A software platform that codifies expert (often tacit) knowledge, semantically unifies data into a Knowledge Graph, and lets users (or agents) ask questions and trigger actions through natural-language prompts—democratising expertise without requiring users to be experts themselves.
A KF is a specialised plug-in that runs inside the graph, applies domain algorithms (e.g., risk models, video vision), and creates new knowledge that downstream agents can chain together.
Unlike context-window prompt stuffing or RAG, Accrete stores entities + relationships explicitly, supports multi-hop reasoning, and persists new insights; LLM-only systems “reset each session” and miss hidden links.
(1) Data Layer—ingests siloed sources; (2) Ingestion Layer—extracts & normalises subject-predicate-object triplets; (3) Knowledge Function Layer—runs codified expert models; (4) Agentic Layer—a suite of planners, memory, coding & other agents that invoke KFs and chat with users.
During a conversational onboarding, the agent asks clarifying questions, identifies entities/relationships in existing data, and links them into the graph—without teams of human ontology engineers.
Creating a single enterprise-wide schema is “expensive and time-consuming”; Accrete instead drives the ontology from the data itself, guided by the problem the user is trying to solve.
Accrete indexes relationships, enabling multi-step inference; LLM embeddings alone are “one-step or shallow” and lose structure.
Each new interaction is stored and compounds the graph for long-term learning, whereas vanilla LLMs forget once the prompt ends.
Agents can traverse several edges in the graph (e.g., company → supplier → foreign influence) to answer complex “why” or “impact” questions reliably, something embedding-only systems struggle with.
They expose APIs (e.g., narrative clustering, risk modelling) that planning agents orchestrate; coding agents even auto-generate execution plans.
An Expert Agent is a composite that invokes one or more KFs plus the graph to deliver decision automation (e.g., ITSM Expert or Argus Supply-Chain).
1️⃣ User describes a problem in chat → 2️⃣ Agent asks follow-ups → 3️⃣ Triplets extracted & normalised → 4️⃣ Graph built → 5️⃣ KFs added → 6️⃣ Agents deliver answers & dashboards.
Ticket resolution dropped from 22 → 8 days for a Fortune-50 company, averting potential $1.2 M/hour downtime costs.
It encodes analyst know-how to predict viral narratives, cutting analyst “scroll time” 94 % and spotting influencers 6.7× faster.
A Video Vision KF scores calls; agents blend those scores with CRM data to surface “top-3 deals at risk,” yielding 85 % time savings.
Yes—Accrete exposes an interface so multiple engines can share selected knowledge, tools and agents.
Foundation models, web-search agents, vector databases and any structured/un-structured corpora ingested through Layer 2.
While granular security details are client-specific, data remain inside the customer’s graph; only authorised agents/KFs can access sensitive nodes—mirroring the platform’s focus on enterprise “ground truth.” (Implied throughout positioning docs.)
No; model vendors supply raw language understanding, while Accrete delivers the reasoning & ground-truth layer those models lack.
Expert interactions are logged in memory, then converted to graph facts and KF logic—scaling hard-won intuition across the org.
Expansion of the multi-agent framework, more plug-and-play KFs, and deeper vertical solutions (e.g., finance, gov-intelligence) per internal roadmap slides.