Technical challenges,
solved the smart way.
A look behind the scenes: real problems, well-thought-out solutions and measurable results from real projects.
Product videos at the push of a button, generated by AI straight from the product.
In software marketing, compelling product videos make the difference. But they're expensive, slow and quickly outdated. This solution changes that fundamentally: an AI generates full-fledged product videos fully automatically from the real product, on-brand and multilingual. What was once costly one-off production becomes a reusable, reproducible production capability.
Product videos are a perennial topic in SaaS marketing: costly to produce, expensive to commission and outdated with every product version. Generic AI video generators deliver material quickly but never show the real product, making them unfit for serious product marketing.
A purpose-built engine enables an AI to generate every video fully automatically from the real product, with the actual interfaces and workflows of the application instead of stock footage or recreations. The videos are produced reproducibly on command, on-brand and multilingual.
The result is a scalable production capability: new videos and languages are created on command, always reflecting the current state of the product. The product videos on this page were created exactly this way: fully automatically, without external production and without manual editing.
SaaS platform made LLM-ready via Model Context Protocol
An established SaaS platform wanted to unlock its next strategic lever: making LLM clients an equal way of working alongside the existing admin UI. AI assistants should be able to maintain master data with full functionality, from CRUD operations to complex sync processes across multiple domains. The condition: the same validations, the same permission model and the same audit trail as in the manual backend. A parallel AI stack with its own logic was out of the question.
A dedicated Model Context Protocol server, built directly on the existing Eloquent domain. OAuth authorize flow for external LLM clients, Sanctum tokens for internal integrations. Activity logging with relation snapshots and rollback strategies per operation. Three domain-separated servers for content, catalogs and translations.
Over 19 production LLM operations across three MCP servers. Every write action is logged, every change is reversible. AI assistants work against the same validation and permission stack as the human admin UI: no parallel world, no drift.
Custom module CMS directly in the application stack
A standalone content system always brings a second stack: separate auth, separate hosting, separate deployment pipeline. Here the CMS was to live where the business logic lives: in the same codebase, in the same pipelines, versioned with the code instead of in a foreign database.
A custom module CMS, declarative in PHP: each module is a class whose field definition feeds the admin edit UI, backend validation, JSON persistence schema, public rendering via Blade and an LLM tool schema all at once. Position updates lock-secured, content multilingual, every mutation versioned.
12 production module types in use, each a single PHP class. New modules slot in without UI or API changes: admin, rendering and LLM integration come from the same source. No second stack, no schema drift.
Cross-channel impact analysis with an execution layer
A performance marketing initiative needed more than yet another reporting dashboard: a platform that structurally examines data from ad platforms, search consoles and web analytics for causal relationships and pushes measures back directly into the respective platform, without switching tools.
A connector architecture following the provider pattern: each data source implements a uniform interface, new sources are added additively without touching the analysis logic. For execution in the ad platforms, an OAuth-based write path with explicit user approval per action and a continuous action log.
Reporting, impact analysis and execution in a single application instead of spread across four tools. Insights are derived from structural patterns, not from opaque AI assumptions. New data sources are added without refactoring the core logic.
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