.
- Mapping is a machine's task.
- In-house data processing is secure data processing.
- Your schema is yours.
Normalization belongs at query time.
Rigid ETL pipelines normalize data upfront. Changes upstream? Back to the drawing board.
Mapping is a machine’s task.
Rosetta's ML identifies that attributes like "gender," "is_female," and "sex_code" all mean the same thing.
In-house data processing is secure data processing.
Running normalization inside your own environment is safer and more convenient for all.
Your schema is yours.
When you train on your own custom attributes with Rosetta, that knowledge becomes your leverage, not theirs.
Proven results
Hours and budget back. Data working.
- 19% revenue increase from existing data
- 99.9% reduction in classification cardinality
- 64 languages normalized
Reach without compromise
“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. We’ve got all this data, but we need #AI to stitch it together as a means to help our clients drive growth."
— 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
Ask Us Anything
How long does it take to see value?
Most teams are normalizing live data within days of connecting their first sources.
We already have tools for data sharing and activation. Where does Narrative fit?
Those tools move data and act on it. They don't normalize it.
Why not build a normalization layer in-house?
The problem isn't the initial build — it's everything after.
Who is Narrative built for?
Data and analytics teams at companies where external data is a core business input — typically organizations that are buying data at scale.
Why does data normalization matter more now that AI is involved?
AI models don't tolerate inconsistency.
How is Narrative different from my data warehouse or CDP?
Your warehouse stores data. Your CDP activates it. Narrative normalizes it.