rubidium inference
we help organizations deploy, customize, and train open-weight large language models for domain-specific workloads where control, privacy, latency, and cost matter.
Model Strategy & Selection
Open-Weight Model Assessment
Evaluation of model families, licenses, context windows, deployment constraints, and benchmark fit for your use case.
Inference Roadmaps
Practical plans for moving from prototype prompts to reliable model-backed workflows, including architecture, data, and operations.
Benchmark Design
Task-specific evaluation sets and scoring methods that make model quality, latency, and cost tradeoffs visible.
Custom Deployment Architecture
Private Cloud & On-Prem Deployments
Deployment of open-weight LLMs in private cloud, VPC, or on-prem environments where control and data residency matter.
Inference Serving & Optimization
Serving stacks, batching, caching, quantization, and GPU sizing to improve throughput, latency, and cost efficiency.
Production Observability
Monitoring for model performance, request patterns, latency, error rates, and infrastructure health in production environments.
Domain Adaptation & Training
Supervised Fine-Tuning
Preparation and training of domain-specific instruction datasets to adapt open-weight models to specialized workflows.
Lightweight Model Customization
Efficient adaptation of open-weight models for targeted tasks without the cost and complexity of full-model retraining.
Training Data Pipelines
Data cleaning, labeling workflows, synthetic data generation, and evaluation splits for reliable model improvement cycles.
Retrieval & Tool Integration
Retrieval-Augmented Generation
RAG systems with document processing, embeddings, retrieval evaluation, and prompt orchestration for grounded responses.
Workflow & API Integration
Integration of models with internal systems, tools, and APIs so inference can support real operational processes.
Agentic Application Design
Design of constrained tool-using model workflows with explicit state, permissions, and handoff points.
Evaluation, Safety & Operations
Quality & Hallucination Testing
Evaluation harnesses that test factuality, refusal behavior, task completion, and regression risk before deployment.
Safety Guardrails
Policy design, red-team testing, output controls, and escalation workflows for sensitive model deployments.
Model Operations Support
Release processes, rollback plans, monitoring routines, and continuous evaluation for production model systems.