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Your Company Brain in a live graph.

Memory Graph unifies your data into entities, relationships, and timelines, updated in real time and ready for any agent via MCP.

Your data volume grows.
Context stays fragmented.

Context Operational growth Data your company generates Context your agents can actually use Context Gap

T he volume of information your company produces grows on its own, without anyone deciding it should.

The context an agent can use does not grow with it. It depends on someone having connected the source, resolved that the customer in the CRM is the one in the help desk, rebuilt the order of events and kept all of it current. That work is manual, and it does not scale at the same speed.

The distance between the two curves is the Context Gap. It is where AI answers with the wrong record, cites the superseded document, or simply does not know. Memory Graph exists to close it.

Your ontology defines the model
and your data fills the graph.

From raw data to agent query in three automated steps, with no code required.

  1. Step 01

    You define the ontology

    You describe your business entities in YAML: Customer, Contract, Product, Ticket.

  2. Step 02

    Memory Worker builds the graph

    Reads the Data Catalog, resolves entities across systems, and writes to the graph in seconds.

  3. Step 03

    Agents query via MCP

    The graph becomes MCP Tools: search_entity, get_entity_context, run_cypher. Any agent uses it directly.

One question.
Your whole company context.

The agent queries the graph over MCP and answers with the right record, already resolved across sources, instead of walking each system.

Which enterprise accounts are at renewal risk this quarter?

Three accounts carry the risk. ACME is down 60% in usage, opened 5 billing tickets and paid its last two invoices late. It renews in 45 days. BetaCorp and Nordis repeat the pattern, at an earlier stage. All three sit with the same CSM.

Open a Slack thread with the CS team for ACME?

One platform for every team.

The same graph, queried by different teams.

Channel performance analysis

Campaigns, organic ranking and media spend in one context. Ask what changed this week without exporting five dashboards.

  • Search Console and ad platforms on one graph
  • Compare channels in natural language
  • Anomalies visible before the weekly report
Organic vs Paid Last 6 months Aug 2026
Signals for the period Organic Paid
  • Organic signups +23% MoM
  • "memory graph" · +140 positions
  • "data graph" · −12 after the core update
  • Paid CAC flat over the period

The complete customer journey

Before the agent opens the ticket, it already has full context: active contracts, payment history, previous tickets, NPS, CSM activity.

  • Contracts, tickets and payments on one record
  • Resolved before the rep opens the ticket
  • Respects the source system permissions
Maria Almeida ACME · Pro plan High priority
Customer context
  • 3 tickets in the last 60 days
  • $85K ARR · Enterprise
  • Renewal in 45 days
  • NPS dropped 9 to 6 in Q1

Real-time account intelligence

SDR asks the agent about a lead and receives who the decision-maker is, the current stack, and who bought at similar companies.

  • Decision-maker, stack and history in one query
  • Compared against similar accounts already won
  • Updated in seconds, no nightly batch
João Pereira BetaCorp · CTO Warm lead
Account context
  • 200 employees · Series B
  • Technical decision-maker identified
  • 4 similar accounts won in 6 months
  • No open opportunity today

One integration instead of ten

The team queries one graph over MCP instead of maintaining connectors for every system. The ontology is versioned in Git, and lineage says where each attribute came from.

  • One MCP surface instead of N integrations
  • Ontology versioned in Git, reviewed in a PR
  • Attribute-level lineage, from source to answer
Customer entity Ontology v4 · versioned in Git Traceable
Record lineage
  • plan · Salesforce · 4 minutes ago
  • nps · Survey · 12 Mar
  • tickets · Zendesk · 2 minutes ago
  • Every query logged and audited

Ready to give your enterprise AI
a working memory?

Book a 30-minute demo. We show Strattum running on data like yours, in the architecture your company can deploy.