GraphRAG Commercial Brief

This page is the concise, investor-facing explanation of the current GraphRAG stack. It is intentionally commercial in framing, but every claim here maps back to code, benchmarks, or tests that exist in this repository today.

Important

The performance figures below are local microbenchmarks of core GraphRAG building blocks, not full end-to-end user latency. They are useful because they show that graph-specific orchestration remains compatible with an interactive product UX on commodity hardware.

Executive summary

RagLogic AI does not rely on a single vector lookup followed by a generic LLM answer. The platform combines:

  • routed retrieval,

  • parallel semantic and lexical search,

  • graph-aware expansion over regulatory relationships,

  • bounded context assembly before generation,

  • source and citation controls for answer traceability.

In commercial terms, this means the product can answer questions that require more than isolated text chunks. It can connect legal acts, amendments, cross-references, and structural dependencies while preserving explainability.

Why the graph matters commercially

Commercial need

What GraphRAG adds

Trust

The graph makes relationships between acts explicit instead of leaving the model to infer them from prose alone.

Better coverage on complex questions

Questions involving amendments, citations, or multi-act obligations benefit from graph traversal and graph-ranked context.

Faster analyst workflows

The runtime can surface both documentary evidence and relationship evidence in the same answer flow.

Product defensibility

The graph layer becomes a reusable knowledge asset rather than a prompt-only feature that competitors can copy quickly.

Clearer demos for investors and clients

The technology can be shown as a traceable decision pipeline, not as a black-box chatbot.

Why this implementation is fast

The current design stays performant because it does not brute-force the graph.

  1. Retrieval is routed first, so the graph is only emphasized when the question justifies it.

  2. Query variants and collection searches run in parallel instead of serially.

  3. Graph ranking starts from semantic seeds and relationship hops, not from a full-graph scan.

  4. Context assembly is budget-aware, so graph evidence is trimmed before it can bloat generation cost.

  5. Read-only Cypher validation is extremely cheap, which keeps the graph support branch safe without adding meaningful latency.

Measured benchmark snapshot

Benchmark source: scripts/benchmark_graphrag.py

Snapshot dataset: docs/runtime/graphrag-benchmarks.json

Environment: Python 3.12.11 on Linux 6.12, Intel Core i7-9850H CPU @ 2.60 GHz. Snapshot date: 2026-04-19 UTC.

Building block

Workload

Result

Why it matters

Read-only Cypher validation

5,000 iterations, 173-character query

0.021 ms median, 0.041 ms p95, about 47.6k validations/s

Safety checks on Cypher are effectively free at runtime scale.

Graph ranking

1,000 acts, 4,985 relationships, 20 seed acts

19.31 ms median, 20.89 ms p95

Graph expansion stays in the low tens of milliseconds even on a dense synthetic fixture.

Graph context budgeting

24 semantic results, top 150 nodes, top 300 relationships, 20k-char budget

0.882 ms median, 1.312 ms p95

The system can compress graph evidence into a generation-ready context almost instantly.

Parallel semantic fan-out

3 query variants across 2 collections

72.78 ms median parallel vs 124.77 ms sequential, 1.71x speedup

Parallel search materially improves responsiveness before generation even starts.

Testing status behind the claims

The benchmark numbers are backed by targeted tests on the current graph and retrieval contracts, not by documentation-only statements.

Validation command:

./.venv/bin/pytest -q \
  tests/test_graph_cypher.py \
  tests/test_neo4j_adapter.py \
  tests/test_retrieval_parallelization.py \
  tests/test_rag_sources_contract.py

What this validation slice covers:

  • read-only Cypher guardrails,

  • Neo4j adapter behavior,

  • retrieval parallelization semantics,

  • source, citation, and grounded-answer contracts on the RAG path.

Current result: 38 passed on 2026-04-19.

Commercial interpretation

The current GraphRAG implementation is strong commercially for three reasons.

  • It improves answer quality on the hard questions that matter in enterprise legal and regulatory workflows: cross-document, cross-reference, and amendment-heavy questions.

  • It stays inside an interactive latency envelope because the graph-specific overhead is mostly sub-millisecond to low-tens-of-milliseconds before the LLM call.

  • It produces a more defensible product story: proprietary knowledge graph, explainable retrieval pipeline, and grounded answers rather than generic chatbot output.

Boundaries

  • These figures do not include LLM generation latency, network hops, or live storage variance.

  • The graph benchmarks are synthetic but intentionally shaped around the code paths that dominate GraphRAG orchestration cost.

  • This page should be updated whenever the benchmark snapshot or the retrieval architecture changes.