Complete data integration.
Deep visualisation.
Mnemosyne Intelligence Suite unifies graph analysis, temporal investigation, geospatial mapping, and LLM-assisted reasoning in one integrated platform — built for team investigations, deployed on your own infrastructure.
Intelligence analysis needs more than link charts
A link chart shows who connects to whom. The questions that actually move an investigation — who controls the network, how it behaves over time, where its activity concentrates — need a typed model underneath the picture, not just the picture.
One actor, many records
The same person, account, or location arrives under a different key in every source — a phone number here, a name there, an IBAN somewhere else. Without a shared semantic model they stay separate rows, and the links that cross sources are never drawn.
Resolved in a shared ontology — one object per real-world entity, every source mapped onto it.
Influence is computed, not eyeballed
Who brokers between two groups, which node holds a cluster together, where the network splits if one actor is removed — these are structural facts of the whole graph. A link chart shows edges; it cannot rank centrality, detect communities, or expose the single points of failure.
Graph algorithms rank centrality, detect communities, and flag articulation points across the full network.
The signal is in time and place
When events happened, in what order, and where they originated often matters more than the connection itself. Treated as plain table columns, sequence and location can't be replayed, windowed, or correlated — so bursts, hand-offs, and co-location patterns stay buried.
Temporal playback and geospatial context make sequence and location first-class, queryable dimensions.
Data to intelligence in three steps
Import from any source. Analyse with algorithms and AI. Deliver structured intelligence products.
Import
Multi-source ETLCSV, JSON, REST APIs, STIX 2.1 bundles. Schema auto-detection. Three-stage entity resolution across sources.
Analyse
Graph + Temporal + GeoCentrality, communities, structural analysis. Temporal playback. Geospatial mapping. LLM reasoning. All views synchronized.
Deliver
Intelligence productsBriefs, network reports, entity profiles, risk assessments. Every finding traceable to source.
One semantic layer between your data and your decisions
Many sources converge into a single ontology — the narrow waist where objects, properties and links are defined. Analytics and applications fan back out from that one truth.
Dashboards, reports, alerts, workflows and agents — every product reads from the same ontology, so what one team sees, every team sees.
Analytical capabilities
Six capabilities operating on a shared ontology. Each handles a different analytical dimension. Results propagate across all views.
Graph Analysis
NetworksCentrality, communities, bridges — the structure behind the data.
Temporal Intelligence
TimePlayback, burst detection, change points — networks in motion.
Geospatial Mapping
PlaceLive maps with heatmaps and trajectories, wired to the graph.
LLM Integration
AI partnerAn LLM that reads the graph directly through structured tool calls.
Pattern Detection
SignalsLaundering structures, call-response chains, propagation patterns.
Data Ingest
SourcesMulti-source ingest with entity resolution into one semantic model.
Deep dive
Networks, decoded
Centrality metrics rank actors across multiple definitions — immediate reach, bridging importance, structural uniqueness. Modularity-based community detection reveals cluster structure at multiple scales; articulation points and bridges pinpoint the single links whose removal fractures the network.
- Centrality across multiple definitions — from direct reach to structural influence
- Modularity-based community detection, hierarchical
- Articulation points, bridges, structural holes, latent-link prediction
LLM assisted network analysis
Each mode builds on the previous. Chat discovers, Enrich refines, Anomaly surfaces, Report documents, Agent investigates.
Conversational Analysis
CHAT MODEAsk questions in natural language. The LLM queries nodes, runs algorithms, computes paths, and explains results — all through structured MCP tool calls. Every answer is grounded in graph data.
Design new analysis by describing it
The built-in algorithms are only the starting point. Describe an analytical idea in plain language and the platform assembles it into a graph algorithm that runs against the ontology. It composes the computation, exposes its parameters, and keeps the result as a building block you can reuse and refine.
- State the metric or pattern you are after and the platform turns it into a concrete graph computation.
- Every composed algorithm is inspectable and parameterised, never a black box.
- Save what works as a reusable analytical building block, ready for the next investigation.
Find the intermediaries quietly moving value between the financial core and the shell-company network, and flag the ones whose role grew right before the latest transfers.
Analytical chain
Query results
Highest cross-boundary betweenness; activity spiked just before the transfers.
Articulation point linking the financial core to the shell layer.
Analyst notes
Reusable metric
Saved as cross-boundary bridge score. Exposed parameters: community resolution, activity window.
Provenance
Every step is inspectable. The result traces back to the transaction graph.
Methods used
Investigators that live in the graph
Scale an analyst's attention across the whole graph.
One person cannot chase every lead in a large network by hand. The search has to run on its own and in parallel, and still stay controllable and traceable.
Investigators that live inside the graph.
Small autonomous actors sit on the graph itself. Each is bound to a single element (a person, a vessel, an event) and carries an assignment. They range from ready-made patterns for familiar cases to investigators that plan a multi-step approach on their own.
Bounded investigators pursue a clear goal and finish: find the hidden coordinator, trace the link from one entity to another.
Continuous monitors watch their territory and raise the alarm the moment something breaks the pattern.
From a defined assignment to an autonomous strategy.
- 1
Each actor receives a defined assignment.
- 2
It works out its own strategy to interpret and analyse the data model.
- 3
State awareness and communication between actors, so each builds on what the others surface.
A trail in the graph, not an answer in a chat window.
Connections drawn, paths marked, findings left where they belong. Each one stays open to inspection and tied back to the steps that produced it.
What you deliver
Traceable, structured outputs for analysts, case officers, or regulatory authorities.
Intelligence Briefs
Structured reports from graph data. Executive summary, findings, network analysis, timeline, recommendations.
Network Reports
Community structure, centrality rankings, bridges, structural holes. Visual maps with algorithm overlays.
Entity Profiles
Per-entity dossiers: properties, relationships, temporal activity, geospatial footprint, risk assessment.
Risk Assessments
Entity and network-level scoring based on centrality, community position, anomaly flags, temporal patterns.
The working surface behind every output
All four deliverables read from the same live, typed graph. Analysts explore entity aggregates, drill into communities, and validate findings against the raw stream — every export carries a verifiable lineage back to source records.
Ready to see it in action?
Graph analysis, temporal playback, and LLM reasoning — in one browser-native environment.
Get in touch
Whether you are evaluating intelligence analysis tools, consolidating fragmented workflows, or exploring LLM-assisted investigation — describe your use case.