From simple chatbots
to enterprise compliance

Persistent Chatbot Memory

Build a chatbot that actually remembers you

The Problem

Standard AI assistants forget everything the moment a session ends. Users must repeat their name, preferences, and context on every new conversation — creating a frustrating, impersonal experience.

Kyros Solution

Kyros persists user-specific episodic and semantic memories across sessions. Each new conversation retrieves the most contextually relevant facts via vector similarity, injecting only what matters into the prompt.

Business Outcome

A returning user is greeted by name, their past preferences are already known, and the assistant picks up exactly where it left off — with zero prompt engineering from the developer.

Customer Support Context Retention

Never make customers repeat themselves again

The Problem

Support agents (AI or human) lack full customer history. Every ticket is siloed. Customers grow frustrated repeating the same issues across 5 support interactions.

Kyros Solution

Kyros acts as a persistent CRM-layer for AI agents. Each support interaction is stored with timestamps. Future queries fetch the relevant complaint history, product version, and last resolution status.

Business Outcome

Support agents resolve tickets 40% faster with instant context. Customers feel heard. AI escalation reduces because the system already knows what was tried.

Multi-Agent Shared Memory

Let your AI agents work as a real team

The Problem

Autonomous agent pipelines (researcher → coder → QA) have no shared state. The researcher's findings are invisible to the coder. Agents duplicate work, lose context, and produce inconsistent results.

Kyros Solution

All agents in a pipeline write and read from a shared, tenant-isolated Kyros memory space. Merkle tree integrity verification ensures no agent receives tampered context. Each agent tags memories by role.

Business Outcome

The coder agent instantly knows what the researcher discovered. The QA agent already has the test criteria. Pipeline latency drops and output consistency improves dramatically.

Longitudinal Patient AI Companion

Medical AI that remembers the full patient journey

The Problem

Health AI assistants start cold every session. Doctors repeat patient history manually. Crucial patterns (medication reactions, visit triggers) are buried in unstructured notes.

Kyros Solution

Kyros stores structured semantic memories per patient: diagnosis history, medication logs, appointment notes. Vector similarity retrieves relevant past events when answering new queries.

Business Outcome

The health assistant surfaces relevant history automatically. Doctors review AI-surfaced context rather than hunting through EHR records. Patient safety improves through continuity.

Bitemporal Audit Trails for Compliance

What did the agent know, and when did it know it?

The Problem

Financial and legal AI systems must demonstrate auditability. Regulators ask: 'What information did the system act on at time T?' Traditional databases overwrite states — making reconstruction impossible.

Kyros Solution

Kyros stores bitemporal metadata on every memory: valid_time (when the fact was true) and transaction_time (when it was recorded). You can reconstruct exact agent state at any historical moment.

Business Outcome

Full regulatory-grade audit trail for autonomous agent decisions. Comply with MiFID II, SEC rules, or HIPAA audit requirements. Prove exactly what the agent knew and when.

Ebbinghaus Token Optimization

Stop wasting tokens on irrelevant memories

The Problem

Long-running AI workflows accumulate thousands of stale memory fragments. Injecting all of them into the prompt burns tokens, degrades LLM reasoning quality, and balloons costs.

Kyros Solution

Kyros applies the Ebbinghaus Forgetting Curve to decay memory relevance weights over time. Temporary events fade in hours; structural facts persist. Only high-density, relevant memories surface.

Business Outcome

Context window utilization drops by 60–80%. LLMs receive sharper, more focused prompts. Token costs fall linearly with decay pruning. Agent accuracy improves as noise disappears.

Personalized AI Shopping Assistant

Shopping that gets smarter with every purchase

The Problem

E-commerce recommendation engines use collaborative filtering — what similar users bought. They ignore the individual's evolving taste, constraints, and stated intent over time.

Kyros Solution

Kyros builds a per-user preference model from browsing signals, stated preferences, and purchase history. Each conversation retrieves this profile to generate hyper-personalized recommendations.

Business Outcome

Conversion rates increase as recommendations precisely match individual taste. Return rate drops. The AI assistant evolves with the customer — knowing their size, style, and budget automatically.

Adaptive AI Tutor with Learning Memory

A tutor that remembers exactly where each student struggles

The Problem

AI tutors reset every session. Students re-explain their weak areas. Tutors repeat content the student already mastered. There is no persistent model of each learner's knowledge graph.

Kyros Solution

Kyros builds a per-student semantic knowledge graph. Topics mastered are weighted high. Persistent confusion patterns are flagged. Each session starts with a precise model of where to focus.

Business Outcome

Students progress 30% faster with zero repetition of mastered content. The tutor surfaces exactly the right challenge level. Retention improves as the system tracks long-term forgetting patterns.