The challenge: “We don’t know what’s hidden in decades of COBOL and Java code. Losing SMEs makes it even worse. A manual audit could take years and still miss business-critical rules.”
 

• Without Slingshot: The manual approach requires teams to comb through millions of lines of code and interview SMEs for months, which is expensive and incomplete.
• With Slingshot: AI agents ingest legacy code and use generative AI models trained on COBOL, Java, other legacy coding languages and mainframe patterns to reverse-engineer functionality. They produce specifications, flow diagrams and dependency graphs with up to 99 percent code-to-spec accuracy in days. SMEs review outputs to confirm nothing vital is missed.

The Slingshot difference: Unlike generic scanners, Slingshot connects outputs to business capabilities, turning hidden code into a usable modernization playbook.

The challenge: “Even when IT delivers a plan, it’s disconnected from business ROI. Funding stalls because I can’t prove value early enough.”
 

• Without Slingshot: In the manual model, architects often create plans in isolation that read like technical documents, disconnected from revenue or risk outcomes.
• With Slingshot: Slingshot closes this gap by cross-referencing the extracted rules and process flows with enterprise KPIs and compliance requirements. It then auto-populates Jira/ADO with backlog items, each tagged with business outcomes such as reduced processing time or cost savings.

The Slingshot difference: Where others stop at technical detail, Slingshot makes modernization fundable by proving the ROI of application modernization early.

The challenge: “We spend months and millions designing a target architecture. By the time it’s ready, our business priorities have already shifted.”
 

• Without Slingshot: Manually mapping APIs, data models, and integrations slows progress and leaves little flexibility. Risks of misaligned design are high.
  
• With Slingshot: The outputs from discovery feed directly into design. The platform proposes microservice boundaries, API specifications and data migration schemas. Architects review and refine these machine-generated blueprints, ensuring designs are enterprise-compliant and business-ready.

The Slingshot difference: Unlike static competitor outputs, Slingshot produces living architectures that update when business rules or standards change.

The challenge: “Big-bang cutovers terrify me. If one thing breaks, the whole business is down. I need a safe, incremental path.”
 

• Without Slingshot: Traditional SI programs run for multi-year timelines with massive labor costs. Programs stall or are scrapped when priorities shift.
  
• With Slingshot: Automate safer pathways by decomposing monoliths into incremental slices. Each slice is tied to a migration playbook that includes automated regression packs, canary deployment scripts and rollback triggers. Predictive dashboards highlight which modules are most complex or defect-prone, allowing CIOs to allocate resources where risk is highest.

The Slingshot difference: Competitor platforms may help decompose code, but Slingshot gives leaders visibility into the probability of failure before it happens.

The challenge: “Manual rewrites are slow and error-prone. Skilled developers are wasted retyping COBOL logic into Java. We can’t afford years of code churn.”
 

• Without Slingshot: Your roadmap involves vendor selection and gradual rollouts, but execution would take years.
  
• With Slingshot: AI coding agents convert specs into production-ready Spring Boot, .NET or Python services. Testing agents auto-generate unit and regression tests. Engineers validate and extend outputs, ensuring 60–70 percent of the work is done before coding even begins.

The Slingshot difference: Unlike coding copilots that output snippets, Slingshot ensures spec-to-code coverage across the full system.

The challenge: “Go-live is usually treated as the end. But I’ve seen too many modern systems become the next legacy because they lack monitoring and resilience.”
 

• Without Slingshot: Deployments often lack monitoring, predictive recovery or DevOps maturity, leaving enterprises vulnerable to new “legacy” debt.
  
• With Slingshot: AI deploy agents provision infrastructure as code, configure monitoring dashboards and establish self-healing workflows. Ops teams govern and tune these processes, ensuring continuous modernization without new technical debt.

The Slingshot difference: Unlike competitors who deliver code and exit, Slingshot creates a continuous modernization capability to ensure today’s modern systems don’t become tomorrow’s technical debt.