Solutions

Memory infrastructure for every team that uses AI.

Cortyxia is the memory layer for AI applications. It sits between your tools and any LLM provider, giving every model call access to the context it needs without rebuilding it from scratch.

For enterprises, that means a unified knowledge bank across Slack, Salesforce, Zendesk, Jira, and dozens of other tools. For developers, it means one ISO key that adds persistent memory to Claude Code, Codex, Cursor, and any other agentic coding tool.

Explore the solutions below or jump straight to the documentation, SDK guide, or pricing.

For Enterprises

AI memory built for scale

The infrastructure layer that connects your entire organization to AI without silos, setup, or maintenance.

One source of truth

Unified Memory

Connect Zendesk, Slack, Salesforce, Teams, Jira, and 40+ tools. Every ticket, conversation, and document becomes instantly available to any LLM through a single ISO key.

40+ enterprise integrations out of the box
Same memory across every tool and team
Sub-2-second semantic retrieval

Integration Hub

Connected sources

40 active

Cortyxia Memory Layer

Slack

12 channels synced

Active

Salesforce

3,241 records indexed

Active

Teams

8 teams connected

Syncing

Zendesk

1,104 tickets parsed

Active

Jira

45 projects linked

Active

HubSpot

892 contacts synced

Active

+34 more

40-60% cost reduction

Token & Context Optimization

Stop sending full conversation histories. Our BM25 + rerank pipeline retrieves only the memory nodes that matter, cutting token spend without losing accuracy.

Semantic retrieval instead of full history
Content-addressable, deduplicated storage
Automatic context window management

Context Optimization

Token usage comparison

40-60% savings

Fragmented context10,000 tokens

Cortyxia retrieval300 tokens

No context lossSub-50ms overhead

No black boxes

End-to-End Observability

Trace every prompt, response, and quality metric in one console. Auto-extracted guardrails, full pipeline telemetry, and cross-model benchmarking.

Auto-extracted guardrail monitoring
Eight-stage pipeline telemetry
Head-to-head model benchmarking

OSuite dashboard

Overview

Traces

Guardrails

Models

P99 Latency

412ms

last hour

Records

1,259+

stored

Quality

axes scored

Request volumeLast 12h

Knowledge health

Memory Gap

Surface the missing information your AI needs to answer well. See coverage by business function and catch unanswered questions before they become problems.

Coverage heat map by business function
Real-time unanswered query detection
Trend analysis to guide knowledge investment

Knowledge Coverage

By business function

6 areas

Coverage gaps

Vendor Risk34%

Compliance78%

Revenue Ops89%

For Developers

Memory that survives every tool

Drop an ISO key into your coding workflow. Your LLM finally remembers what it learned, across sessions and tools.

One key, every tool

CLI & IDE Integration

Drop an ISO key into Claude Code, Codex, Cursor, Continue, Cline, Roo Code, Kilo Code, or any VS Code extension that supports OpenAI-compatible endpoints. Point the base URL at Cortyxia, set your token, and the tool thinks it's talking to OpenAI or Anthropic.

Works with Claude Code, Codex, Cursor, Continue, Cline, Roo Code, and more
Each tool gets an isolated memory namespace via devNamespace — no cross-tool pollution
Multi-provider fallback: swap the ISO token to switch between OpenAI, Anthropic, Gemini, DeepSeek, and xAI

Cortyxia—api-server

}

⎇

Cortyxia Assistant·42 memory nodes

claude-sonnet-4

You

fix the auth token refresh race condition in auth.ts

auth.ts · lines 24-31

const mutex = new Mutex()

await mutex.acquire()

try {

const token = await refresh()

await cache.set('token', token)

} finally { mutex.release() }

3 memory nodes used

·Avoided previous failed strategy

Ask Cortyxia...

↑

Never repeat a failed strategy

Graph-Based Memory

Every fix, decision, and mistake is stored as a memory node with multi-tier graph mechanisms — raw, indexed, and entity-linked. File relationships are tracked automatically: auth.rs touches user.ts, and the graph knows. When you start a new session, the LLM sees what worked, what failed, and what was already tried — so it picks up where you left off, not from zero.

Multi-tier memory: raw → indexed → entity-linked with separate node architecture per tier
File relationships tracked via entity-linked graph memory
Failed approaches tagged and avoided in future sessions
Cross-session strategy tracking — yesterday's debugging session is remembered and built upon

Memory Timeline

Cross-session strategy log

Session 1442 nodes

workedClaude CLI·2 min ago

Fixed auth token refresh race condition

Mutex lock on refresh. Previous attempt without lock caused duplicate tokens.

avoidCursor·8 min ago

Avoided: naive retry on 429

Previously caused cascade failure. Switched to exponential backoff with jitter.

workedCodex·23 min ago

Cache invalidation strategy confirmed

Write-through cache with event-driven invalidation. No stale reads in production.

avoidClaude CLI·1 hr ago

Avoided: manual string parsing for JSON

Regex-based parser broke on nested objects. Used JSON.parse with schema validation.

insightVS Code·2 hr ago

Test coverage gap identified in router.ts

Edge case: empty path segments. Added parametrized test for /a//b pattern.

workedCursor·3 hr ago

DB connection pool sizing

Pool size = 2 × CPU cores. Previous default of 10 caused connection exhaustion.

40-60% fewer tokens

Token Efficiency

Most CLI agents start every session from zero — re-reading files, re-discovering context, re-trying failed approaches. Cortyxia's BM25 + cross-encoder reranking pipeline retrieves only the memory nodes that matter, so dev mode keys get the most relevant memories instead of a firehose. Sub-50ms routing overhead, zero prompt changes, and 40% token cost reduction — all at the proxy layer.

40% token cost reduction via intelligent context assembly
BM25 retrieval with cross-encoder reranking at the proxy layer
Sub-50ms routing overhead with zero prompt changes
Dev mode keys get isolated context windows for agentic coding workflows

Token Efficiency

7-day rolling average

40% token savings

Tokens per session

Without memory

With Cortyxia

50k35k20k5k

S1S2S3S4S5S6S7S8

Token reduction

40%

Context window

8 nodes

Retrieval

<50ms

Failed attempts

reduced

Start building your memory layer

Connect your tools in minutes. No infrastructure, no maintenance, just memory that works everywhere.

Read the Docs

Every solution runs on the same Cortyxia core: model-agnostic memory, sub-200ms retrieval, and full observability. Read the system overview to see how it works under the hood.