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Your team needs to understand the true scope, risk, and target architecture of a database migration before committing execution budget. Source schemas are undocumented, stored procedures hold tribal knowledge, and the right GCP service and cost are unknown. Without a grounded assessment, the migration estimate is a guess.

• Source database profile, query baselines, and dependency inventory
• Validated GCP target architecture with service-selection ADRs
• Schema compatibility assessment and initial DDL conversion plan

Traditional static analysis floods your team with findings and false positives, with no understanding of your architecture, dependencies, or deployment model — and you need a repeatable, human-validated process before any customer-facing security report goes out.

• Authorization-gated static analysis across authorized repositories (SAST, secrets, SCA/container, optional IaC)
• Anthropic Claude enrichment with P1-P4 prioritization, remediation, and architecture-aware correlation
• Institutional memory (local memory) that suppresses known false positives and compounds security knowledge across scans
• Expert-validated Technical and Executive Findings Reports delivered as PDFs

Discovery-stage Fiori prototypes are either hand-mimicked in CSS that drifts from SAP's design system the moment it evolves, or they force a full UX-discovery and design-system engagement before a single screen can be shown. Stakeholders wait too long to see something that looks like Fiori, and what they finally see does not behave like real Fiori components.

• Delivers true-to-life Fiori user flows without full-scale development costs
• Highlights missing business logic and technical gaps before writing production code, saving time and budget
• Hands off a clear, tested prototype alongside a gap analysis to engineering teams for faster, predictable delivery

Your team knows the digital twin use case and has executive buy-in, but you're stuck in the NVIDIA Omniverse template maze - you don't know which template fits your scenario, which of the 400+ extensions to enable, or how to generate boilerplate that actually compiles.

• Ranked Kit app template selection matched to your use case
• Resolved extension manifest and .kit config with dependency graph
• Scaffolded Omniverse Kit project structure
• Template-accurate boilerplate: USD stage setup, entry points, build scripts

Your team produced a vibe-coded prototype in an AI-first IDE — Cursor, Lovable, v0, Bolt, Replit, Windsurf, or Claude Code. The demo earned executive buy-in and leadership wants to launch. The prototype works in the IDE but cannot ship: no architecture, no auth model, no security posture, no tests, no observability, no data contracts, scaffolding mixed with real product logic, packages chosen by the AI rather than by an architect. Throwing the prototype away discards the validation it earned. Shipping it as-is is reckless. Rebuilding from a blank PRD is slow and ignores everything the prototype proved.

• Complete intent, FRD, and Mermaid user-flow record extracted from the running prototype
• Inferred JSON Schemas for every observed data shape
• Tokens Studio export of the prototype's design system
• Behavior catalog of observed state transitions and edge cases

You have a product vision but turning it into reality takes too long. By the time you launch, the market has moved. Your team spends months in planning paralysis, afraid to commit without perfect information. Competitors with inferior ideas but faster execution are winning your customers.

• High-fidelity MVPs ready for user validation
• Parallel solution explorations with comparative analysis
• Complete product requirements and specifications
• Production-ready code foundations for scaling

Your Marketing Cloud Engagement instance is underused because every campaign requires coordination across data extension owners, journey designers, email creative teams, and execution operators. Velocity is gated by handoffs, not by ideas.

• Data extension schema design with subscriber relationships and update strategy
• Journey Builder flows with entry sources, decision splits, and waits
• Email creative — HTML, AMPscript, dynamic content blocks — aligned to brand
• Campaign execution plan with send windows, throttling, and deliverability checks

Your Oracle estate spans databases and applications nobody fully maps any more. Releases are drifting out of support, DBLinks and embedded cross-database calls are undocumented, and PL/SQL volume makes every migration option look risky. You cannot sequence a migration because you cannot prove which components can move together without stranding the ones left behind, and live access to the estate is often unavailable when the assessment must start.

• Database portfolio catalog (engine type, release, patch level, RAC / Data Guard) and application portfolio inventory (language, release), every record stamped with source and fidelity
• Out-of-support / EOL and patch-risk register per engine release
• Database- and application-level security and role-model baseline
• Unified interdependency graph: application-to-database links, database-to-database connections classified DBLink or embedded code, and integration endpoints (APIs, files, other mechanisms)

Your team needs to understand the true scope, risk, and target architecture of a database migration before committing execution budget. Source schemas are undocumented, stored procedures hold tribal knowledge, and the right GCP service and cost are unknown. Without a grounded assessment, the migration estimate is a guess.

• Source database profile, query baselines, and dependency inventory
• Validated GCP target architecture with service-selection ADRs
• Schema compatibility assessment and initial DDL conversion plan

Traditional static analysis floods your team with findings and false positives, with no understanding of your architecture, dependencies, or deployment model — and you need a repeatable, human-validated process before any customer-facing security report goes out.

• Authorization-gated static analysis across authorized repositories (SAST, secrets, SCA/container, optional IaC)
• Anthropic Claude enrichment with P1-P4 prioritization, remediation, and architecture-aware correlation
• Institutional memory (local memory) that suppresses known false positives and compounds security knowledge across scans
• Expert-validated Technical and Executive Findings Reports delivered as PDFs

Discovery-stage Fiori prototypes are either hand-mimicked in CSS that drifts from SAP's design system the moment it evolves, or they force a full UX-discovery and design-system engagement before a single screen can be shown. Stakeholders wait too long to see something that looks like Fiori, and what they finally see does not behave like real Fiori components.

• Delivers true-to-life Fiori user flows without full-scale development costs
• Highlights missing business logic and technical gaps before writing production code, saving time and budget
• Hands off a clear, tested prototype alongside a gap analysis to engineering teams for faster, predictable delivery

Your team knows the digital twin use case and has executive buy-in, but you're stuck in the NVIDIA Omniverse template maze - you don't know which template fits your scenario, which of the 400+ extensions to enable, or how to generate boilerplate that actually compiles.

• Ranked Kit app template selection matched to your use case
• Resolved extension manifest and .kit config with dependency graph
• Scaffolded Omniverse Kit project structure
• Template-accurate boilerplate: USD stage setup, entry points, build scripts

Your team produced a vibe-coded prototype in an AI-first IDE — Cursor, Lovable, v0, Bolt, Replit, Windsurf, or Claude Code. The demo earned executive buy-in and leadership wants to launch. The prototype works in the IDE but cannot ship: no architecture, no auth model, no security posture, no tests, no observability, no data contracts, scaffolding mixed with real product logic, packages chosen by the AI rather than by an architect. Throwing the prototype away discards the validation it earned. Shipping it as-is is reckless. Rebuilding from a blank PRD is slow and ignores everything the prototype proved.

• Complete intent, FRD, and Mermaid user-flow record extracted from the running prototype
• Inferred JSON Schemas for every observed data shape
• Tokens Studio export of the prototype's design system
• Behavior catalog of observed state transitions and edge cases

You have a product vision but turning it into reality takes too long. By the time you launch, the market has moved. Your team spends months in planning paralysis, afraid to commit without perfect information. Competitors with inferior ideas but faster execution are winning your customers.

• High-fidelity MVPs ready for user validation
• Parallel solution explorations with comparative analysis
• Complete product requirements and specifications
• Production-ready code foundations for scaling

Your Marketing Cloud Engagement instance is underused because every campaign requires coordination across data extension owners, journey designers, email creative teams, and execution operators. Velocity is gated by handoffs, not by ideas.

• Data extension schema design with subscriber relationships and update strategy
• Journey Builder flows with entry sources, decision splits, and waits
• Email creative — HTML, AMPscript, dynamic content blocks — aligned to brand
• Campaign execution plan with send windows, throttling, and deliverability checks

Your Oracle estate spans databases and applications nobody fully maps any more. Releases are drifting out of support, DBLinks and embedded cross-database calls are undocumented, and PL/SQL volume makes every migration option look risky. You cannot sequence a migration because you cannot prove which components can move together without stranding the ones left behind, and live access to the estate is often unavailable when the assessment must start.

• Database portfolio catalog (engine type, release, patch level, RAC / Data Guard) and application portfolio inventory (language, release), every record stamped with source and fidelity
• Out-of-support / EOL and patch-risk register per engine release
• Database- and application-level security and role-model baseline
• Unified interdependency graph: application-to-database links, database-to-database connections classified DBLink or embedded code, and integration endpoints (APIs, files, other mechanisms)