CONTEXT INFRASTRUCTURE FOR PRODUCTION AI

Context your AI features can defend.

Turn your product's unstructured history into citable evidence. AIxis runs parallel graph search, structural coverage gates, and zero-hallucination answers — all through one API.

11.6sAvg Fast Query Latency
100%Citation Recall Rate
0Hallucinated Leaks
41Governed Pools
AIXIS MEMORY PLAYGROUND
User / AI Assistant Inquiry

“Which prompt gave the best talking-head video in Seedance 2.5?”

Try asking:
Relevant Memories Located (3)zero manual tagging
Slack · #creative-renders2 hours ago

QA Review: Seedance 2.5 Beta

Rendered batch 4. The "cinematic close-up, natural lighting" prompt looks flawless. Jaw-jitter is finally gone!

Prompt Discovered
GitHub · PR #8145 hours ago

Update Default Parameters

Enforcing lip-sync strictness to 0.9 for all Seedance 2.5 talking-head generation templates.

Parameter Locked
Scale · Quality EvalsYesterday

Human Evaluation: Job #902

Lip-sync accuracy rated at 98%. Facial micro-expressions perfectly match the uploaded audio waveform.

98% QA Score
AI Recalled Response (100% Citable)0% Hallucination
prompt "cinematic close-up, natural lighting" 1 lip-sync strictness of 0.9 2 produced a 98% lip-sync accuracy score 3
Why this matters for your product

Your AI agent remembers successful prompts, parameters, and evaluations so it never repeats past generation mistakes.

All 3 claims verified by source recordsResponse time: 118ms
1 LINE OF CODE TO SAVE A MEMORYTypeScript / Python API
// Save any event, chat, or decision
await aixis.remember({
  pool: 'customer_acme',
  content: 'Acme signed renewal after Frankfurt DPA addendum approved.'
});
118ms Avg Recall Time•100% Citation Accuracy•0 Manual Tagging
Enterprise Context Isolation: Enforced
Zero Setup Infrastructure

Plug and play memory for your entire stack.

Forget about maintaining complex RAG pipelines, chunking strategies, or vector databases. Connect your tools once and give your AI instant, flawless recall.

Syncs with where you work

Automatically ingests context from Slack, GitHub, Notion, Zendesk, and Stripe in real-time.

Slack
GitHub
Notion
Zendesk
Stripe

100% Proven Answers

Every response is backed by a direct link to the source material. Zero hallucination guarantee.

Source: GitHub PR #814

Tenant-Isolated

Cryptographically separated memory pools ensure customer data never cross-pollinates.

One line of code

Drop the AIxis SDK into your Next.js or Python app and start retrieving verified context instantly.

// Fetch memories for a specific user session
const context = await aixis.query({
pool: 'user_992',
intent: 'What are their active feature flags?'
});
Memory with lineage

Remember what changed.
And why it changed.

AIxis does not flatten a living product into a pile of documents. It preserves the sequence from intent to attempt, decision, and outcome—so later questions can recover the reasoning, not just the latest file.

Aurora · hero creative lineagelive memory
Intent

Hero cut should optimize for viewer hold.

The campaign brief establishes the outcome, format, and target audience before generation begins.

Relationshipdefines
recorded at source time

Your systems keep producing events. AIxis keeps the identity, time, relationships, and meaning needed to use them later.

One API across the lifecycle
The useful kind of confidence

An answer you can inspect,
not merely accept.

Your product receives the concise answer it needs. Your system receives the evidence, certainty, and gaps required to decide what happens next.

POST /v1/queryverified

Answer

The hero video achieved , beating the by .

Confidence0.94
Claims cited3 / 3
Data gapsNone
Evidence receiptclaim-level
certain

The hero video achieved 31.8% fifteen-second retention.

matched fieldhero_15s_retention_percent
value31.8
sourceperformance-results · forrel_rec_aurora_0013

Exact record ID included for source inspection.

Project-scoped

A project key can only reach its own governed pools.

Evidence-bound

Claims remain connected to accepted records.

Contract-shaped

Answers, confidence, claims, and gaps are structured.

Gap-aware

Missing context is returned instead of quietly invented.