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The substrate of Semantic Engineering.
This graph layer is what every Accion platform shares.
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SKG
Stop rebuilding grounding for every copilot: one governed knowledge graph that every agent queries.
Overview
SKG is our Semantic Knowledge Graph: the governed, queryable model of your enterprise's concepts, relationships and rules. Agents across products, processes, people and data query it for grounded context and write validated results back. It is the graph layer that BreezeAI, SPEX, ASIMOV and ECL stand on, and the reason grounding gets built once instead of once per copilot.
The Challenge
Every new agent or copilot starts by rebuilding the same context: what a product is, which policy applies, who owns what. That work is duplicated per team, drifts per copy and cannot be audited anywhere. The result is inconsistent answers from tools that are each individually confident, and the second or third copilot on the roadmap makes the cost visible. SKG provides the shared source of meaning these tools each lack.
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What We Deliver
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A governed, queryable graph. SKG models concepts, relationships, and rules as one connected model, generalizing the four-layer ontology BreezeAI proved in engineering to the wider enterprise: products, processes, people, and data on a graph-database foundation.
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Support automation from the same graph. Continuous ingestion from ITSM, monitoring, RPA, and engineering tools keeps the graph current as a byproduct of daily work, with no separate documentation effort.
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Immediate failure response. When something fails, dependency edges show the blast radius at once, resolution playbooks rank by historical success rate, and every resolved incident enriches the graph, so the next resolution is faster than the last.
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Flexible deployment by data velocity. Where knowledge accretes over time, a materialized graph store fits. Where the underlying data moves fast, SKG runs as a virtual graph layer: a query engine reads your warehouse or lakehouse tables in place, with no ETL pipeline and no second copy of the data.
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Governed meaning either way. Query-in-place suits fast-moving data and shallow traversals, while deep multi-hop analysis favors the materialized store. Meaning stays governed in both cases.
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Pluggable storage, Accion IP above it. Accion's IP is the layer above the store: the ontology schemas, validators, and governance tooling. The storage and query engines underneath are pluggable.
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Grounded, traceable agent operations. Agents read grounded context from the graph and write results back through validation under named ownership, so one graph serves every platform that needs grounding, and every answer traces to a governed node rather than a document guess.
How SKG Works
One graph that every agent reads from and writes to makes grounding consistent, reusable and auditable. New assistants inherit the enterprise's meaning on day one instead of rebuilding it.
Model
Represent concepts, relationships and rules as a governed graph.
Query
Serve agents the grounded context their task needs.
Write back
Accept only validated results into the graph.
Govern
Enforce named ownership and validation so meaning stays trusted; invalid writes are rejected at the gate and routed to the custodian for correction.
What You Get
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A governed, queryable knowledge graph
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One shared source of meaning across platforms
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Grounded context for every agent
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Validated write-back under named ownership
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Consistent, auditable knowledge
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Materialized or virtual deployment, chosen by data velocity
Proof Points
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A large electrical distributor with millions of product SKUs
A data-quality outcome first: client-reported results after linking SKUs, categories, vendors and rebates in one governed graph were 40% fewer duplicate SKUs and 30% higher quote-to-order conversion.
Why Accion
The layer beneath the platforms.
One graph grounds engineering, operations and analytics, so investment compounds instead of fragmenting.
Named ownership.
Every part of the graph has a human custodian who answers for its accuracy.
Store-agnostic by design.
Materialized or virtual deployment; the IP is the schemas, validators and governance above the store.
A compounding asset
Daily operations enrich the graph as a byproduct, so value grows with use.
Cut hallucinations at the root
Grounding and guardrails create a safe environment for trustworthy AI that can inform real decisions.
Why Accion
Semantic Engineering at the core
Deterministic, explainable AI grounded in your enterprise knowledge, not generic models.
Engineering depth
Decades of product, platform and data engineering behind every AI build.
Outcome-led delivery
Measurable business impact, not proofs of concept that stall before production.
IPs and accelerators
AI Prism, BreezeAI, ASIMOV, SPEX and more shorten time to value.
Governed by design
Security, compliance and Responsible AI built in from the first sprint.
Find out what Semantic Engineering at the core can bring to your business
FAQs
SKG is Accion Labs' Semantic Knowledge Graph: a governed, queryable model of enterprise concepts, relationships and rules that grounds agents across products, processes, people and data. It is the layer beneath the platforms.
A catalog inventories data assets; a vector store retrieves similar text. SKG models meaning: typed entities, relationships and rules that agents query precisely and write back to under governance. Catalogs and vector stores can feed the graph; neither replaces it.
No. Where knowledge accretes, a materialized graph store fits. Where the data changes by the minute, a virtual graph query engine reads your existing warehouse or lakehouse in place, with no ETL pipeline to build. Most enterprises end up with both, chosen per domain by data velocity; Accion's schemas, validators and governance run the same way over either.
The store is not the hard part; SKG treats it as pluggable. What determines whether agents can trust the graph is the ontology schema, the validators and the ownership model on top, and that is the layer Accion productized. Teams that start from a bare graph database rebuild that layer by hand; teams that buy a closed ontology platform inherit its lock-in. SKG keeps meaning governed and the store portable.
SKG is the layer; ECL is the linking capability inside it. ECL connects each entity to its validated context in the graph, which is what makes assistant answers citable.