Context Is Everything
Turn every kind of data into one secure memory your AI apps and agents can use and act on — and build it into your product within minutes
// no spam, just a beta waitlist
Connect the places information already lives. Miriel turns it into one memory, then puts that memory to work.
- Documents & scansdrawings, spreadsheets, tables
- Email, chat & calendarsand the business apps behind them
- Databases & codecloud storage, repos, warehouses
- Meetings & callsunderstood live
- Cameras & sensorslidar, audio, MQTT — live
- Understandtext, tables, images, audio and video are read, transcribed and described as they arrive
- Remembersearchable content, the relationships between people, things and facts, and a timeline of events — queried together
- Protectencryption, permissions that follow every answer, and deployment in your own environment if needed
- Answerplain-language questions, with sources
- Actmessages, alerts, drafts and updates
- Automateworkflows on a schedule or on new data
- Buildcontext-aware apps, generated and deployed
Open the workspace, plug it into the AI tools your teams already use, or build it into your own software. The same memory sits behind all three.
The Miriel workspace
Chat sessions, a live calendar and inbox, the organization's memory and its apps — ready for a team on day one, by keyboard or voice.
Inside your AI tools
Miriel hosts MCP servers, so Claude, ChatGPT, GitHub Copilot, Cursor and your own agents can learn into and query the same memory as a tool.
# create and deploy your server POST /api/v2/mcp/default
Inside your software
One API and one credential to learn, query and build apps, so Miriel can sit behind products you already run.
# put data in, get answers out POST /api/v2/learn POST /api/v2/query
// one line connects your data; the second retrieves the context, with sources and entities
// runs in Miriel's cloud, your own cloud account, or fully local — with the model provider you choose
WHY MIRIEL
AI is only as useful as the data it can reach — and that data is large and never stops arriving
The two usual shortcuts break down at that scale.
Give an LLM tools to search it
The model reads through the data to find anything, so every question burns tokens scanning it — slower and costlier as the data grows.
Train a custom model on it
Out of date as soon as new data arrives. Every new source, product or procedure means retraining and redeploying.
Index it as it arrives
Data is understood and indexed on the way in. The model sees only the few facts that answer the question, and new data never needs retraining.
Documents are one kind of source. Cameras, microphones and sensors are another, and the interesting questions cross between them.
Point a camera at it and the frames become rows: open-vocabulary detection whose classes are text prompts rather than a fixed label set, embeddings that make a moment retrievable by a description of what was happening, transcripts, identity that starts anonymous, and depth perception measured in metres rather than pixels.
All of it lands on one timeline beside your documents, so “did what happened at booth 3 on Tuesday match the procedure that governs it” is a single query rather than a project.
Our answer to the most sensitive data you own is not a promise that we will not look at it.
Vectors and their metadata are encrypted before they enter the index, and nearest-neighbour search runs against that encrypted index rather than a decrypted copy of it — which turns “the operator could read your data and simply will not” from a promise into an architecture. Index builds are GPU-accelerated on NVIDIA cuVS.
Our fully homomorphic search work takes the same idea further — matching an encrypted query against an encrypted database without decrypting either side — and is published as a PETS 2025 artifact. That tier is research with working code, not yet what your production index runs on, and we would rather you knew which was which.
Depth cameras run their depth engines on an onboard NVIDIA Jetson, so a space is measured at the sensor rather than reconstructed afterwards. Recorded clips are embedded with NVIDIA Cosmos, making what a space recorded searchable by what happened rather than just what was in frame. The vector database builds its indexes on NVIDIA cuVS. The result is built to the same shape as the NVIDIA Metropolis VSS blueprint — we consume frame-level understanding and add the layer above it. Miriel is a member of the NVIDIA Inception program.
One memory, and the products built on it.
Workspace
Your organization’s memory, ready to use: sessions, inbox, calendar, memory and apps.
Autodev
AI that ships code to real dev/prod environments, debuggable, under human control.
NexusBuild
Reclaim your website — even if you’ve lost every login. Strictly read-only.
GET STARTED
Be first to build with Miriel.
A name and an email — that's it. No spam, just the beta waitlist.
// no spam, just a beta waitlist