01
Save
After each reply, Engram captures the AI's "train of thought" as a tiny bookmark. The bookmark stays the same size whether the conversation is 5 minutes or 5 months old.
Introducing
AI models
that remember.
True stateful inference — model-native memory, not prompt replay. Save state, restore in ~2 ms, process only new tokens.
Validated on IBM, NVIDIA, and Alibaba models — 5 architectures, 4B to 120B parameters.
Side effects may include: 93.8% fewer tokens processed
The Problem
The Solution
Every turn, your
AI starts over
Send one message,
receive one message.
Right now — the AI re-reads everything you said, every single time
Turn 11×
Turn 22×
Turn 33×
Turn 44×
Turn 55×
By turn 50, your AI has re-read your first message 50 times. In production, this re-reading is 90%+ of what you're paying for.
With Engram — the AI remembers where it left off
Turn 11×
Turn 21×
Turn 31×
Turn 41×
Turn 51×
94% fewer tokens, every turn. Cost stays the same whether you've chatted for 5 minutes or 5 months.
Developer Recommended
Thecurefor
AgentAmnesia.
Real memory, built inside the AI itself. Your AI picks up where it left off — instantly.
Side effects may include:
- Conversations that actually continue
- Costs that don't grow with every turn
- AI agents that complete multi-step tasks without losing the thread
- Memory that works the same at 5 minutes or 5 months
Conversations that actually continue
TalktoyourdevelopertoseeifEngramisrightforyou.
For the record: we build AI infrastructure. The pharmaceutical framing — Rx Only, Prescriber / Patient, side effects — is creative metaphor, not a regulatory claim. Engram is an Infrastructure software product.
How it works
Memory inside the AI, not next to it
Most "AI memory" today is a workaround — a separate database at best, a single JSON file at worst, pretending to be part of the AI. Engram gives the AI real memory, inside the model itself. Three steps.
Not a database
Not a cache hack
Not retrained
02
Resume
When you come back, the AI picks up exactly where it left off. In 2 milliseconds. No rereading, no catching up.
03
New Only
From there, the AI only thinks about what's new — not the whole history, every time. Cost stays flat.
Same AI you already use. Working the way you always assumed it did.
Why it matters
Real memory, not workarounds
Most AI memory today is bolted on. Engram is built in.
Memory that stays the same size, forever
Your conversation from five minutes ago and your conversation from five months ago take the same amount of storage — a small, steady file. No compounding cost. No growing footprint.
The core benefit
Conversations that continue
Long-running AI agents pick up exactly where they left off. Instantly. Every time.
94% less compute, per message
AI conversations stop getting more expensive as they get longer. Costs stay flat.
Works with the newest models
IBM, NVIDIA, Alibaba, and Mistral have all shipped AI models with the right architecture. Engram works with all of them.
Under the hood
The four moves, in detail
A closer look at what happens every time you send a message to an AI running Engram.
01
Save the AI's place
After each reply, Engram captures a tiny bookmark of what the AI was thinking. Same size every time, no matter how long you've been talking.
02
Tiered memory, like a librarian
Recent conversations stay right at hand. Older ones move to slightly slower storage. The oldest sit quietly on disk — still there, still ready. Nothing is ever thrown away.
03
Pick up instantly
When you come back to a conversation — even months later — the AI remembers. In two milliseconds. Not two seconds. Not six. Two thousandths of one second.
04
Only think about what's new
The AI doesn't re-read the whole history. It looks at the new thing you said, and only that. Faster responses, lower costs, same quality.
Where it helps
Who this is for
Four kinds of teams get the most out of Engram. Built-in memory matters most when the AI can't phone home — or when it shouldn't.
Where it changes the game
Agent platforms
Build AI agents that actually remember across steps — without stitching together external databases and retrieval tricks to fake it. The memory is part of the AI.
Featured case
Robotics
Robots whose AI genuinely remembers — real memory built into the model, not a cloud lookup or sidecar database. The robot keeps thinking on its own.
Edge AI
High-end AI running locally on specialized edge hardware (Jetson-class, mini PCs), where sending data to the cloud isn't possible or isn't allowed.
On-prem / private
Your AI, your hardware, your data. No cloud service required for memory, state, or continuity.
Validated results
Tested on NVIDIA H200 · IBM Granite · NVIDIA Nemotron · Alibaba Qwen · Mistral — seven models, four vendors, six architectures.
Token reduction
0.0%
97 tokens per turn → 6, zero failures across 271 requests
Snapshot restore
~0 ms
vs 6.5 s full prefill at 128K context length
Restore speedup
0×
over full context reprocessing, constant snapshot size
Tested models
Click any row for Model Facts| Model | Vendor |
|---|---|
| Granite 4.0-H-Tiny (4B) | IBM |
| Granite 4.0-H-Small (32B) | IBM |
| Nemotron-Cascade-2 (30B) | NVIDIA |
| Nemotron-3-Super (120B FP8) | NVIDIA |
| Qwen3-Next (80B) | Alibaba |
| Qwen3-Coder-Next (80B) | Alibaba |
| Codestral Mamba (7B) | Mistral |
All snapshot sizes stay constant regardless of conversation length · zero memory leaks across tested load
Infrastructure
Built on SGLang. Running on 400,000+ GPUs.
Current status