Mappings in hours, not weeks
AI reads your schemas and drafts the mapping for you. The work that used to mean days of tracing fields by hand and writing transformation code becomes a review, not a build.
Deterministic where it counts
AI helps you design the mapping. It never touches your data at run time. The transformation runs as compiled XSLT, so the same input always produces the same output. Every run is repeatable, testable, and auditable.
Scale without the mapping sprawl
A shared canonical model means you connect each format once. Add a new source or target and it works with everything already connected. No separate mapping for every pair of systems.
You stay in control
Nothing runs until you approve it. Every proposed mapping and rule is visible, explainable, and editable. You review, adjust, and lock decisions before a single document is transformed.
Solution overview
Atlas in action
Upload
Upload your sources and target sample files. You can also include XSD schema or Schematron rules if these exist.
Schema analysis
Atlas parses each schema using AI and builds a full picture of its structure: fields, types, hierarchies, and constraints. It surfaces the shape of your data clearly, so you are not reading raw XSD or EDI specifications by hand. It handles the parts that are easy to miss:
- Nested and repeating structures
- Optional and conditional fields
- Data types and value constraints
Mapping generation
In seconds, AI proposes a field-by-field mapping between your source, the canonical model, and your target. It matches fields by meaning and context, not exact name matches alone, so it handles differences in naming and structure across systems. Each proposed mapping comes with a reason, so you can see why it was suggested.
Review
You see every proposed mapping before anything runs. Confirm what is right, correct what is not, and give attention to anything Atlas flags as low confidence. In this step you:
- Accept or adjust each field mapping
- Resolve unmapped or ambiguous fields
- Add notes and lock decisions for the team
Nothing moves forward until you approve it.
Rules generation
Alongside the mapping, Atlas generates validation rules that check your input against business logic, in the same spirit as Schematron assertions. They confirm the data is well formed and correct before it flows through, so problems are caught at the source rather than downstream. AI drafts the rules from your schemas and samples, then you review and refine them. Typical rules read like plain statements of intent:
- Currency must be a three-letter uppercase code
- VAT rate must be zero or greater
- Line-item amounts must add up to the document total
XSLT creation
Atlas compiles your approved mapping into standard XSLT: the transformation that reshapes source data into your target format. Your validation rules run alongside it as deterministic checks. The output is clean, standard-based, and portable. Because it is XSLT, you can inspect it, test it, and version it like any other code artifact.
Transform
Run your documents through deterministic transformation to produce consistent and reliable output in the exact target format. The transformation can be run either in Atlas or using your own environment.
AI where it helps. Deterministic where it counts.
Atlas puts AI and deterministic execution in the right places on purpose.
AI handles the understanding
Reading and interpreting schemas, proposing mappings, and drafting rules are exactly the tasks where AI removes weeks of manual work. This is the design phase, and it is where Atlas saves you time.
The runtime is deterministic
Once you approve the mapping and rules, Atlas compiles them into XSLT and runs that. No model sits in your data path at transform time. The same input always produces the same output.
That split is deliberate, and it is what makes Atlas safe to run in production:
Reproducible
Every transformation is repeatable and testable, run after run.
Auditable
The logic is compiled XSLT you can read, review, and version control.
No surprises
No probabilistic output, no drift, no unexplained changes between runs.
Compliant by design
A transparent, inspectable data path stands up to audit and review.
You get the speed of AI at design time and the reliability of deterministic code at run time. You do not have to choose between them.
One canonical model in the middle
Atlas maps every format through a shared canonical model rather than connecting systems directly to each other. The difference matters at scale. Connect five sources to five targets directly and you maintain up to twenty-five separate mappings, each one breaking on its own schedule. Route them through a canonical model and you maintain ten: each format mapped once to a shared middle layer.
Add formats, not integrations.
A new source or target is one mapping to the canonical model. It immediately works with everything already connected.
Change one side without touching the other.
Update a target system and your source mappings stay untouched. The canonical model absorbs the change.
A single, consistent vocabulary.
Your data means the same thing everywhere it flows, which makes pipelines easier to reason about and maintain.
Lower maintenance over time.
Fewer mappings, cleaner separation, and less to break when a schema shifts.