AI Memory
Human-like memory and context for AI agents and robots
Sapiek transforms raw text into interconnected memory objects, building semantic graphs that let AI agents truly understand and recall context over time.
Through advanced embeddings and long-term memory models, Sapiek allows agents to reason contextually — not just react — enabling human-like continuity in decision-making.
Sapiek provides an intelligent memory layer for AI agents and physical robots, storing experiences, emotions, and decisions — just like a human memory system.
Pour all your company's documents, databases, and logs into Sapiek. It transforms 100 GB of raw data into around 1 GB of tagged, actionable memories — structured and ready for instant, secure retrieval.
Sapiek extracts episodes, facts, rules, and skills from emails, tickets, manuals, logs, or ROS bags. It normalizes entities (people, companies, zones) and generates concise capsules of up to 1024 characters with linked artifacts. Less noise, more signal — no redirections required.
Every memory is ranked by importance, recurrence, predicted usefulness, confidence, and risk/safety. Low-salience memories are compressed or reduced to lightweight markers — cutting cost and latency while keeping traceability.
For robotics, every memory is anchored in space and time — pose-graphs, costmaps, and temporal windows. Retrieve memories such as "safe routes," "congested zones (7–9 AM)," or "best grasp per material."
Memories decay exponentially with a practice effect: those frequently used are reinforced, rare ones are summarized and moved to cold storage or deleted per policy. Storage savings exceed 40% with no context loss.
Memory remains read-only for critical planners. Encrypted, auditable, and compliant with "right to forget." Deterministic retrieval under 50 ms p95 (edge) using a hybrid index (vector + inverted + graph) — no GC spikes or control-loop blocking.
Sapiek empowers agents, teams, and fleets to share useful knowledge securely. It's local-first with selective sync (Edge → Site Hub → Cloud), offering actionable context, lower latency, and safer AI collaboration.
Sapiek connects seamlessly with your existing tools — turning gigabytes of documents, conversations, and databases into a few megabytes of structured, tagged memories ready for AI agents and robots.
Connects directly to your document repositories to ingest PDFs, DOCX, spreadsheets, and manuals — normalizing metadata and converting entire folders into structured “knowledge capsules”.
Syncs your conversations, threads, and meeting summaries, identifying decisions, issues, and key insights. Captures what was agreed and why — forming actionable episodic memories.
Imports your internal knowledge bases and playbooks, extracting semantic facts, rules, and procedures. Sapiek links each topic with related teams, skills, and assets in its semantic graph.
Connects securely to Postgres, HubSpot, Salesforce, or HR platforms to extract structured facts about people, customers, and operations — turning raw tables into organized graph entities and insights.
LLM agents bloat their context, waste tokens, and lose continuity. Sapiek extracts only the top ideas, tags them as compact memories, and retrieves them deterministically—cutting cost, latency and hallucinations.
Conversations and long docs keep expanding, yet most tokens don’t help the next response. Sapiek condenses sessions into 2–3 top ideas per turn as capsules, avoiding context stuffing and keeping only what matters for the next step.
Most input is redundant or off-topic. Sapiek ranks memories by salience (importance, recurrence, predicted utility, confidence, safety risk) and compresses the rest, reducing hallucinations and improving reasoning fidelity.
Agents forget plans, decisions and prior actions. Sapiek stores episodic, semantic and procedural memories and reuses successful workflows, preserving intent and state across steps, days and tools.
Overfeeding context is expensive and unstable. Sapiek delivers deterministic retrieval (<50 ms edge) with hot→warm→cold consolidation—cutting tokens, compute and energy while keeping the most useful knowledge at hand.
Pre-launch insights collected during discovery — how Sapiek could havesaved months of work, reduced tokens, and improved reliability.
Staff ML Engineer (Fintech)
“My team spent three months stitching Drive, Slack and Notion to build a brittle memory layer. With Sapiek, we would have compressed sessions into 2–3 top ideas per turn and cut prompt tokens by 50–70% — fewer hallucinations, faster iteration.”
Platform Engineer (SaaS)
“We re-implemented retrieval three times to tame context bloat. With Sapiek’s deterministic hybrid index (<50 ms edge), we would have shipped weeks earlier and kept prompts stable across releases.”
Robotics Lead (Ops & Logistics)
“We lost days to route regressions after staffing changes. With Sapiek’s spatio-temporal memory and fleet sharing, we would have kept safe routes and zone policies consistent without touching control loops.”
Security & Compliance Engineer
“Legal blocked us from centralizing logs. With Sapiek’s local-first model, read-only planners, encryption, audit trails and right-to-forget, we would have onboarded months sooner.”
We turn gigabytes of raw data into a few megabytes of tagged, actionable memories—so your AI agents (and robots) act with context, not bloat.
Sapiek is an operational memory layer for agents and robots. It extracts episodes, facts, rules, and skills, scores theirsalience (importance, recurrence, predicted utility, confidence, safety risk), and stores them as compact capsules linked to asemantic graph and (for robots) a spatio-temporal index. A vector DB is one component; Sapiek adds structured memories, graph/context, consolidation, forgetting policies, and deterministic retrieval.
Through semantic deduplication, smart chunking (by headings/tables),entity normalization (people, companies, zones, skills), andconsolidation of low-salience content into short summaries or lightweight markers. We keep the 2–3 top ideas per turn or section as capsules (≤1024 chars), linking back to source artifacts. This yields typical 100–300× reduction while preserving what agents actually need to act.
Yes. By retrieving only high-salience capsules and brief summaries instead of full documents or bloated chat history, prompts shrink dramatically. This reduces token spend, speeds up responses, and improves reasoning fidelity—since the model sees cleaner, more relevant evidence rather than noisy context stuffing.