Local generative AI pipelines.
Production systems for AI models, running on hardware you control. Built for brands, agencies, and product teams whose output has to repeat, scale, and pass review.
What is a local generative AI pipeline?
A local generative AI pipeline runs AI models on hardware you control instead of a hosted API. Inputs are controlled, settings are versioned, and every step from raw input to delivered asset repeats reliably. Assets under NDA never leave your network. Costs stay flat no matter how much you generate. And a system you validated last quarter still behaves the same this quarter.
The range is wider than most people expect. Generative image and video pipelines in ComfyUI. Self-hosted language models for drafting, extraction, classification, and internal assistants. Agents running on local hardware around the clock. Different models, one engineering standard: controlled inputs, versioned settings, repeatable output.
Local versus hosted APIs
| Aspect | Local / self-hosted | Hosted API |
|---|---|---|
| Confidential assets | Never leave your network | Sent to a third party per request |
| Cost model | Flat: hardware plus electricity | Metered per image or token |
| Reproducibility | Pinned models keep output stable | Models can change under you mid-project |
| Setup effort | Higher upfront: hardware and workflow engineering | Minutes to first API call |
| Peak scaling | Bounded by your hardware | Elastic by default |
Both columns have their place. The default here is local-first, because the production constraints that come up most are confidentiality, cost predictability, and reproducibility. That is where local wins all three.
How projects run
- Scope against throughput. Hardware and architecture follow your actual production volume, not benchmarks. Small teams often run fine on one good GPU.
- Engineer the system. Generation graphs become versioned, documented workflows with controlled inputs; LLM setups get pinned models and evaluated prompts. Custom nodes and trained LoRAs close the gap between base models and brand requirements.
- Build the interface. The people using the pipeline are designers and editors, not engineers. Interfaces are web apps that make the pipeline usable without reading its source.
- Hand it over. Documented workflows, team training, and tools your operators run themselves. A system that only works with outside help is not finished.
Capabilities
Local Generative AI Pipelines
A local pipeline runs AI models on hardware you control: your workstation, an on-premise server, or a rented dedicated GPU. Nothing is sent to a hosted API. For brand work that means assets under NDA never leave the building, there is no per-request meter running, and a system you validated last quarter still behaves the same this quarter.
Systems get built end to end: generative image and video pipelines in ComfyUI, self-hosted language models for drafting, extraction, classification, and internal assistants, and the Python automation layer that ties them into production. Inputs are controlled, settings are versioned, and every step from raw input to delivered result repeats reliably.
Where the cloud is genuinely the better tool, that gets said plainly. But the default setup keeps the work on your side of the network line.
Custom AI Solutions & Creative Tooling
Most AI tools are generic because they were built for everyone. Production teams need the opposite: a tool shaped around their briefs, their approval loops, and their asset formats.
Tools like these get designed and built end to end, from interface to model plumbing. Examples from real projects: a prompt enhancement engine that turns creative briefs into consistent production prompts, and segmentation interfaces that let non-engineers direct computer-vision models in the browser. Built with Next.js, TypeScript, and Python.
Everything ships documented well enough that a team can run it without outside help. Deliberately: tools you depend on should not depend on anyone else.
AI Visual Production & AI Film
Campaign imagery has requirements a one-off generation cannot meet: the product looks like the product in every shot, typography in frame is correct, and the edit holds together. Getting there is a pipeline problem before it is a prompting problem.
Visuals and film come out of controlled workflows: reference-locked generation, controlled inputs, and real post-work. The result is imagery that holds up under a brand name, delivered at campaign speed.
This work runs on the same infrastructure as every other build here, locally where the project demands it.
Agentic Systems & GEO
More and more buying decisions start as a question to ChatGPT, Claude, or Perplexity instead of a search box. Showing up in the answer requires content machines can parse and trust: clean structured data, agent-readable formats, and interfaces agents can query directly.
The setup covers llms.txt, JSON-LD, machine-readable feeds, and MCP servers that let assistants pull facts about a business straight from the source. Everything on offer here runs in production on this site first, so you can inspect exactly what you would be getting.
Questions
What is a local generative AI pipeline?
A local generative AI pipeline is a production workflow that runs generative models on hardware you control: your own workstation, an on-premise server, or dedicated rented GPUs, instead of a hosted API. Inputs are controlled, settings are versioned, and every step from raw input to delivered asset is repeatable. For brand and agency work this means confidential assets never leave your network, costs stay flat regardless of volume, and validated workflows keep producing consistent output over time.
What hardware does a local generative AI pipeline need?
Less than most people expect to start. A single workstation with a modern 24 GB GPU runs image pipelines for a small team; larger teams add a shared server or rented dedicated GPUs. Hardware gets scoped against actual throughput needs before anyone buys anything.
Is ComfyUI production-ready?
With engineering around it, yes. Out of the box ComfyUI is a node editor; production readiness comes from what surrounds it: versioned workflows, controlled inputs, automated quality checks, custom nodes where the stock ones fall short, and documentation your team can follow without outside help.
Do you also build local LLM systems?
Yes. Self-hosted language models cover internal assistants, document extraction, classification, and agent workloads. They run on the same principle as everything else: open tooling serving models from your own hardware, connected to your data without it leaving the premises.
Can you train custom models or LoRAs for our brand?
Yes. Where base models drift from brand requirements, trained LoRAs close the gap: product consistency, style locks, character consistency across shots. Training happens locally too, so reference material stays in-house.
When is a cloud API the better choice?
When you need frontier capability that only exists as a hosted model, when volume is too low to justify hardware, or when burst demand exceeds what local infrastructure can absorb. Local infrastructure is the default here, with honest callouts where hosted models earn their place.
What does an engagement look like?
A short scoping phase to map your production requirements and existing tooling, then a build phase with working pipelines early instead of a big reveal at the end, then handover: documented workflows, training for your team, and tooling they can operate themselves.
Do you work with clients outside Berlin?
Yes, worldwide. Most work runs remote; on-site visits happen wherever the project is, with travel costs covered by the client. Pipelines are handed over as systems your team runs, so distance matters less after handover anyway.