This is the work behind the pitch: projects for smaller businesses, private AI builds, and engineering our founder has done in production environments. Here is what needed fixing and what we did about it.
We have worked with game developers, website owners and smaller software companies on the stuff that keeps products from running smoothly.
A developer needed a dependable backend environment without spending release week fixing servers.
We worked through hosting setup, deployment steps and backend issues, with clearer places to check when something went wrong.
The team had a more workable setup for releases and one less part of the stack to babysit.
A site was running into the sort of issues that appear after hosting changes, updates and configuration drift.
We tackled the hosting configuration, deployment path and server-side issues, then left the setup easier to maintain.
The owner could focus on the website instead of chasing down technical problems behind it.
Infrastructure chores and technical bugs kept getting pushed behind urgent product tasks.
We helped with deployments, fixes and supporting services, taking on work that did not justify another full-time hire.
The core team could stay focused on customers and product development.
Self-hosted models are not just a demo. We have worked on the hardware, model-serving software and application connections needed to make them usable.
The goal was to run capable AI without routing everyday prompts through an outside hosted model service.
Installed and ran self-hosted models, chose workable model sizes and configured the services needed to use them.
Private model inference was available from hardware under the operator’s control.
Powerful hardware alone is not enough; the model still needs drivers, serving software and a way to stay available.
Configured the GPU software stack and inference services, then made the model accessible on the local network.
The machine could serve model requests instead of just running one-off experiments.
A model sitting in a terminal was not very useful to applications that needed to call it.
Exposed a model-serving endpoint, configured client access and tested requests between the applications and the hosted model.
Existing software had a way to request AI responses from the private deployment.
This work was delivered by Jorge in full-time engineering roles, not billed through Layer Frame. It is part of the experience he brings to consultancy projects.
Basic changes had to be repeated by hand. The work was predictable, but it still consumed engineering time.
Built repeatable workflows for common operations, with a consistent path for running changes and less guesswork.
Routine jobs stopped depending on someone manually repeating the same checklist.
When one part of a system broke, the visible error rarely pointed to the actual cause.
Investigated failures across services, configuration and data flows, then worked through restoration and follow-up fixes.
Teams got service restored and a clearer understanding of what needed to change.
A team relied on costly platforms for recurring operational tasks and needed a better long-term approach.
Built and introduced simpler alternatives that covered the necessary workflows without the same vendor overhead.
The business could reduce recurring software costs and keep the operational capabilities it needed.
You do not need a perfectly written spec. Tell us what is broken, slow, expensive or missing. We will work out whether Layer Frame is the right team for it.