For implementation teams at vertical SaaS

Migrate any client's data in minutes.
No developers.

Tell an AI agent your goal in plain English, and it maps, transforms, and validates each file. You supervise, approve, and keep every onboarding as a reusable template.

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www.dataflowmapper.com

§ 01Capacity

An asset, not a one-off

Run a four-person team like it’s seven.

Capacity compounds with each migration.

Customer

Blaine Roberts

Director of Partnerships

Without this, we couldn’t have migrated them. We didn’t have the bandwidth, the tooling, or the resources. In a couple hours we had a repeatable template running their data exactly how we needed.

Velocity

2–3×

more client go-lives
per implementation rep

Engineering load

Zero

engineering work to
onboard the next client

Asset growth

+1/run

reusable template added
with every onboarding

§ 02Autonomy

Two paths

Delegate it,
or drive it.

Hand the data to the autonomous pipeline, or steer an AI copilot at the level of autonomy you choose. Anywhere from auto-approve to off. Every onboarding produces the same editable template, in your library, yours to keep.

manualautonomous

01Hand it off

Autonomous pipeline.

Source files in, editable template out. The agent maps, transforms, validates, and self-corrects across cycles, flagging review items as it goes.

Runs in the background·You approve the output

app.dataflowmapper.com/pipeline

DataFlowMapper autonomous pipeline running

Runningcycle 3 of 4

AssetEditable template

02Drive it

AI copilot.

A goal in plain English, or your hands on the wheel. The copilot navigates and executes any feature in the app, at the permission level you set. Auto, per-step, or off.

You drive·Python escape hatch for edges

app.dataflowmapper.com/mapping

Autonomy

manualper-step

You have the wheel

§ 03Toolkit

What you build with

Four tools, one surface

Build mappings once, reuse often.

Mapping, transformation, validation, enrichment. The work your team does on every onboarding. Here’s what each looks like in the workbench.

01·Map

Map fields visually.

One mapping card per field: source, destination, logic, & validations. Drag, drop, or let the AI handle it all.

Drag & dropNested JSON

mapping editor

12 of 14 mapped

Source

InvoiceDate

Destination

Invoice Date

Logic

Source

Amount

Destination

Total

Logic

Amount * fx_rate

Source

CustomerRef

Destination

Account ID

Logic

AI suggested

logic builder

Saved · order_status

Status

equals

"PAID"

AND

Amount

> than

0

then

order_status

"fulfilled"

else

order_status

"pending"

02·Transform

Logic without code.

Visual rules that compile to Python. 50+ functions, AND/OR groups, and a Python escape hatch when an edge case needs one.

If / thenPython escape

validation

3 errors

3 errorsfilter5 / 480 rows

Emailstr

Countrystr

Amountnum

Datedate

01
a.diaz@acme.io
US
1,284.50
2025-04-12

02
noreply@
CA
890.00
2025-04-12

03
k.wu@beta.co
XX
2,140.00
2025-04-13

04
m.olsen@x.dev
SE
-12.00
2025-04-13

05
r.patel@n.io
IN
640.00
2025-04-14

Invalid emailMust include a domain.

03·Validate

Catch errors before they ship.

The same visual builder defines validations. Inline errors on the row, downloadable error reports, never a surprise at load time.

Inline errorsCustom rules

04·Enrich

Cross-reference any source.

Reference tables, live APIs, databases. Lookups slot into a mapping the same way logic does.

Local lookupsAPI callsDatabase joins

reference tables

country_lookup249 rows

code

country_name

US
United States

DE
Germany

JP
Japan

FR
France

match on code

Source

country_code

Destination

Country Name

Logic

lookup('country_lookup', code)

Plus templates, AI agents, integrations, and more…

See the full list →

FOR SECURITY FIRST TEAMS

Scale securely with confidence.

Our architecture is designed to minimize liability and risk. Client data is securely stored for 24 hours to facilitate large file operations, then permanently deleted.

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