Backboard Capsules: Minted AI Models, Deployed and Hosted on AWS

Backboard Capsules convert AI from a per-token dependency on outside vendors into an owned asset running on your own AWS GPU infrastructure. A Capsule fuses a leading open model with your company's context - documents, code, tickets, and history - into one endpoint that you own and run inside your AWS account.

HOW A CAPSULE IS MADE

1. Prepare. Your data is sorted and cleaned by Backboard's data engine.
2. Mint data into the model.
3. Compress. BBQuant compression makes the model up to 70%+ smaller with 98-101% quality recovery so large models fit on one GPU and decode up to 2.7x faster.
4. Deploy. Run it in your AWS account, inside your VPC, or on an air-gapped network. You call it like any model.
   WHY TEAMS CHOOSE CAPSULES OVER RAG AND CONVENTIONAL POST-TRAINING
5. RAG rents your context; a Capsule owns it. Retrieval stuffs documents into every prompt and still guesses. A Capsule pays for context once, at mint.
6. Built for work that can't guess. Regulated, high-stakes, and air-gapped teams need a model that abstains instead of inventing an answer.
7. Nothing leaves. Data, weights, and inference stay inside your environment. No calls to an outside API.
8. No token fees after build. Inference runs on compute you already pay for, including committed AWS spend.
9. New models are a tailwind, not a migration project. When a better open base model ships, your Capsule can be re-minted on it.
   PROOF POINTS
10. Up to 70% compression with 98-101% quality recovery versus full precision (example: 152 GB models compressed to 40-44 GB).
11. Up to 2.7x faster decode; 68-74% memory savings; one GPU does the work of two or three.
12. 99.95% on LoCoMo, the leading agent-memory benchmark, on a quantized 35B Capsule.
13. In one customer engagement on the same data set, a Capsule was built with roughly 21x less compute than a conventional post-train, on 1 GPU instead of 8, in about a week instead of a month (single engagement; illustrative of that use case, not a general benchmark).
    WHO IT'S FOR
    Enterprises and public sector organizations with sovereignty, IP, or security requirements that rule out sending data to outside model APIs - financial services, energy, mining, healthcare, defence-adjacent, and government - plus ISVs embedding an owned model into their own product, and any team running self-hosted models that want to increase speed and accuracy. DEPLOYMENT AND AWS INTEGRATION Capsules deploy as containers on customer-controlled AWS GPU infrastructure (Amazon ECS or Amazon EKS), keeping data, weights, and inference inside your account. Compression cuts the GPU instance count needed per workload, so you can afford to run more owned models on AWS. Enterprise engagements transact through AWS Marketplace, letting you apply committed AWS spend. Backboard is the full post-pre-training stack: memory, routing, action, compression, and Capsules. A Capsule can run standalone or alongside the Backboard AI Platform (benchmark-leading memory and agentic RAG) and Backboard R-CLI (secure enterprise AI coding).