Make your AI product remember what matters.
Neuralbase gives your app reliable long-term memory with one API. Stop stitching together vector plumbing and ship responses that feel personal, consistent, and useful.
< 20ms
P95 retrieval latency
99.9%
Uptime SLA
1-line
to start storing
10B+
vectors supported
- 01$ curl -X POST https://api.neuralbase.cloud/v1/memories
- 02-H "Authorization: Bearer nb_live_..."
- 03-d '{"userId":"u_173","content":"Prefers short answers"}'
- 04{ "ok": true, "id": "mem_3f2" }
- 01const result = await fetch(
- 02"/v1/memories?query=dashboard+preference"
- 03);
- 04// [{ id: "mem_3f2", score: 0.94 }, ...]
Semantic retrieval
What teams unlock with Neuralbase
Build AI experiences users actually come back to.
Neuralbase helps your product feel stateful and personal, even when models are stateless underneath.
Support agents that remember context
Keep customer preferences, prior incidents, and resolution history available across every new conversation.
Product copilots with continuity
Let your in-app assistant remember user behavior and feature preferences so answers get better over time.
Research assistants with long memory
Store findings, summaries, and references so researchers can resume work without losing earlier context.
Automations that adapt per user
Trigger workflows based on remembered user state, not just the current request payload.
Your first memory-powered flow in under 15 minutes.
Three steps to go from zero to a product that remembers every user, every session.
Create your workspace
Generate an API key from the console and set your project-level auth in minutes.
Send memories from your app
Ingest user events, preferences, and conversation snippets through one consistent endpoint.
Retrieve context on every response
Query by meaning and metadata to feed the right memory back into your prompt pipeline.
Already have an account? Go to dashboard →
Replace memory glue code with one reliable platform.
Neuralbase removes the fragile parts of memory infrastructure so your team can focus on product behavior and user outcomes.
End-to-end memory pipeline
Write memory once and let Neuralbase handle embedding, indexing, and retrieval orchestration — no plumbing required.
Relevance you can tune
Blend semantic similarity with metadata filters so responses stay grounded and personalized to each user.
Isolation by design
Hard tenant boundaries and key scopes so your team ships fast without data leakage risk between users.
Visibility built in
Track reads, writes, latency, and key activity from one dashboard. Every metric you need as usage scales.
Developer experience built for speed.
The API is straightforward enough for your first prototype and stable enough for production traffic.
Simple endpoints
Ingest and retrieve through clean REST routes that are easy to test and maintain.
Fast integration
Drop into existing Node, Python, and serverless backends without rewriting your app architecture.
Production auth
Use scoped keys for clients and privileged keys for server workloads.
- 01await fetch("https://api.neuralbase.cloud/v1/memories", {
- 02method: "POST",
- 03headers: { Authorization: "Bearer nb_live_..." },
- 04body: JSON.stringify({ userId, content, metadata })
- 05});
- 01import requests
- 02result
- 03= requests.get(
- 04"https://api.neuralbase.cloud/v1/memories",
- 05params={"query": "refund policy preference"}
- 06)
- 07# result["items"][0]["score"] -> 0.94
Keep control of your memory infrastructure.
Neuralbase works the way serious teams prefer: a public API layer in front, a private vector store behind it — nothing exposed that shouldn't be.
Public API layer
Expose only your API domain to clients. Internal services stay protected — no direct vector access from the outside.
Private vector runtime
Run your vector layer on the same infrastructure as your backend for lower latency and tighter data control.
Managed embedding layer
Use Neuralbase's managed embedding layer now. Keep the flexibility to tune retrieval settings as traffic scales.
Fits into the backend you already have.
You do not need a platform migration to add memory. Neuralbase drops into your current architecture and starts working immediately.
Web applications
Call memory retrieval from your app backend or server actions.
Backend services
Plug into existing APIs without restructuring your core service.
Worker pipelines
Power async tasks, agent workers, and model orchestration layers.
Event workflows
Persist memory from webhooks, queues, and scheduled jobs.
Analytics tooling
Track retrieval quality and memory impact over time.
Cloud runtimes
Run from serverless, containers, or your private VM environment.
Filter memory precisely
Use project, user, and metadata filters before ranking so results stay relevant and safe.
Separate environments cleanly
Keep staging and production keys isolated to avoid accidental cross-environment writes.
Start free, scale when usage proves out.
No heavy commitment up front. Validate memory impact first, then expand capacity with your product growth.
Free
For prototypes
Perfect for trying memory in a new AI feature.
Growth
Per project / month
For products with real users and growing traffic.
Scale
Enterprise
For high-volume workloads and dedicated engineering support.
What teams ask before going live.
Most teams get first results the same day. If you already have an API backend, integration is usually a few endpoint calls plus key setup.
No. Neuralbase handles the embedding + indexing pipeline so you can focus on what your product should remember, not on infrastructure wiring.
Yes. That is the common production setup. Keep vector services internal and expose only your API domain to clients.
Yes. Project boundaries and scoped keys help keep data isolated across environments and customer workloads.
No. Postgres still handles auth, accounts, and transactional data. Neuralbase is for long-term memory retrieval and context search.
Use the dashboard to track writes, reads, latency, active keys, and operational trends as your memory traffic scales.
Yes. The API is language-agnostic and works cleanly from JavaScript, Python, and any backend runtime that can make HTTP requests.
Yes. You can start on the free plan, validate user impact, and upgrade only when traffic and value justify it.
Your model is only as good as its memory.
Give your product the persistent context users can feel. Start free — your first memories are live in under 15 minutes.