Consolidated
One substrate, not six licenses
Graph, vectors, documents, workflow, and reasoning without the stitched-together stack or the glue pipelines that keep it barely standing.
Context Graph
One platform.
Not six systems.
Your data, documents, vectors, process state, and the reasoning behind every decision, all in one connected substrate instead of a stack of stitched-together tools. Every answer knows where it came from and when it was true.
Let's Connect
ONE QUESTION · EVERY SHAPE OF DATA
Ask: Which customers had an incident last quarter where the remediation cited our data-retention policy?
Entity
Northwind Mutual · Tier-1 customer, EU region
Process
Incident #4-118 · remediation workflow, closed in April
Document
Retention Policy §4.2 · the exact clause the fix cited
Reasoning
Remediation decision · who approved it, and why
Every result carries its source
Provenance: full
The problem
Your knowledge is scattered across systems that don't talk.
A graph database here, a vector store there, documents in one silo, workflows in another, and the reasoning nowhere at all. Every real question becomes an integration project.
The integration tax
Six licenses, six schemas, and a maze of glue pipelines just to answer questions that span more than one system. Cross-system answers arrive weeks late.
No memory of "when"
"What did we know when we made that call?" Most stacks overwrite yesterday's truth. Reconstructing it means digging through backups and hoping.
Provenance archaeology
Where did this number come from? Which document backs this claim? When the trail isn't captured as you work, every audit becomes a forensic project.
How it works
Everything your AI needs to know, in one place.
Four properties of the substrate turn "integrate six systems" into "ask one question."
01
Every shape of knowledge, one graph
Business entities, documents, vector embeddings, live process state, and reasoning artifacts live in a single typed graph. A question that spans all five is one query, not a custom pipeline.
Proof: a multi-shape substrate under one logical schema.02
Time is built in, not bolted on
The graph tracks both when something was true and when you learned it. "Show me what we believed on March 1" is a query, and the basis for any past decision replays on demand.
Proof: bi-temporal history with point-in-time replay.03
Provenance on everything
Every fact carries where it came from: which source, which document, which reasoning step. Answers arrive with receipts attached, captured as the work happens.
Proof: universal provenance edges on every artifact.04
Memory that knows the difference
Generic AI memory is one big pile of embeddings. Here, "what's structurally related," "what happened recently," and "what's our standard procedure" are distinct kinds of recall, for your people and your agents alike.
Proof: typed memory kinds with purpose-built retrieval strategies.
ONE SUBSTRATE · FIVE SHAPES
EntitiesCustomers, systems, products
DocumentsPolicies, contracts, reports
VectorsSemantic search, native
ProcessLive workflow state
ReasoningDecisions and their why
Time + provenance across all five
One typed graph. Cross-shape questions are queries, not integration projects.
What it means for you
Stop paying the integration tax.
When knowledge lives in one substrate, the questions that used to take a project take a query.
Trusted answers
Receipts on every answer
Ask where any fact came from and get the source, the document, and the reasoning step instead of a shrug. That's what makes AI output usable.
Time travel
Defend past decisions
Replay exactly what the organization knew at the moment a call was made. For regulated industries, that isn't a feature. It's the baseline.
AI-ready
The foundation AI actually needs
Grounded, provenanced, time-aware context is what turns generic AI into AI you can act on. Everything else GraphLogic does builds on this.
Ready to connect the dots?
See how one context graph replaces the stack of systems between your organization and its next decision.
Let's Connect