SLLAM + MEMS
AI that remembers your business.
Private AI infrastructure, not a prompt wrapper. MEMS captures decisions, workflows, documents, customers, and how your company actually works — then makes that memory available when agents need it.
MEMS flow
Not a prompt wrapper.
Input
docs, tickets, chats
Episode capture
who, what, why
Retrieval
semantic + graph
Agent context
task-ready memory
Writeback
new learning stored
Sample episode
{
"type": "decision",
"subject": "refund policy exception",
"entities": ["Acme Co", "support"],
"why": "preserve renewal relationship",
"retrieval_hint": "future billing disputes"
}episode
support decision
source
ticket + policy note
retrieval
semantic + graph
writeback
approved exception
27K+
episodic memories handled in SLLAM’s internal EMS workstream.
3-layer
memory backbone pattern: relational records, vector search, and graph context.
Operator-led
built from real agent operations, not a mockup wrapped around an API call.
The problem
Most AI starts from zero every time.
That is fine for toy prompts. It breaks down inside a business where context, decisions, customer history, and operational nuance matter.
Repeated context
Teams explain the same customers, policies, systems, and decisions every day.
Lost decisions
Reasoning disappears into chat threads, meetings, inboxes, and half-updated docs.
Scattered knowledge
Documents, tickets, notes, and workflow events sit outside the assistant’s working memory.
Unsafe generic AI
Sensitive business context should not be sprayed across opaque SaaS tools just to be useful.
The service
MEMS is the memory layer for private AI.
MEMS turns AI from a stateless chat box into an operational system that accumulates useful context over time.
Capture
Ingest conversations, documents, decisions, tickets, notes, and workflow events into durable memory.
Retrieve
Use semantic, relational, and graph retrieval to bring the right context into the current task.
Apply
Connect memory directly to agents, assistants, and business workflows through OpenClaw integrations.
Operate
Monitor freshness, failures, drift, and memory quality so the system keeps improving.
Connect
Connect your agents and memory sources without guessing what MEMS can access.
The Connect surface will make OpenClaw and agent integrations visible, testable, and permissioned before memory starts moving.
First supported path
OpenClaw → MEMS
OpenClaw gateway
First pathConnect an existing OpenClaw deployment as the first supported path for agent memory sync.
Agent systems
Designed nextRegister A2A-compatible agents or custom agent endpoints with explicit memory permissions.
Knowledge sources
Controlled ingestBring in transcripts, repositories, docs, and workflow records once scope and retention are approved.
Trust controls
Every connection shows its blast radius.
- Read scope: what MEMS can inspect
- Writeback scope: what MEMS can store or update
- Sync health: connected, syncing, degraded, or disconnected
No vague sync button. Users should know what is connected, what can be read, what can be written back, and when the last sync failed.
Operating model
We design it, deploy it, and run it.
01
Assess
Map workflows, data sources, privacy requirements, and the places memory creates real leverage.
02
Deploy
Build the MEMS stack on your infrastructure or SLLAM-managed infrastructure with the agents your team needs.
03
Operate
Run monitoring, tuning, memory hygiene, upgrades, incident response, and ongoing improvement.
Assessment output
The first engagement produces decisions, not theater.
The goal is not to admire AI architecture. It is to identify the first memory-backed workflow worth deploying.
Memory map
The workflows, tools, documents, and conversations where durable context would actually matter.
Deployment path
A practical recommendation for customer-hosted, SLLAM-managed, or hybrid infrastructure.
Risk model
What should be remembered, what should be excluded, and where human review belongs.
First agent use case
One concrete workflow to prove value before expanding the memory layer across the business.
Use cases
Where buyers recognize themselves.
MEMS is for recurring work where context compounds: customers, decisions, projects, research, operations, and support.
Executive assistant
Meeting prep and follow-ups depend on context scattered across inboxes, notes, and old decisions.
An assistant that knows the current projects, decision history, people, and next actions before the meeting starts.
Customer support
Support teams retype the same product, policy, and customer context across tickets all day.
Agents retrieve product details, prior cases, escalation paths, and customer-specific history in the moment.
Internal operations
Recurring reports and SOPs depend on tribal knowledge that never quite makes it into documentation.
Operational memory that remembers how the business actually runs and turns that context into repeatable work.
Research and analysis
Research agents rediscover the same facts every week because yesterday’s work never becomes durable context.
A research memory that compounds findings, sources, assumptions, and open questions over time.
Platform trust
Evidence beats AI theater.
SLLAM builds on OpenClaw orchestration, durable memory storage, vector and graph retrieval, observable pipelines, and deployment models that keep business context under control.
- Memory backbone
- Relational records, vector retrieval, graph context, and episode history.
- Operations
- Freshness checks, observability, review loops, incident response, and upgrades.
- Deployment
- Customer infrastructure or SLLAM-managed infrastructure, depending on risk and operating needs.
Proof surfaces
Built from live agent operations.
Agent workstreams
Memory-backed agents coordinate infrastructure, email, security review, and project governance.
Inspectable records
Episodes preserve the context behind decisions instead of hiding the system behind chat transcripts.
Human-operated
SLLAM runs the system like infrastructure: monitored, reviewed, corrected, and improved.
Next step
Start with a MEMS assessment.
We will map where durable AI memory can help your business, what data should stay private, and what a real deployment would take.