Move SQL Server and Oracle to PostgreSQL without rewriting your business logic by hand.

Proteus assesses, converts, tests and deploys your database code through a rules-first pipeline you can audit line by line. Same input, same output, every run.

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  • AWS Premier Tier Partner
  • Built natively on AWS Transform
  • Validated by the AWS APJ Modernization team
90% faster migration timelines
~60% lower migration cost
100% auditable conversion

Your database is the hardest part of the migration

Most modernization plans stall at the database. The schema is the easy bit. The real work sits in thousands of stored procedures, functions, triggers and inline queries that encode years of business rules, and every one of them has to behave the same way on PostgreSQL as it did on SQL Server or Oracle.

Meanwhile the licence bill keeps coming. SQL Server 2016 reached end of support in July 2026, so teams still on it are paying for software that no longer gets security fixes.

Done by hand?

That's months of developer-by-developer rewrites, each with its own quality.

Done with a generic generative AI tool?

It's fast but unpredictable: run the same procedure twice and you can get two different answers, and the problems show up in UAT.

Licensing drag

Oracle and SQL Server licences eat budget that could fund the move to AI-ready platforms.

Stored procedure risk

Business-critical logic buried in T-SQL and PL/SQL is where manual migrations break.

Inconsistent output

Manual rewrites and pure-LLM tools produce different results for the same code.

Cutover anxiety

Without automated testing, nobody can prove the new database behaves like the old one.

Rules first. AI only where judgement adds value.

Some migration tools send your source code to a language model as-is and hope for the best. Proteus doesn't.

It parses your source database into a structured model, then runs the bulk of the conversion through a migration rule engine. AI is brought in for the cases that need judgement, and anything complex is flagged for a human to review. That design choice is why the output is repeatable, why it can be audited, and why it costs less to run.

Repeatable builds

The same procedure converts the same way on every run. You can re-run the pipeline after a schema change and trust the diff.

Rules you can read

Conversion logic lives in an open, extensible migration-rules.json file. Your architects can inspect it, customize it and add rules for your own patterns.

Lower AI cost

Because the rule engine does the heavy lifting, token use stays low. Proteus tracks it at job, project and user level, so you see exactly what AI is costing you.

Naive vs Proteus migration comparison

One pipeline from assessment to a working PostgreSQL database

Every stage runs with quality gates and progress tracking, so you always know what’s converted, what’s tested and what needs a person.

Step
01

Assess before you convert

Proteus scores migration readiness before a single line is converted. It analyzes schemas, stored procedures, queries and dependencies and tells you:

  • which objects will convert automatically and which need manual review
  • compatibility gaps between the source database and PostgreSQL
  • size, dependency and complexity metrics, including linked servers and external tables
  • estimated effort, cost and team needed

You get a migration plan with effort and risk scoring, not a guess.

Step
02

Convert the schema

Source-to-target schema mapping runs automatically using AWS Schema Conversion Tool combined with Proteus conversion logic, with accurate data type mapping.

Step
03

Convert the code

T-SQL is converted to PL/pgSQL, covering stored procedures, functions and inline queries embedded in application code. Conversion is dependency-aware, so objects are processed in the right order and relationships stay intact.

Step
04

Test for functional equivalence

Proteus generates and runs unit tests to check that converted procedures and functions return the same results as the originals, that data integrity holds across tables and relationships, and that business rules and constraints survive. You get a test report for every object.

Step
05

Deploy and validate against a live target

Converted procedures are deployed to your PostgreSQL target and checked to confirm they execute correctly there, not just that they compile.

Step
06

Move the data and the pipelines

  • Data migration through AWS DMS, with schema mapping and data type conversion
  • ETL modernization: SQL Server-based ETL re-platformed to AWS Glue / PySpark, so you're not tied to the old vendor after cutover
  • GitHub integration: converted code committed straight to your target branch
Download the Proteus one-pager
Assess before you convert
Convert the schema
Convert the code
Test for functional equivalence
Deploy and validate against a live target
Move the data and the pipelines

Watch Proteus convert a real SQL Server workload

Built with AWS

Proteus was built over several months with AWS stakeholders across the APJ and worldwide modernization teams. It runs as a native skill on AWS Transform, so it slots into the migration tooling AWS already recommends, alongside AWS Schema Conversion Tool and AWS Database Migration Service.

Skill

Native AWS Transform skill, built on AWS Transform composability

Validation

Validated by the AWS APJ Modernization team, and demonstrated to AWS India and US modernization teams

Target Platform

Amazon Aurora PostgreSQL

Funding

Eligible migrations may qualify for AWS partner funding programs, including funded modernization assessments. Ask us what applies to you

What a Proteus migration delivers

90%

Faster migration timelines

~60%

Lower migration cost

95%

Less manual code review

100%

Auditable, repeatable conversion

Disclaimer : Indicative figures based on SourceFuse delivery experience. Actual results depend on project scope, complexity, environment and client requirements.

Built for everyone who owns the migration

Common starting points:

  • A SQL Server or Oracle licence renewal is coming up
  • You’re still running SQL Server 2016 or older
  • You’re modernizing .NET applications and the database is next
  • You want your data on PostgreSQL before building AI features on top of it

CIOs and finance leaders

A costed plan with effort, timeline and risk scoring before you commit budget. A way out of licence renewals you’d rather not sign.

Database architects

Complexity scoring, dependency resolution and quality gates, plus a rules file you can inspect and extend.

Migration engineers

Standard conversion patterns, automated unit tests and live-target validation, so review time goes to the objects that actually need a human.

Start with an assessment

We run Proteus against your environment first, so you see real output before you sign up for a full migration.

Book a migration assessment

Analyze

your SQL Server or Oracle environment and migration complexity

Plan

with effort and risk scoring for every object

Prove

it with a sample conversion and test results on your own code

Common Questions

  • SQL Server to PostgreSQL, including Amazon Aurora PostgreSQL. Oracle to Aurora PostgreSQL is also supported.
  • Proteus uses AI, but it doesn't hand your code to a model and accept whatever comes back. It parses the source into a structured model and runs most of the conversion through a rule engine. AI handles the cases that need judgement, and complex objects are flagged for human review. Every converted object is unit tested and validated against a live PostgreSQL target.
  • Yes. Rules live in an open migration-rules.json file that your team can read, customize and extend.
  • Proteus runs as a native AWS Transform skill. It uses AWS Schema Conversion Tool for schema mapping and AWS DMS for data movement, and adds its own conversion logic, testing and validation on top.
  • The rule engine does most of the work, so AI calls are limited to where they add value. Token use is tracked per job, project and user.
  • Proteus runs on AWS as a native AWS Transform skill, and your data moves from source to target through AWS Database Migration Service. Most of the conversion is handled by the Proteus rule engine, so AI is used only for the objects that need judgement, not your whole codebase.

    Before any conversion starts, we agree on the setup with your security team; where Proteus runs, which AI services are used, what access it needs, and how long anything is kept. You get this in writing as part of the assessment, and every converted object, test report, and log is delivered to you.
  • It depends on the number and complexity of objects, which is exactly what the assessment tells you. Most teams see timelines around 90% shorter than a manual migration.
  • Yes. ArcDBMigrate was renamed Proteus in 2026 as part of SourceFuse's move to named AI agents, alongside NYX (AIOps) and ARGUS (QA).

See what Proteus does with your database

Send us a sample and we'll show you the conversion, the tests and the migration plan.