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PLATFORM

Everything in the platform serves one artifact: the load file.

Five capabilities, one loop. The AI assembles, your experts govern, the rules persist, the file releases.

01 · AI ASSEMBLY

Raw extracts in. A mapped, validated file out.

ATOM Data Lab reads your extracts, proposes the multi-table joins, and maps every field into the target template with a per-column confidence score and a plain-English explanation.

Transforms run in a safe, auditable language, every one reviewable in plain English, alongside value crosswalks and defaults. Validation passes on every generation, and a confidence and health heatmap shows where to look first.

PROPOSED JOIN · ITEM MASTER CONFIDENCE 0.96
MARA left join MARC on MATNR + WERKS, language = EN
“Basic material data joined to plant data on material number and plant. English descriptions only.”
COLUMN HEALTH · WHERE TO LOOK FIRST
■ confident■ review■ attention
COLUMN CASE FILE · lead_time_days ● NEEDS REVIEW
SOURCE
PLIFZ 10
PLIFZ 0
PLIFZ 21
TRANSFORM
COALESCE(PLIFZ, 14)
“Default lead time 14 days where blank”
TARGET
10
14
21
R. OKAFOR · PLANNING SME · thread lives with the column
Who decided 14 days for blanks, and why?
APPROVE REJECT + 4 MORE six-state verdict model
02 · REVIEW AND GOVERNANCE

“Who decided this value, and why?” has an answer. For every column, forever.

The Column Case File is a three-pane, per-column review surface: a plain-English case card, real sample rows from source to transform to target, and a six-state verdict model.

The conversation lives with the data, not in an email thread. Release is a human-only gate, and every state change, human or AI, lands in the same immutable audit log.

03 · ITERATION ENGINE

You fix a value once. ATOM never asks you about it again.

Approvals travel with the data: every column carries a data fingerprint, and unchanged data keeps its approval through regeneration. New iterations supersede old ones cleanly.

Plans re-generate against fresh source snapshots with one setting. On a typical re-release, roughly 77% of columns need no re-review.

CARRY-FORWARD · ITERATION 3 → 4 fresh snapshot 06-02
item_numberfp 9c41 = 9c41✓ carried forward
vendor_namefp 2b7e = 2b7e✓ carried forward
unit_costrule applied · fp 77aa = 77aa✓ carried forward
plant_codefp 51c0 ≠ 8d2f · data changed● re-review
roughly 77% of columns carry approval forward on a typical re-release · observed on a live engagement
04 · SCALE AND ARCHITECTURE

Sized for the ugliest table in your landscape.

10,000,000+
rows in a single generated load file, on dedicated compute. generated, not loaded
60,000,000+
rows in a single source table handled. 300+ source tables ingested on one project.
SECONDS
to generate standard files. The largest run on dedicated compute.
XLSX · CSV
outputs, with multi-sheet template support for D365-style objects.
05 · SECURITY AND TENANCY

Multi-tenant architecture with per-project isolation enforced on every request, fail-closed. Release approvals are human-only, enforced at the platform layer.

What ATOM does not do.

It doesn’t replace your experts. It puts real data in front of them.

It doesn’t auto-approve anything a human hasn’t approved before.

Its AI cannot release a file. Only people can.

It doesn’t do the physical load. Your integrator does, through a walled portal that serves only released files.

Where it sits in the market.

Categories, not vendors. You know the names.

SPREADSHEETS + CONSULTANT HOURS
ENTERPRISE MIGRATION SUITES
BUILT-IN ERP LOADERS
ATOM Data Lab
What your experts review
Mapping workbooks
Mapping specs and profiling reports
Load-error logs, after the fact
The actual load file: real data, target format, column by column
What happens to a fix
A consultant re-edits a workbook by hand
Depends on the consultants running the tool
Fix the file yourself, reload, repeat
Becomes a rule that applies to every later iteration
Re-review burden each cycle
Everything, every mock load
Varies by team
Full re-test per load
Approvals carry forward on unchanged data
Cross-vendor corridors
Yes, at manual cost
Strongest inside their home ecosystem, priced for Fortune-scale programs
Loads into its own ERP only
Any corridor: templates and rules are data, not code
Audit trail
Email threads and workbook tabs
Varies
Load logs
Every verdict, note, and release in an immutable log
Who’s accountable
Your SI’s staffing plan
Certified platform consultants
Your team
The people who built the platform, on the engagement
WHAT YOUR EXPERTS REVIEW
ATOM Data Lab
The actual load file: real data, target format, column by column
SPREADSHEETSMapping workbooks
SUITESMapping specs and profiling reports
ERP LOADERSLoad-error logs, after the fact
WHAT HAPPENS TO A FIX
ATOM Data Lab
Becomes a rule that applies to every later iteration
SPREADSHEETSA consultant re-edits a workbook by hand
SUITESDepends on the consultants running the tool
ERP LOADERSFix the file yourself, reload, repeat
RE-REVIEW BURDEN EACH CYCLE
ATOM Data Lab
Approvals carry forward on unchanged data
SPREADSHEETSEverything, every mock load
SUITESVaries by team
ERP LOADERSFull re-test per load
CROSS-VENDOR CORRIDORS
ATOM Data Lab
Any corridor: templates and rules are data, not code
SPREADSHEETSYes, at manual cost
SUITESStrongest inside their home ecosystem, priced for Fortune-scale programs
ERP LOADERSLoads into its own ERP only
AUDIT TRAIL
ATOM Data Lab
Every verdict, note, and release in an immutable log
SPREADSHEETSEmail threads and workbook tabs
SUITESVaries
ERP LOADERSLoad logs
WHO’S ACCOUNTABLE
ATOM Data Lab
The people who built the platform, on the engagement
SPREADSHEETSYour SI’s staffing plan
SUITESCertified platform consultants
ERP LOADERSYour team

See it on your own extract, not a demo dataset.

The extract challenge: 45 minutes, your data, no slideware.

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