From raw data to AI-ready. Automatically.

Your production data is not analytics-ready.
Every AI answer you trust depends on fixing that.

26% of enterprise data is already rated untrustworthy by its own owners. Transform closes the gap. Build a cleaning pipeline with 24 no-code operations or a native Code Editor — both in the same pipeline — see DQ scores update per column after every step, save it, schedule it. Every time new data lands, Transform runs. Every morning AskEdi answers from validated, re-scored, AI-ready data. No assumptions. No guesses.

26%

of enterprise data rated untrustworthy by its own owners

26% of enterprise data is already rated untrustworthy by its own owners. Most AI analytics tools assume your production data is clean and query it directly. It is not.

Transform closes that gap automatically. Build a cleaning pipeline, see DQ scores update per column after every step, save it, schedule it. Every time new data lands, Transform runs. AskEdi answers from validated, re-scored, AI-ready data every morning.

Seamless True-Hybrid Orchestration

24 Visual Operations.
One Native Code Editor.

24 no-code visual operations and a native Code Editor in the same pipeline. Data flows should never hit a no-code wall. When standard operations fall short, insert a Code Editor node at any position and hand your dataframe directly to custom logic, then back to the visual sequence.

Native Code Editor

The No-Code Escape Hatch

When standard operations are insufficient - proprietary logic, custom cryptography, or niche math - seamlessly insert a Code Editor node anywhere in the visual sequence.

  • Accepts custom Python (Polars-compatible)
  • Executes in isolated serverless container
  • Automatically passes mutated schema downward
Polars_Transform.py
# Operation 4: Custom Python Logic
def transform(df):
# Apply proprietary scoring model
df = df.with_columns(
(pl.col("revenue") * 1.4).alias("proj_rev")
)
return df
Visual Standard Operations Pipeline

Structural Engineering

FilterSplit ColumnsMerge ColumnsDrop DuplicatesFlattenPivot / Unpivot

Relational & Math

Joins (L/R/In/Out)ConcatGroup ByColumn AggSamplingWindow FunctionsConditional Column

Cleaning & Formatting

Manage NullsCast DatatypesDrop/RenameFind & ReplaceRound OffText Case ConvertBin / Discretize

Temporal Operations

Date Time AggDate Time DeltaManage Timezones

Ordering

Sort / Order By

Type-Aware Operation Gateways

Every column is implicitly typed on ingestion — DateTime, Scalar, String, or List. The builder actively blocks impossible configurations: no mean on a string, no regex on a number.

DateTimeScalarStringList

Pipeline DQ Intelligence

Quality Scores At Every Step —Build-Time And Run-Time.

Every pipeline run scores data quality before and after execution, so the dashboards your CFO sees reflect clean, validated data, not silently degraded rows. During construction, every operation surfaces a real-time DQ delta so engineers see quality impact at each step before writing to production.

Run-Level DQ Scoring

Live now

Pipeline DQ Report
16,600 row sample
Source (Pre-Transformation)
71%C+
manage_nullsfill → column mean on revenue, region
+14 pts
edit_dtypescreated_at → DateTime64
+8 pts
Result (Post-Transformation)
94%A
+23 point DQ improvement across this pipeline run

Column-Level DQ Delta

Live now

Column-Level DQ Delta
Live now
revenue
88%
99%
+11
region
63%
100%
+37
created_at
72%
95%
+23
product_id
91%
91%
±0

During pipeline construction, each operation surfaces a DQ delta per affected column — so engineers see quality impact in real time, before writing to production.

Eradicating Spaghetti Pipelines

Top-To-Bottom. Always.Never A Node-And-Noodle Canvas.

Most ETL tools force chaotic drag-and-drop canvases that only the builder understands. Transform deploys a strictly linear, form-based interface. Operations stack sequentially, making complex pipelines instantly legible to any stakeholder.

Transform

Readable by everyone

Linear Pipeline Builder
1
Filterrevenue > 10000
2
Group Byregion, product_id
3
Manage Nullsfill → column mean
4
Code Editorcustom scoring logic
5
Cast Datatypesscore → Float64
6
Sort / Order Byscore DESC

Legacy ETL Tools

Owner-only knowledge

Node-And-Noodle Canvas
Filter
Join
GroupBy
Nulls
Cast
Concat
ILLEGIBLE TO STAKEHOLDERS
Strictly sequential top-to-bottom
Form-based — no drag-and-drop
Legible to non-technical stakeholders

Zero-Trust Architecture

Your Data Never Persists.Not Even During Construction.

Building pipelines blind is dangerous. Transform materializes a cryptographically locked sandbox for real-time preview during construction - then destroys it. Execution is serverless and immediately terminated.

16,600-Row Sandbox

16,600 rows are extracted from your live source and materialized in isolated server-side memory for real-time operation previews - the same sample size used for DQ profiling.

  • AES encryption with domain-level salts
  • Immediate purge on pipeline save

Ephemeral Serverless

Transformation pipelines spin up transiently when triggered, apply logic in isolated memory, and are instantly destroyed upon completion.

  • Zero always-on standalone servers
  • Pay strictly for active milliseconds

Session-Exit Data Purge

The moment you leave a pipeline session or save your configuration, the encrypted 16,600-row preview payload is immediately and permanently deleted from the server. No transient data lingers beyond your active session.

You have seen the operations, the DQ feedback loop, and the security model. Every pipeline you build runs on schedule, scores your data at every step, and delivers AI-ready output before AskEdi sees a single row.

Polyglot Orchestration

Schedule, Execute,And Automate Translations.

Cross-database migrations shouldn't require manual type mapping. Schedule pipelines seamlessly while the engine automatically translates incompatible schemas on the fly.

Automated Schema Casting

Transforming data between structurally incompatible systems requires zero manual column mapping. The engine automatically detects destination architecture and transparently casts source datatypes.

Postgres textSnowflake VARCHAR
PostgreSQL
Snowflake

Single-Fire

A single, non-recurring execution. Ideal for one-off data normalization jobs, test runs, or ad-hoc migrations.

Daily Interval

A simplified daily trigger at a user-defined time. All schedules are displayed as plain English on the card.

Custom Cron

A full 5-part Cron expression builder for complex requirements automatically translated into human English.

Pipeline Lifecycle

Trigger on-demand outside scheduled windows. Pause scheduled pipelines without deletion. Re-enter the operation builder to edit - actively blocked during runs to prevent mid-execution corruption.

Auditable Execution Lineage

Granular execution audit trails filterable by time window. Success rates, average runtime, and total compute seconds tracked per pipeline. Failed runs expose expandable stack-trace post-mortems.

Success Rate98.2%
Avg Runtime4.3s

Pipeline Lifecycle

Full Control.With Auditable Execution Lineage.

Trigger, pause, edit, reset, and duplicate pipelines with structural guardrails at every step. Every run is logged with success rates, runtime, and expandable stack-trace post-mortems on failures.

Trigger On-Demand

Fire any pipeline immediately outside its scheduled window. Compute balance validated before authorization.

Pause & Resume

Suspend scheduled runs without deleting the pipeline. Operation sequence and schedule preserved.

Edit (Run-Locked)

Configuration changes are blocked while a run is active — preventing mid-execution race conditions.

Reset to Baseline

Discard all draft changes and reinstate the last saved configuration in one click.

Duplicate

Fork any pipeline into an independent draft copy with full operation sequence and schedule inherited.

View Operations

Read-only inspection mode renders the full transformation sequence without permitting modifications.

Success Rate

97.4%

Avg Runtime

3.7s

Total Runs

1,847

Compute Used

6,834s

RUN-1847successscheduled
3.2s
02:00 AM
RUN-1846successtriggered
4.1s
Yesterday
RUN-1845failed↗ expand stack-tracescheduled
1.8s
Yesterday
RUN-1844successscheduled
3.7s
2d ago

Zero Data Vendor Lock-In

Your Warehouse.Your Data. Always.

Transform is pure orchestration. Compute spins up ephemerally, executes your pipeline in isolated memory, writes the output directly into your destination database, and is immediately destroyed. Edilitics never holds your data.

Your Source DB

Postgres · Snowflake · BigQuery

Ephemeral Compute

Spins up → executes → destroyed

zero persistence

Your Destination DB

You own every byte written

Edilitics retains exactly one thing

The JSON configuration of your pipeline — the operation sequence, column mappings, and schedule. No customer records. No data copies. No lock-in.

No customer records stored
Bring Your Own Warehouse (BYOW)
JSON config only — no data copies
Ephemeral compute, zero idle servers

Governance & FinOps

Share, Alert,And Control Every Dollar.

From governed pipeline sharing to fair-share compute billing, every operational concern is addressed structurally - not with disclaimers.

Governed Pipeline Sharing

Shared users can view and trigger pipelines. Edit, delete, duplicate, and re-share are owner-only. Access is revocable at any time for total data control.

Event-Driven Alerting

Pipeline failures trigger email and in-app alerts with stack-trace post-mortems. Successful runs, pauses, and shares deliver in-app alerts adjusted to each recipient's own timezone.

Second-Accurate Compute Billing

Billed on actual measured runtime down to the second. Compute balance is validated before every run. If balance cannot cover the cost, execution is blocked with a clear error.

Active Integrity Walls

Configuration changes are blocked when target tables are engaged in an active flow run to prevent race-condition data collisions and ensure the integrity of your production data.

Zero Data Vendor Lock-In

Outputs are written directly into your BYOW database. Edilitics acts as purely ephemeral orchestration and retains only JSON pipeline configs for maximum security and zero lock-in.

Impenetrable Logic Guards

Every column is implicitly typed. The operation builder actively repels impossible configurations like stopping you from applying a mean calculation to a string or invalid data type.

AES-Encrypted Preview Sandbox
Bring Your Own Warehouse (BYOW)
Ephemeral Compute, Zero Persistence
Session-Exit Preview Data Purge

Governed Collaboration

Share WithoutLosing Control.

Share transformation pipelines with verified organizational peers. Shared users can audit logic, trigger runs, and fork a copy into their own workspace, but can never modify or delete the production configuration.

Capability
Owner
Shared
View operation sequence
Access Run History & Logs
Trigger on-demand execution
Duplicate to own workspace
Edit operation configuration
Delete pipeline
Share with additional users
Revoke access

Owner Capabilities

Full control — configure, schedule, share, revoke, and delete. Production logic is protected behind owner-only write gates.

Shared User Capabilities

Audit the full operation sequence, access run history, and trigger on-demand executions. Read-and-run access — no write, no delete, no duplicate.

Instant Revocation

Revoke any shared user's access at any time. All pipeline notifications and execution rights are immediately withdrawn.

COMMON QUESTIONS

Everything you need to know before you decide.

No sales call needed. If you have a question we haven't answered here, reach out directly.

THE NEXT LEVEL

Deployable pipelines without the engineering tax.

24 visual operations. Full Python when you need it. Ephemeral compute. DQ scoring before and after every run. One governed pipeline from raw data to AI-ready output.