Solutions

Memory infrastructure for enterprises and developers.

Cortyxia is the memory layer between your tools and any LLM. It stores what matters, retrieves only what the next call needs, and keeps that memory intact when you change models or products.

For enterprises, that means one knowledge bank across Slack, Salesforce, Zendesk, Jira, and dozens of other systems, with measured token savings as conversations get long. For developers, that means one Cortyxia API key that adds persistent memory to Claude Code, Codex, Cursor, and other agentic coding tools without rewriting prompts.

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

For Enterprises

AI memory built for the organization

One memory layer across tools and models. Cut prompt cost as sessions grow, keep quality measurable, and give platform teams visibility into what the AI actually used.

One elegant key

Unified Memory

You do not manage a web of LLM connections. Point every app at Cortyxia and use a Cortyxia API key that already carries your LLM credentials. One key replaces wiring OpenAI, Anthropic, or Gemini keys into each tool, and the same memory layer sits behind all of them.

Use a Cortyxia API key instead of pasting provider keys into every product
No per-tool connection sprawl: one endpoint, one memory layer
Slack, Salesforce, Zendesk, Teams, Jira, and 40+ sources feed the same bank
Swap models without rebuilding integrations or losing institutional context

Integration Hub

one_key · no connection sprawl

cortyxia key

Cortyxia Memory

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

80.8% fewer tokens

Token & Context Optimization

Stop shipping full conversation histories into every prompt. On a 50-question enterprise governance evaluation, Cortyxia cut prompt tokens by 80.8% versus full-context replay while holding answer quality, with the gap compounding to 10.2× by question 50.

80.8% fewer prompt tokens vs full-context (governance, Gemini 2.5 Flash)
10.2× fewer tokens by question 50 as sessions grow
Assembled context typically stays in a ~6–12K token budget

Context Optimization

governance · n=50

80.8% fewer

prompt_tokens · per question10.2× by q50

q10

q20

q30

q40

q50

full-context cortyxia~10K vs ~104K at q50

Token cut

80.8%

Quality

Held

Audit-ready AI ops

End-to-End Observability

Most enterprise AI stacks fail the first compliance question: what did the model see, what did it retrieve, and did it break a rule? Cortyxia gives platform, security, and risk teams one console for the full path: prompt in, memory retrieved, guardrails evaluated, response out. Auto-detect persona and hard limits in your prompts, alert when a reply violates them, compare models on the same memory so vendor bake-offs are fair, and hand auditors a trace instead of a black box.

Empty-retrieval and low-confidence signals so ops catches silent failures before users do

Observability

pipeline · traces · quality

live

Overview

Traces

Guardrails

Models

Guardrail hits

last 12h

Empty retrievals

need knowledge

Models scored

same memory

request_volume12h · center axis

Know what you do not know

Knowledge Health

Wrong answers in production rarely start as model bugs. They start as missing institutional knowledge: a policy never indexed, a vendor process only in someone's head, a function with thin coverage. Knowledge Health is the command view for that risk. See coverage by business function, cluster live queries to find hotspots and blind spots, and prioritize acquisition exactly where recent questions found no relevant memory. Leadership gets a clear map of knowledge debt; engineering gets a backlog of what to fix next before agents invent answers.

Unanswered queries listed as an acquisition backlog, not buried in logs
Cluster view of hotspots, stale nodes, and under-retrieved knowledge under load

Knowledge Health

coverage · by function

6 areas

priority_gaps · act now

Vendor Risk34% covered

empty retrievals rising · acquire knowledge

Compliance78% covered

Revenue Ops89% covered

For Developers

Memory that survives every tool

One key for your coding agents. Carry fixes, failures, and file context across sessions while keeping token spend flat as the work gets longer.

One key, every tool

CLI & IDE Integration

Drop a Cortyxia API key into Claude Code, Codex, Cursor, Continue, Cline, Roo Code, Kilo Code, or any OpenAI-compatible endpoint. Point the base URL at Cortyxia and your agent keeps memory across sessions without rewriting prompts.

Works with Claude Code, Codex, Cursor, Continue, Cline, Roo Code, and more
Isolated namespaces per tool so contexts do not bleed across agents
Swap providers with the same memory: OpenAI, Anthropic, Gemini, DeepSeek, xAI

Cortyxia · api-server

Cortyxia Assistant·42 memory nodes

claude-sonnet-5

You

Fix the auth token refresh race condition in auth.ts

auth.ts · lines 24-31diff

24const mutex = new Mutex()

25await mutex.acquire()

26try {

27const token = await refresh()

28await cache.set('token', token)

29} finally { mutex.release() }

3 memory nodes

avoided failed strategy

Ask Cortyxia...

↑

Never repeat a failed strategy

Graph-Based Memory

Every fix, decision, and dead end is stored as structured memory. File relationships and prior attempts travel with you into the next session, so the model picks up where you left off instead of rediscovering the same bugs.

Cross-session strategy tracking across CLI and IDE tools
Failed approaches tagged so they are not retried blindly
File and entity relationships kept with the work that produced them

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.

91.5% fewer tokens

Token Efficiency

Long coding sessions bloat fast when every tool call stays in the prompt. On a 20-turn emulated IDE session, Cortyxia cut prompt tokens by 91.5% with comparable code quality. On SWE-style tasks, it resolved 100% of cases versus 73.3% for full-context while using 70% fewer tokens.

91.5% token reduction on a 20-turn IDE session (Gemini 3.1 Flash-Lite)
100% vs 73.3% resolution on SWE-style tasks, with 70% fewer tokens
Retrieval temperature to trade focused vs deeper context without prompt rewrites

Token Efficiency

ide · 20-turn session

91.5% fewer

tokens · per turn

without cortyxia

highlow

IDE cut

91.5%

SWE resolve

100%

SWE tokens

−70%

Budget

~6–12K

Start building your memory layer

Connect your tools once. Give every model the same institutional memory, with token spend that stays flat as sessions grow.

Read the Docs

Every solution runs on the same Cortyxia core: model-agnostic memory, research-backed context assembly, and full observability. Read the research or the system overview to see how it works.