1. Upload an archive into a project
Important conversations are gathered in one place instead of scattered across old tools and tabs.
S.Y.S. (Save Your Spark)
S.Y.S. — private memory and AI over your archives
S.Y.S. brings AI chat archives into a single project, turns them into working memory, and helps you do more than store history. You can search it, recover context, and run AI on top of that memory with clear source grounding.
This is not just a chat archive. It is a private AI product for import, retrieval, and AI work over your project history — available as managed delivery now, with a self-hosted path as rollout evolves.
What you get first
- your archive enters a project and becomes working memory
- chats and messages become readable and useful again
- context comes back through retrieval and AI over history
First useful result
The point of S.Y.S. is not that import finishes. The point is that memory becomes usable and ready for AI work.
2. Open chats and messages
Imported history becomes readable and operational for daily work.
3. Recover the right context
Text and semantic retrieval bring back the relevant fragments without manual digging.
4. Add AI over memory
Once the corpus is assembled, S.Y.S. adds a routed AI layer over project memory on eligible surfaces.
What already exists
S.Y.S. already exists as a real product contour: no longer just an archive, just memory, or import-plus-search workspace.
Project corpus and import runtime
Archives pass through a multi-step import runtime and become part of a working project corpus.
Chats, messages, and retrieval
After import, users can revisit history and run both text and semantic retrieval across memory.
Evidence and source grounding
It is not only about getting an answer. S.Y.S. keeps answers anchored to memory and source context.
Exports, usage, and operator maturity
The product already includes exports, usage visibility, billing/entitlements, Telegram companion, and mature operator/admin surfaces.
AI over your memory
S.Y.S. already has an operational AI layer on top of project memory.
AI chat over project corpus
When history is assembled, S.Y.S. uses it as the base for further AI work.
Grounded answers
The product already includes a grounded-answer stack with source-aware behavior.
History, compare, reruns, artifacts
On eligible contours, the AI layer goes beyond one-off replies through grounded history, compare flows, reruns, and artifacts.
Real layer, no hype promises
We do not claim universal readiness. We do claim a real AI layer that is already materialized and expanding.
Import support keeps growing
S.Y.S. already supports multiple sources and keeps expanding. We do not market it as a universal importer of everything.
Already supported
ChatGPT, Claude, Telegram, WhatsApp, VK, Grok / xAI.
Already in runtime
Source auto-detection, ambiguous archive handling, resumable import, retries/recovery, provenance retention, and limited mixed-archive handling.
What is expanding
Source coverage, recognition quality, mixed/fallback ingestion, and overall import intelligence.
What we do not claim as ready
Unrestricted import of any archive, arbitrary document ingestion, or universal importer behavior.
Who it is for
Founders and operators
Keep decisions, hypotheses, and working routes in one usable memory layer.
Researchers, writers, and heavy AI users
Use old conversations as a valuable corpus instead of disposable logs.
Teams
Keep important discussions and AI context from getting lost between tasks and iterations.
Privacy- and control-sensitive users
Keep memory closer to the owner instead of fully inside an external platform.
Managed / self-hosted
Two launch paths: speed first or tighter control.
Managed pilot with support
The fastest way to see value: upload archive, assemble memory, and run retrieval plus AI without infrastructure overhead.
Self-hosted direction
The same core module in a more controlled contour. This is a real product path, with separate rollout and implementation requirements.
Next step
Start with your current archive. Validate context recovery, memory assembly, and AI over history, then scale through managed pilot or self-hosted rollout.
Product passport
Stage
Active product for memory, retrieval, and AI over archives.
What already exists
Import, project corpus, retrieval, AI layer, companion contours, and operator/admin maturity.
Launch format
Managed pilot now. Self-hosted path as a separate implementation track.
Typical start path
Archive → project → chats/messages → retrieval → AI layer → contour expansion.