AlphaCode Products
Discover AlphaCode’s diverse products that turn ideas into reality.
Product Introduction
Orbit
Keep your data in-house while managing the entire LLM lifecycle. Orbit is a unified LLMOps platform built for air-gapped environments — from prompt authoring to evaluation, deployment, operations, and data processing, all in a single on-premise console.
Key Features
Running LLMs in production means building prompt management, quality evaluation, a gateway, monitoring, and data pipelines as separate tools. Orbit brings all of it into one on-premise console.
Prompt Management & Evaluation
Prompts are decoupled from code and managed as a first-class asset. Test changes instantly, publish by version, and roll back immediately if something breaks. Automated scoring and A/B testing let you confirm a change is actually better with numbers, not guesswork, before it goes live.
Unified Gateway
Call multiple LLMs through a single API. Swap models without touching client code. Automatic caching cuts redundant call costs, rate limiting blocks usage spikes, and automatic failover switches to a backup model to keep the service running.
Observability & Cost Management
Every LLM call is traced. See exactly which steps a request went through and where it slowed down, on a timeline. Call volume, latency, error rate, and cost appear on a real-time dashboard, with cost automatically aggregated by model, project, and user.
Data Layer & RAG
Turn operational data into a reusable asset. Build datasets from real production logs, refine and embed them through a visual pipeline, and load them into a vector index. This becomes the RAG infrastructure for internal chatbots and search, with full data lineage tracked as a graph at every step.
Orbit
In environments where data can't leave the network, cloud-based LLMOps tools aren't an option — so companies end up stitching together five or six separate tools of their own. Orbit solves this. Deployed on-premise as a single unit, prompts, operational logs, evaluation data, and embeddings all stay in-house, with the only external touchpoint being the LLM inference call itself. Self-signup blocking, two-factor authentication, IP whitelisting, role-based access, and audit logs are built in by default, not optional add-ons. Inside Orbit, data flows in one direction: operational logs become evaluation cases, and refined datasets become vector indexes that improve search quality for internal services. This compounding structure — the more it's used, the more valuable it becomes — is what sets Orbit apart from a simple management tool.