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Incept MigrateIQ™
Data Architecture & Integration
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Incept MigrateIQ™

Function: AI Migration Services

Two proven conversion paths, both with the same discipline: automated extraction, automated conversion, and an explicit report of what a human still needs to look at. Nothing is translated as a black box.

11 component mappings covered, 4 generated artifact types per path

  • MuleSoft Anypoint
  • dbt
  • Apache Airflow
  • Databricks
  • Informatica
11
component mappings
4
artifact types per path
2
migration paths
[Interactive demo]

Walk the pipeline yourself.

Sample data, in your browser. A faithful simulation of the real flow. Approve or reject at step four and watch the score change.

Incept DataIQ™

analytics.sales.customer_master

Sample data · not a client system

Profiling analytics.sales.customer_master

Eight dimensions across every column, five of them shown here. Nothing is written; this is a read.

Column
Type
Null %
Distinct
Detected pattern
Signal
customer_id
string
0.0%
4,812
^C[0-9]{6}$
clean
customer_name
string
0.2%
4,780
free text
clean
tax_id
string
12.2%
4,201
^[0-9]{2}-[0-9]{7}$
issue
country_code
string
0.0%
47
ISO 3166-1 alpha-2
review
email
string
8.1%
3,944
RFC 5322
issue
created_date
date
0.0%
1,204
ISO 8601
clean
status
string
0.0%
4
enum, 4 values
clean
credit_limit
decimal
31.7%
892
numeric
review

4,812 rows · 8 columns · 2 columns flagged · 2 for review

Business metadata, drafted

Table and column descriptions written from the profile and the column context. Every one is a draft until a steward accepts it.

Object
Drafted description
customer_master
Master record for every business customer the organization sells to. One row per customer, keyed on customer_id. Sourced from the order system and enriched with credit attributes.
customer_id
System-generated unique identifier for a customer. Format C followed by six digits. Primary key.
tax_id
Government-issued tax identification number, formatted NN-NNNNNNN. Required for any customer invoiced in the current fiscal year.
country_code
ISO 3166-1 alpha-2 country code for the customer’s registered address.
credit_limit
Approved credit ceiling in reporting currency. Null where no credit assessment has been completed.

1 table + 4 columns described · awaiting steward

Rules recommended, with the evidence

Seven rules proposed from the profile. Each carries the proof that produced it, so approval is a judgment call rather than a leap of faith.

customer_id, Must not be null

Completeness

SELECT COUNT(*) AS failed_records
FROM analytics.sales.customer_master
WHERE customer_id IS NULL;
Evidence

0.0% null across 4,812 rows. Already clean, worth enforcing as a hard constraint before it drifts.

customer_id, Must be unique

Uniqueness

SELECT customer_id, COUNT(*) AS occurrences
FROM analytics.sales.customer_master
GROUP BY customer_id
HAVING COUNT(*) > 1;
Evidence

4,812 distinct values across 4,812 rows. Uniqueness holds today; the rule locks it.

tax_id, Must match the tax ID format

Validity

SELECT COUNT(*) AS failed_records
FROM analytics.sales.customer_master
WHERE tax_id IS NOT NULL
AND tax_id NOT RLIKE '^[0-9]{2}-[0-9]{7}$';
Evidence

97.2% of non-null values match the pattern. 118 do not, mostly nine-digit strings missing the hyphen.

tax_id, Null rate must stay under 5%

Completeness

SELECT COUNT(*) AS failed_records
FROM analytics.sales.customer_master
WHERE tax_id IS NULL;
Evidence

12.2% null against a 5% domain threshold. 587 rows. This is the largest single rule failure in the table.

email, Must be a parseable email address

Validity

SELECT COUNT(*) AS failed_records
FROM analytics.sales.customer_master
WHERE email IS NOT NULL
AND email NOT RLIKE '^[^@\\s]+@[^@\\s]+\\.[^@\\s]+$';
Evidence

4,359 of 4,422 non-null values parse. 63 do not: trailing semicolons and two addresses in one field.

status, Must be one of the accepted values

Validity

SELECT COUNT(*) AS failed_records
FROM analytics.sales.customer_master
WHERE status NOT IN ('ACTIVE','INACTIVE','PENDING','BLOCKED');
Evidence

Exactly 4 distinct values observed, all within the expected set. Cardinality of 4 on 4,812 rows reads as a controlled vocabulary.

country_code, Must exist in the country reference set

Consistency

SELECT COUNT(*) AS failed_records
FROM analytics.sales.customer_master cm
LEFT JOIN ref.country c
ON cm.country_code = c.alpha_2
WHERE c.alpha_2 IS NULL;
Evidence

47 distinct codes. Two (XK, AN) are absent from ref.country, affecting 12 rows. Neither is a current ISO 3166-1 code.

7 rules · 4 quality dimensions

Steward approval

Nothing reaches production without this step. Uncheck anything you would not stand behind. In the real product, the SQL is editable too.

customer_id, Must not be null

Completeness

Evidence

0.0% null across 4,812 rows. Already clean, worth enforcing as a hard constraint before it drifts.

customer_id, Must be unique

Uniqueness

Evidence

4,812 distinct values across 4,812 rows. Uniqueness holds today; the rule locks it.

tax_id, Must match the tax ID format

Validity

Evidence

97.2% of non-null values match the pattern. 118 do not, mostly nine-digit strings missing the hyphen.

tax_id, Null rate must stay under 5%

Completeness

Evidence

12.2% null against a 5% domain threshold. 587 rows. This is the largest single rule failure in the table.

email, Must be a parseable email address

Validity

Evidence

4,359 of 4,422 non-null values parse. 63 do not: trailing semicolons and two addresses in one field.

status, Must be one of the accepted values

Validity

Evidence

Exactly 4 distinct values observed, all within the expected set. Cardinality of 4 on 4,812 rows reads as a controlled vocabulary.

country_code, Must exist in the country reference set

Consistency

Evidence

47 distinct codes. Two (XK, AN) are absent from ref.country, affecting 12 rows. Neither is a current ISO 3166-1 code.

7 of 7 approved, nothing will run

Execute approved rules

Approve at least one rule

Executed on schedule

Approved rules run against the table. Failures produce downloadable rejected records; scores register to the catalog.

97.7%

Overall score

93.9%

Completeness

100.0%

Uniqueness

98.7%

Validity

99.8%

Consistency

Rule
Dimension
Result
Failed rows
customer_id, Must not be null
Completeness
PASS
0
customer_id, Must be unique
Uniqueness
PASS
0
tax_id, Must match the tax ID format
Validity
FAIL
118
tax_id, Null rate must stay under 5%
Completeness
FAIL
587
email, Must be a parseable email address
Validity
FAIL
63
status, Must be one of the accepted values
Validity
PASS
0
country_code, Must exist in the country reference set
Consistency
FAIL
12

No rules approved, so nothing ran.

Back to approval

7 rules executed · 780 rejected rows · scores registered to the catalog

No rules executed · nothing registered to the catalog

[HOW IT WORKS]

The pipeline, step by step.

  1. Extract Mappings, tasks, chained jobs, processes and connections read out of the source platform.
  2. Convert Each component mapped to its target equivalent: flows, transformations, scheduled workflows, SQL models, workflow steps.
  3. Generate Build-ready output: project structure, dependencies, configuration, deployment descriptors.
  4. Validate and flag A coverage report names what converted cleanly and what needs manual review.

[CAPABILITIES]

What it does.

  • To MuleSoft. Data integration mappings become Mule 4 flows, with transformation logic rewritten for MuleSoft. Chained jobs become flow chains. Application integration processes become API flows with a generated API specification. Test cases become automated test suites.
  • To dbt + Airflow. Processes become Airflow workflows with dependencies and retries preserved. Assignment steps become modular dbt SQL models that reference each other. Decision steps become branches in the workflow.
  • Coverage reporting. Every path produces a mapping coverage report. Manual review items are flagged, never silently dropped.
  • Known considerations. Best fit for batch and scheduled processes. Event-driven and synchronous APIs may need a serving layer; steps that wait on a person or a timer need review.
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See it on your data.

Discovery is read-only, takes five days, and costs nothing. We assess your environment and tell you honestly whether this agent is worth your time.

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