August 2025 - Cross-Environment Isolation and Retrieval Tuning¶
Context¶
Pre-production month with two pilot clients, not yet open to the public. Both foundation-phase R&D lines advanced: tenant isolation was pushed to the harder cross-environment case, and the hybrid-retrieval quality/latency investigation moved from direction to a concrete operating point. Most of the remaining work was the measurement and experimentation apparatus those investigations depend on; only product delivery is excluded.
Maps to 2026 R&D projects: Compounding Context Engine for Company, Industry, and Buyer Intelligence (tenant isolation and hybrid-retrieval quality).
Multi-Tenant Isolation for a Graph-RAG Stack (continued)¶
Project and lock¶
July resolved runtime query isolation within one environment. The open part of the same lock was isolation under data movement: copying a tenant's graph between test and production while preserving relationship integrity and proving no leakage occurred during or after the transfer. Cross-environment operations open new leakage paths, and there is no standard pattern for migrating one tenant out of a shared multi-tenant graph database without disturbing the others.
This month's work¶
The isolation layer was extended so a single tenant's subgraph can be extracted, transferred, and reinserted with its relationships intact, and isolation re-verified after the copy. The genuine uncertainty addressed was integrity and non-leakage during selective extraction from a shared graph — not the operator ergonomics around it.
Results, proof, and next step¶
Integration tests and manual validation confirmed relationship integrity after copy and no cross-tenant leakage. The isolation-under-transfer problem extended July's lock; the command-line tooling and confirmation prompts built around it were ordinary delivery. Next step: keep the remaining July query-pattern coverage open into later months.
Hybrid Graph-Vector Retrieval at B2B Production Quality and Latency (continued)¶
Project and lock¶
July left the operating point unknown: which retrieval settings let the hybrid graph-plus-vector system answer well while staying inside the latency budget, for B2B content with no prior benchmark. The three objectives — answer quality, response latency, and cost — trade off against each other and interact non-linearly, so they had to be tuned as a set rather than independently.
This month's work¶
The hybrid-retrieval parameters were made configurable and tuned empirically, driven by reading execution traces and re-comparing on fixed query sets. The knobs that actually moved behavior were the number of vector chunks retrieved, the number of graph entities, the total context-token budget, and the similarity threshold; the query-processing and generation models were chosen to keep retrieval cheap while keeping answers good. Because no benchmark existed for this combination on B2B content, the operating point was discovered through systematic iteration, not configuration lookup. The tuning being trace-driven, the execution tracing is its measurement instrument; and several other pieces of apparatus, built the same month, are part of the same investigation: a per-request retrieval-mode switch to run hybrid-versus-local-versus-global comparisons with mode-tagged traces, resolution of a library event-loop conflict that had been crashing the retrieval system under concurrent load (without which no experiment could run), a per-tenant configuration substrate that applies and compares per-client experimental conditions without redeployment, and stateful conversation memory that makes the multi-turn behaviour under study observable. Only the generic CRUD and lifecycle parts of these are ordinary work.
Results, proof, and next step¶
The decisive, defensible result is latency: moving off the container-embedded store and tuning the retrieval set brought pilot response time from the several-second range July recorded as unacceptable down to roughly three seconds, under the five-second target. This is an observed pilot improvement, not a controlled benchmark. No quality measurement existed yet — the evaluation harness was not built until September — so August makes no numeric quality claim; an earlier draft's quality-gain figure was a recycled July estimate and has been removed. This was the systematic-tuning arm of the quality/latency investigation. Next step: build the harness that scores quality, then test reranking against it.
Not retained as R&D¶
Widget rendering and lifecycle improvements — product delivery with no nexus to a named experiment.
Next Work (September)¶
- Build the dataset-evaluation harness so retrieval quality becomes measurable.
- Test document reranking against that harness.
- Prepare for the October production launch.