Memory that follows you
Your sources, decisions, and roles travel across tools and models, on-device or in the cloud, with the same memory contract.
Your AI forgets. We remember what matters.
Threadbaire is an LLM-agnostic memory layer that keeps your context alive across GPT, Claude, and whatever comes next. It remembers why you pivoted, which role you were in, and what changed since, so you don’t restart from scratch.
Pilot
— make outputs comparable across models
Works in your keys/tenant; zero retention in Pilot. Privacy.
🧭 Before a handoff: get the timeline.
🔀 During a pivot: see the decision & sources.
🔁 Swap models: compare on the same memory.
Why Threadbaire? Provenance-first memory that survives model swaps
The problem isn’t prompts, it’s context loss. We restore a single, provenance-first timeline you can trust.
Receipts by default
Every recall carries who/what/when/which model/source, so answers are auditable.
Model-swap safe
Change models as prices or policies shift; the story stays stable on the same history.
Threaded project memory
Pick up a month-old thread in seconds, see what changed and where you left off.
Strategic recall on demand
Answer “why did we change that?” with rationale, sources, and timestamp in one view.
Decisions with receipts
Compare outcomes across models on the same slice; handoffs take minutes, not hours.
Integrations vs Decision Layer — what you actually need
Some platforms focus on hosted connectors / a “memory API” (integrations). Threadbaire is a decision layer: we structure what/why/who/source and replay the same memory across models. Storage ≠ Strategy.
Integrations / Memory API
- Connect tools, sync content
- Objects/notes/files as output
- Optimized for coverage breadth
Threadbaire — Decision Layer
- Decision snapshot: { summary, 2 timeline, next }
- Receipts: why · who · source · date
- LLM-agnostic replay (same slice, comparable outputs)
Already using an OpenAI-compatible proxy/context extender? Threadbaire rides on top and adds decision receipts.
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Quick answers
What is Threadbaire?
A portable, LLM-agnostic memory layer that preserves project context and provenance so identical inputs yield comparable outputs across models.
Who is it for?
Founders, PMs, and research-heavy teams who switch tools/models and need decisions with receipts.
How do I try it?
Use the interactive demo or request the Pilot to make outputs comparable across models in ~2 weeks.
Do I need integrations to use Threadbaire?
No. Threadbaire works without connectors. Integrations are experimental and optional.
Does it work with my OpenAI-compatible proxy/context extender?
Yes. Threadbaire rides on top and adds decision receipts and strategic recall. In Pilot, it runs in your tenant and keeps zero retention.
How do I share memory across GPT & Claude?
Use LLM-agnostic replay: Threadbaire turns decisions into a portable memory slice (who/what/why/source). You paste the same slice into GPT and Claude, run the same prompt, and compare outputs — same inputs → same story. No extensions, no fine-tuning. Start small: capture one decision with receipts, validate it in Strict mode, then reuse that slice across models and tools without losing context.
How can I prove where an AI answer came from?
Every answer carries AI answer receipts: model & time, the exact prompt, structured inputs, and linked sources under clear role boundaries. Open the receipt to see lineage and re-run the same memory slice anywhere to verify results or spot divergence. Built-in compliance & retention lets you export, delete, or time-box receipts so audits aren’t a scramble.
RAG vs memory layer — what’s the difference?
RAG fetches facts for a single answer. A memory layer preserves decisions across answers and tools with receipts. Think: RAG = knowledge retrieval; Memory = decision context + who/why/source so results stay comparable across models. Use both: RAG for fresh docs; Threadbaire for replayable history that prevents context rot and enables multi-LLM comparability.