Hyperspell
you are building a company context layer for internal agents and want synthesized files or docs that many systems can ingest in a controlled enterprise environment.
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compare ·Hyperspell vs. 3ngram
Hyperspell is aimed at building a company-wide context graph for teams and agents.
If you want your AI work carried forward across Claude, ChatGPT, Cursor, and Codex today, 3ngram is the closer fit.
at a glance ·7 capabilities
Hyperspell vs. 3ngram, side by side.
| capability | 3ngram | Hyperspell |
|---|---|---|
| what it is | shared memory and follow-through for AI work | company context graph for agents |
| primary audience | individuals first, teams later | companies and internal agent systems |
| delivery model | typed work memory over MCPsame layer across Claude, ChatGPT, Cursor, Codex | filesystem artifacts and synced docs |
| memory model | typed — decisions, commitments, blockers, preferences, patterns | synthesized company context files |
| follow-through | deadlines, stale detection, nudges, autonomous close-the-loop | context delivery only |
| where it works | inside the AI tools you already use | where the synced files are mounted or read |
| maintenance burden | remember once, reuse everywhere | files and agent instructions can drift across surfaces |
3ngram
you want your own AI stack to carry decisions, commitments, blockers, and source context across chat and coding tools without maintaining shared prompt files.
Hyperspell and 3ngram share the same broad thesis: retrieval is not enough.
The split is in the buyer and the delivery model. Hyperspell points up-market at the company graph. 3ngram starts with one person and makes their AI follow through everywhere they already work.