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How BotMan AI Uses Knowledge Graphs for Legacy Modernization

BotMan AI says Codikel uses a knowledge graph to analyze legacy systems before redesigning their architecture and generating replacement code. Here’s what the company claims—and what to verify.
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BotMan AI says its Codikel platform analyzes a legacy application and builds a knowledge graph of its dependencies, business logic, and data models. The company describes using that graph to design a target architecture and generate code, rather than translating the old application line by line. This is a vendor account of the product, not an independently validated assessment.

What the knowledge graph is meant to do

In BotMan AI’s description, the graph is the analysis layer between an existing system and its proposed replacement. Codikel examines the whole system, maps relationships among components and business rules, then uses that model to inform a redesigned application. The company also says the graph supports Barbara, an agent that answers technical and functional questions from an IDE. These are product claims; the reviewed material does not independently demonstrate how complete the graph is or how accurately it represents a particular system.

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Graph-based analysis has a broader technical rationale: a system’s source code and other artifacts, including data schemas, can be represented as connected information to help analyze a legacy application. An arXiv paper discusses this approach, but it is general background and does not evaluate Codikel. Read the paper on arXiv.

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Which systems and target technologies BotMan AI names

BotMan AI’s English product page lists Oracle Forms, COBOL, Delphi, and Visual Basic 6 as source technologies. It gives Spring Boot, React, and .NET as examples of possible destinations, with the target standards defined with the client. These examples do not establish that every version, dependency, or integration is covered.

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For a proposal, confirm source-version support, database and integration dependencies, the desired target architecture, and explicit exclusions. Ask how the team will determine whether the graph captures the system’s important behavior, and how proposed changes will be tested against agreed acceptance criteria.

What the Forus case figures do—and do not—show

BotMan AI’s current English product page reports that its Forus project involved more than 500 forms and 70 batch processes, delivered in 14 months against a 16-year estimate. The company’s LinkedIn page describes the work as migrating 68 Delphi applications to Spring Boot and React. These are company-reported case details; the reviewed sources do not include independent audit documentation or enough information to compare the estimate and delivery on a like-for-like basis.

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BotMan AI’s white paper also claims 50–90% savings in time and cost on large migrations. The publication date was not confirmed in the reviewed material, and the claim is not an independently established average. Treat it as a vendor-reported general claim, not a forecast for a specific project. The product page describes the company’s team as having more than 20 specialists; that is also a company description.

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Security and data handling to verify

BotMan AI says project code runs on controlled infrastructure with access restricted to the assigned team, temporary copies are removed at project close, and on-premises variants can be arranged. Its white paper says production database data is not required for code migration and that testing uses test or synthetic data. These statements do not replace project-specific contractual and technical review.

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  • Confirm where source code, derived artifacts, and test data will be stored and processed.
  • Ask for retention and deletion timelines, including backups, and identify any subprocessors.
  • Document who can access project materials and whether on-premises deployment is available for the engagement.
  • Have your security team verify the proposed controls and contract terms before sharing sensitive material.

How to evaluate a modernization proposal

BotMan AI describes a path from assessment to proof of concept and then a full engagement. It also says client standards and delivery preferences shape the project. Use the early phases to require evidence tailored to your application, rather than relying on a platform description or headline case figures.

  1. Define scope: List applications, source versions, batch jobs, interfaces, databases, and operational constraints. Record what is explicitly out of scope.
  2. Inspect the graph: Ask what artifacts feed it, which relationships and business rules it captures, and how the team checks for missing or incorrect information.
  3. Agree on the target: Specify architecture, technology choices, coding standards, deployment requirements, and ownership of generated and modified code.
  4. Set proof-of-concept success criteria: Choose representative workflows and define measurable functional, integration, performance, and security checks before work begins.
  5. Require validation evidence: Agree on test coverage, defect handling, traceability from legacy behavior to replacement behavior, and acceptance criteria for delivery.
  6. Check relevant references: Request customer references for a comparable source stack, scale, and regulatory or operational environment.
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Availability and engagement model

Codikel is presented as an enterprise modernization platform and service, not a consumer product. BotMan AI says it is available directly or through certified technology partners. The reviewed material does not establish publisher referral terms or an affiliate program.

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