One upload. Four versions. Available everywhere downstream.
Most teams start an AI engagement by uploading documents into a chat window and pasting in whatever the agent needs to know each time. That works for one operator on one task. It collapses when twenty agents across fifteen workflows all need the same corpus.
Altabric inverts the model. You upload your documents — up to 500MB per engagement, in any common format including PDF, DOCX, XLSX, PPTX, HTML, and plain text — and Altabric produces four versions of each one: the original file, a clean full-text extraction, a rich summary of around 500 words, and a short summary of around 150 words. All four are produced once, on upload, using Anthropic's Haiku — a cost-effective model well suited to summarisation. The cost is a few cents per document at most.
From that point on, every downstream agent has access to the version that fits the task — full text when it needs to design against the detail, rich summary when it needs grounding, short summary when it just needs to know what the document is about. Nothing is regenerated on the fly. Nothing drifts. The agent reviewing a Story three weeks from now reads the same summary the agent designing the Epic read last week.
Each document, four ways. Each agent picks the one that fits.
When you upload a document, Altabric keeps the original and produces three compressed versions alongside it. Downstream agents use the version that matches what their task actually needs.
Background process. Seconds per document. You're notified when each one is ready.
What this looks like in practice.
One upload. Many downstream uses.
You upload a forty-page solution architecture PDF on day one of an engagement. Altabric extracts the full text, produces a 500-word rich summary and a 150-word short summary, and makes all four versions available in the library.
An agent designing an Epic for the data migration workstream needs to understand the target architecture. It reads the rich summary — enough to ground the design without spending budget on the full forty pages.
Later, a different agent reviewing a Story for the cutover plan only needs to confirm the architecture uses a particular integration pattern. It reads the short summary — just enough to verify.
A third agent, drafting the data dictionary, needs every detail. It reads the full text. A fourth agent, writing the change communications, doesn't need the architecture at all. It sees nothing of it.
The library is rich, the context stays lean.
A demo runs against your documents, not a generic deck. We read every message. You will hear back within two working days.