Human-Like Memory
for AI Agents.

Most AI systems forget everything between sessions. They ask the same questions repeatedly, ignore past conversations, and treat returning customers like strangers. This prevents AI from building the trust and context that create business value.

Swisper Memory is a cognitive memory system that mirrors how humans actually remember: it knows your context, remembers your preferences, learns from every interaction, and builds a relationship that compounds value over time.

“In five years, everyone will have an AI companion that knows them deeply, what they see, hear, prefer, and feel.”

Mustafa Suleyman, CEO Microsoft AI (January 2026)

Six Memory Capabilities

Each capability maps to a concrete technical operation. No black-box magic — deterministic, auditable, controllable.

Targeted Recall

Retrieves facts by meaning, entity, or time range — scoped to what the agent actually needs. The right information surfaces at the right moment without consuming the entire context window.

Entity Disambiguation

When a user says “My meeting with Sarah,” Memory knows which Sarah from conversation history or retrieved from other systems. It resolves ambiguity silently when possible and asks naturally when not: “By the way, which Sarah?”

Fact Storage with Auto-Entity Handling

“Remember this” as a single operation. Extracts facts from conversation, creates person records when needed, links everything automatically.

Fact Conflict Detection

When new information contradicts stored facts e.g. a client changed their investment horizon, a contact moved companies, Memory flags the conflict rather than silently overwriting. Trust is maintained.

Forgetting + Reinforcement

Facts that are repeatedly relevant are reinforced. Facts that go unused gradually decay. Memory stays focused on what matters now. Important things stick; trivia fades.

Memory Retraction

“Forget that I like sushi” — soft-deletes facts on request. Auditable, reversible, GDPR-compliant. Users and enterprises retain full control over what the agent remembers.

Agent-Controlled, Not Pipeline-Forced

Traditional memory systems run as fixed pipelines with loading facts, resolving entities, and running disambiguation on every message, whether the agent needs memory or not. This wastes compute and creates irrelevant interruptions.

✕Without (Fixed Pipeline)

✓With Swisper Memory (Agent Tools)

11 nodes run on every message in fixed order

Agent calls only the memory operations it needs

Entity resolution runs before the agent knows the task

Agent resolves entities when and if it needs them

Disambiguation interrupts even for irrelevant ambiguities

Agent decides whether disambiguation matters for the current task

Same generic context loaded regardless of query

Targeted recall with query, entity, and time range scoping

~15 internal state fields for pipeline coordination

Clean tool calls with explicit inputs and outputs

Why This Matters for Your Business

Memory is what makes the 50th interaction exponentially more valuable than the first.

40%

McKinsey:

Companies excelling in AI personalization generate 40% more revenue from those activities than average performers.

25%

Bain & Company:

A 5% increase in customer retention can drive profit increases of 25%.

82%

Medallia:

82% of consumers say personalized experiences drive their brand choice in at least half of shopping situations.

For banking, this means a wealth advisor AI that remembers portfolio composition, risk preferences, family structures, and talking points — making every client interaction relationship-driven rather than transactional.

For consumers, this means a shopping assistant that knows a customer’s style, past purchases, sizing, and preferences — turning online shopping into a virtual luxury experience with personal consultation.

Trust Through Control

Memory only creates value when users trust how their data is handled. Swisper Memory gives enterprises and end-users full control:

Memory Retraction

Users can request “forget this” at any time. Soft-delete is auditable, reversible, and GDPR-compliant (Article 17).

Conflict Transparency

When facts contradict, the agent surfaces the conflict to the user rather than silently overwriting. No hidden decisions.

PGP-Encrypted Storage

Facts are encrypted at rest. Not metadata-level encryption — fact-level PGP encryption with European-sovereign key management.

Disambiguation Consent

When the system is unsure about an entity, it asks. No assumptions, no silent guesses — the user stays in the loop.

Under The Hood

For technical evaluators and integration teams:

Semantic Retrieval:

Retrieves by meaning, not keyword match. Vector-based similarity search over 2000-dimensional embeddings finds relevant facts without needing exact words.

Two-Tier Context Model:

Pre-loaded top facts for instant greeting context, plus on-demand targeted recall for complex tasks. No unnecessary compute on simple queries.

Blocking + Non-Blocking Disambiguation:

Critical ambiguities pause the conversation and ask the user. Non-critical ambiguities are handled as follow-ups (“By the way, which Thomas?”) without interrupting the flow.

Person Graph:

Full entity model with display names, aliases, roles, electronic addresses, and company affiliations. Entity resolution works across contacts, calendars, email, and conversation history.

Stateless Tool Design:

Each memory operation is independent — no hidden session state between calls. Fully traceable, fully auditable via Swisper Prism.