Evidence tier Source Confirmed · 4 captures on record
What the publisher says
As described on AWS Marketplace.
Generative AI has tremendous potential for increasing coding efficiency. However, organizations often face significant challenges in its implementation - security, scalability, IP considerations and hallucinations EXL Code Harbor addresses these challenges using an extensive and reliable methodology.
The key steps of the process include:
Show the rest of the publisher’s description (16 more lines)
**Detailed Analysis:** An under the hood analysis to understand the size, scale & complexity of the legacy codebase. It helps EXL to perform accurate effort estimation for the migration.
**Explanation:** Detailed documentation of each code into its natural language with descriptions of each function, step and key concepts of the code.
**Data lineage:** A pictorial, logical, and json view showing the flow of the logic from one table or variable over to another along with table and variable transformation logic tracked for both backward and forward lineage.
**Data dictionary:** Metadata creation mapping raw, intermediate and final derived tables along with variable format, description, transformation logic, source table and source variable for each variable within each table.
**Chunking:** Code pre-processing by splitting the source code into logical chunks, simplifying it for better quality conversion.
**Conversion:** Migrating source code to any language of choice, and then recompositing the code to get the full logic back.
**Iterative Debugging:** Agentic iterative debugging to solve for breaks and errors within the converted target code.
**Optimization:** Code optimization across code readability, syntax & function and logical improvements to enhance code performance, run time and memory usage.
**Synthetic data generation:** Test data creation either through reference data leveraging GANs or through a data dictionary in the absence of reference data to test and validate the converted target code output with source code output.
**Testing:** Creating test –and plan scenarios and generating the scripts to sufficiently test the code.
**Output validation**: Comparison of source code generated output with target code generated output across the suite of data validation and quality metrics. This human-in-the-loop step ensures complete input-output equivalency. Depending on the client's preference, it can be offered by EXL as a service or performed in-house by the client teams themselves.
**Audit**: Validate business logic and conditions present in codes to any structured or unstructured data.
**Efficiency**: Eliminate redundant code and enable performance optimization.
**Documentation**: Create comprehensive documentation to explain the coding process.
The tool requires S3, CloudWatch, Secrets Manager & ECR. The inferencing requires Sagemaker, ECS speaking to the Finetuned Model, along with RDS in the AWS tech stack. Multi-geography availability is obtained through Amazon ALB.
[Tool Catalogue](https://www.exlservice.com/insights/solution-sheet/exl-code-harbor)
Highlights
Highlighted by the publisher on AWS Marketplace.
EXL Code Harbor is a Generative AI-powered service leveraging multi-agent conversion framework that accelerates the migration of legacy codebases to novel and open-source languages, as well as enhances data and code governance. It addresses the manual effort involved in writing and optimizing code to transform the process, resulting in accelerated delivery, reduced costs, and higher accuracy.
EXL Code Harbor is an end-to-end service for code diagnostic & analysis, code explanation, metadata creation and lineage, conversion, test data generation, iterative debugging & testing, along with data validation, code optimization and process documentation. The pipeline is model agnostic and it is possible to integrate the client’s choice of. We work with the client to deploy the tool in the client environment, run the MVP, and then proceed to code migration initiatives at enterprise scale.
We work with the client to deploy the tool in the client environment, run the MVP, and then proceed to enterprise scale code migration initiatives. Alternatively, we can run the tool on the behalf of the client, as a service, or sign up for end-to-end migration.
Agent build and provenance
See the full provenance
The layer-by-layer build, the evidence behind each claim, the risk basis and the cross-marketplace links are open to any account. Some rows are disclosed, some the source leaves Unknown; a free account shows you which.
Compliance
- FedRAMPConfirmedNot listed90%, registry-checkedNo FedRAMP Marketplace entry matched this vendor's domain, checked 2026-08-27registry recordas observed 2026-08-27
Confirmed means matched to a public authoritative registry. Claimed means the vendor or its listing states it, not yet cross-checked. A framework not shown was not found in any source we hold, which is not evidence against it. Not listed means a scoped registry check found no match for this vendor's domain: a No is a scoped registry check, not a compliance judgment. Confidence bands: 95% domain-verified, 90% registry-checked, 80% self-attested, 70% weak signal. Self-attested items marked “vendor's site” are gathered from the vendor's own website and are not verified by us.
Vendor
External enrichment
Sources
Publisher resources
1 linkLinked repositories
Unknown means this listing does not publish a repository. It is not a statement that the code is closed, and a linked repository is not a claim that the publisher wrote it: the registry computes that relationship privately and does not publish it.
Evidence risk is the share of the build you cannot see before you deploy, not a security rating. Sign in to see the layer-by-layer basis for this band.