The infrastructure underneath

Alpheric Atlas Platform

Private AI Deployment

Running models inside your own environment where data sovereignty, regulation, or air-gapped operation prevents sending data to a third party.

LLM Integration

Connecting language models into existing applications and workflows, with the error handling, cost controls, and fallbacks production use requires.

RAG Development

Retrieval-augmented generation grounded in your own material, with authorization enforced at the retrieval layer so the system cannot surface documents a user could not otherwise open.

Vector Database Setup

Selecting, deploying, and tuning the vector store behind retrieval, including the chunking and indexing decisions that determine whether results are useful.

Model Orchestration

Routing each task to the model best suited to it rather than sending everything to one general model, which applies a single cost and latency profile to every request.

AI API Integration

Exposing AI capability through APIs your own systems can call, with authentication, rate limiting, and audit logging appropriate to enterprise use.

AI Infrastructure Setup

The compute, networking, and deployment foundations for running AI workloads reliably, sized to real usage rather than to a demo.

AI Security & Observability

Authentication per request, authorization at retrieval, output filtering, and immutable logging of who asked what, which model ran, and what resulted.

Model Management

Versioning, evaluation, and rollout of models across environments, so changing a model is a controlled operation rather than an incident.

Prompt Engineering Systems

Treating prompts as versioned, tested artifacts rather than strings pasted into code, so behaviour can be changed deliberately and regressions are visible.

AI App & CLI Integration

Bringing AI capability into the environments developers already work in, including the terminal and CI pipelines, where context is already present.