Dedicated 32B
From US$2,500/month
For specialized workloads in a validated 32B-class envelope.
- Customer-specific endpoint with one dedicated model replica.
- Authenticated API, version pinning, rollback and usage visibility.
Tramline builds task-specific AI models and applied AI systems that trade generic breadth for repeatability, operational ownership and domain fit.
For repeated workflows, the winning model is often not the biggest one. It is the one trained, evaluated and deployed for the specific job.
Prompting a frontier model can work once and drift the next time. Repeated work needs measured behavior, not vibes.
Repeated work needs explicit model versions, access rules and operating boundaries that a team can inspect and control.
Real workflows have local language, standards, edge cases and review rules. The model should learn the domain, not just receive another prompt.
Collect real examples, clean them, preserve provenance and define what good output means.
Use blind comparisons, task metrics and regression checks before calling anything an improvement.
Fine-tune, distill or wrap the model until the behavior fits the repeated job.
Ship it as an API, app, CLI or internal service with monitoring, versioning and rollback.
Tramline provisions a customer-specific model endpoint after contract. You get a pinned model version, authenticated API, defined capacity envelope, deployment controls, rollback and a reproducible evaluation contract. This is a recurring product, not a block of consulting hours.
From US$2,500/month
For specialized workloads in a validated 32B-class envelope.
From US$6,500/month
For high-capability reasoning and coding workloads requiring a larger validated hardware envelope.
From US$10,500/month
For open-weight frontier workloads that validate within dedicated capacity.
Three-month minimum. Provisioned only after signed contract and prepaid initial term. Final model, capacity, region and support are validated per contract.
Request a dedicated modelTramline is built around working artifacts. Caramelo and MindApps show the range: a specialized model product and a portfolio of applied AI tools.
Caramelo is an open-source Brazilian AI assistant based on Gemma + LoRA, served through an OpenAI-compatible API, web app and CLI. It proves the Tramline thesis: a focused model can become useful and productized without frontier-scale infrastructure.
What it demonstrates
MindApps is a portfolio of open-source applied AI projects: legal drafting, data analysis, transcription, resume analysis, PDF conversion, image tooling and automation. It shows a second Tramline pattern: many narrow tools, each built around a concrete job.
Examples
Guilherme Favaron is CTO / VP of Technology & AI at GRI Institute and an independent applied AI builder. Tramline is his vehicle for turning repeated AI workflows into owned model assets and working products.
Start with the workflow, model envelope and evaluation contract that would make the endpoint useful in production.