Session memory
Working context managed across long tasks and handoffs without token bloat.
Services
AI Memory Systems
Give AI systems durable, governed memory so conversations and workflows improve over time.
- 40%task-time reduction from persistent context
- 100%of memories governed and auditable
- GDPRaligned retention and erasure by design
Capabilities
AI that remembers responsibly
Short-term, long-term and semantic memory architectures for agents and copilots.
Long-term user memory
Preferences and history that persist across sessions — with consent and controls.
Semantic enterprise memory
Organizational knowledge accumulated from interactions, curated and governed.
Privacy and retention
Scoping, expiry, erasure and audit engineered into the memory layer.
How we deliver
Memory as architecture, not an afterthought
Stateless AI frustrates users and wastes context. Naive memory creates privacy debt. We engineer the layer between.
Design
What to remember, forget and never store
Memory taxonomies designed per use case: working state, user preferences, organizational facts — each with explicit scoping, retention and consent rules before a byte is stored.
- Memory schemas with owners and retention policies
- Consent and purpose limitation designed in
- PII classification before storage, not after
DocsCRMWiki
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Answer with citationsscoped to the user's permissions [1] [2]
Engineering
Recall that helps instead of haunts
Relevance-ranked retrieval into context, summarization that compresses without distorting, and conflict resolution when memories disagree — quality-evaluated like any retrieval system.
- Ranked recall tuned against eval sets
- Progressive summarization for long histories
- Staleness and contradiction handling
Ingest→
Chunk→
Embed→
Index→
Retrieve
PDFConfluenceSharePointSQLTicketsEmail
Grounded answer…with citations [1] [2] and permissions applied
Governance
Every memory accountable
Users and admins can inspect, correct and erase; retention expires on schedule; every read and write is audited — memory that survives a privacy review.
- User-visible memory with edit and erase
- Automatic expiry and right-to-be-forgotten flows
- Full audit of memory reads and writes
✓PII detected & redactedenforced
✓Answer grounded in sourcespassed
✓Role-based data accessenforced
✕Out-of-scope tool callblocked
SOC 2GDPRAudit logHITL
Use cases
Where memory changes the product
Personal assistants
Preferences and context that persist across sessions.
Long-running agents
Multi-day tasks with durable working state.
Support continuity
Customers never repeat themselves across contacts.
Sales intelligence
Relationship history accumulated and surfaced in context.
Team knowledge
Decisions and rationale captured as organizational memory.
Personalized learning
Learner progress shaping every interaction.
Delivery
How we engage
Flexible models — from strategy sprints to dedicated pods — with clear phases, quality gates, and ownership through production.
- Clear delivery process
- Senior engineering talent
- Security built in
- Long-term capability transfer
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1
Align
Goals, constraints, success metrics and stakeholders.
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2
Design
Architecture, delivery plan and risk controls.
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3
Deliver
Build, integrate, harden and launch.
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4
Operate
Monitor, optimize cost and continuously improve.
Our assistant stopped asking users the same three questions every session. Task completion rose 40% — and legal signed off because erasure and audit were built in, not promised.
Works with your stack
Memory infrastructure
Vector, graph and relational stores composed per memory type — in your cloud.
- Postgres
- pgvector
- Redis
- Pinecone
- Neo4j
- Anthropic Claude
- OpenAI
- Kafka
- AWS
- Azure
- Okta
- Datadog
FAQ
Common questions
Isn't memory just RAG over chat history?
That's the naive version, and it degrades fast — noisy recall, stale facts, privacy exposure. Real memory systems classify, summarize, expire and govern what's stored, and rank what's recalled. The difference shows up by week four.
How do you handle GDPR and privacy?
Purpose limitation, consent capture, automatic retention expiry, user-visible memory with erasure, and full audit — designed with your counsel from the schema up.
Does memory make model costs explode?
The opposite when engineered: summarization and ranked recall inject less, better context than replaying history. We measure tokens-per-task before and after.
Can memory be shared across our AI products?
Yes — a governed memory service with per-product scopes is exactly the architecture we recommend once you have two or more assistants.
Next step
Give your AI a responsible memory
One workshop maps your memory taxonomy — what to remember, what to forget, what to never store.