Beyond Vector RAG: A NIST-Aligned Context Engineering Architecture and Go-To-Market Strategy for Engram.work

A doctoral-level market and systems research paper for enterprise adoption of keyword

• semantic + graph retrieval across 10,000+ document corpora

Prepared: December 31, 2025 Audience: Business Analysts, Product Marketing, Solutions Architects, and Risk/GRC Leaders Confidential Draft — for GTM planning and stakeholder review

 

Executive Summary

Enterprises attempting Retrieval-Augmented Generation (RAG) at scale routinely hit a reliability ceiling once their proprietary knowledge base grows beyond ~10,000 documents. The dominant “vector-only” retrieval pattern becomes increasingly brittle under three common conditions: (i) exact-match requirements (IDs, policy names, error strings), (ii) multi-hop questions whose answers are encoded in relationships across documents, and (iii) temporal drift where what was true changes over time. Engram.work’s context engineering thesis addresses this ceiling by combining three complementary retrieval modalities—keyword (BM25-style lexical search), dense semantic retrieval (vector), and temporal knowledge graph retrieval—then assembling a traceable, token-efficient context bundle for agentic systems.

This paper provides (1) a research-grounded technical architecture for 10k+ document retrieval using Temporal for durable ingestion workflows, Unstructured for document partitioning, and Zep/Graphiti for temporal graph memory and hybrid search; (2) an evaluation and observability regimen suitable for regulated enterprises; and (3) a market-ready go-to-market (GTM) strategy aligned to the NIST AI Risk Management Framework (AI RMF) functions—GOVERN, MAP, MEASURE, and MANAGE.

Key GTM outputs included:

·       Market framing: “context engineering” as a reliability and governance layer for enterprise AI, not merely another vector database.

·       Positioning: “Beyond vectors” – tri-modal retrieval + temporal knowledge graphs for multi-hop and point-in-time answers.

·       Buyer story: measurable reduction in hallucinations and time-to-answer; improved auditability through provenance and traces.

·       Packaging: modular platform (Ingest, Index, Graph, Assemble, Evaluate, Govern) with BYOC/VPC and compliance-ready controls.

·       Sales motion: design-partner pilots with benchmarkable retrieval metrics, culminating in a governance-ready rollout.

Abstract

Retrieval-Augmented Generation (RAG) is increasingly adopted to ground large language models (LLMs) in proprietary enterprise data, yet vector-centric retrieval alone frequently underperforms once corpora exceed 10,000+ documents and query demands shift from topical similarity to exactness, relational reasoning, and temporal correctness. This paper proposes a production-grade, tri-modal retrieval architecture—keyword (BM25), dense semantic vectors, and temporal knowledge graph traversal—integrated with durable ingestion workflows and governance-by-design. The approach synthesizes recent hybrid GraphRAG research and temporal agent-memory architectures to overcome “semantic blur,” multi-hop retrieval gaps, and time-varying factuality. We provide a measurement framework (faithfulness, context precision, latency, drift) and a NIST AI RMF-aligned operational model for deploying context engineering in regulated enterprises. Finally, we translate technical differentiation into a go-to-market strategy: segmentation, positioning, messaging, packaging, pricing logic, and a pilot-to-production sales motion.

Keywords: Context Engineering; Hybrid Retrieval; GraphRAG; Knowledge Graphs; Temporal Memory; Agentic Systems; Enterprise Search; NIST AI RMF; Governance; Evaluation

Contents

1. Introduction and Problem Definition

2. Related Work and Evidence Base

3. Research Questions and Hypotheses

4. Reference Architecture for 10k+ Documents

5. Evaluation, Observability, and Reliability Engineering

6. NIST AI RMF Alignment for Context Engineering

7. Market Analysis and Competitive Landscape

8. Go-To-Market Strategy and Execution Plan

9. Risks, Constraints, and Mitigations

10. Conclusion

References

Appendix A: Pilot Blueprint and Metrics

 

1. Introduction and Problem Definition

Enterprises are rapidly adopting LLM-powered assistants, copilots, and autonomous agents to accelerate knowledge work. However, the dominant enterprise deployment pattern—RAG over chunked documents with vector similarity—often fails to meet the reliability, auditability, and change-management requirements of production systems. The failure is amplified as the knowledge base scales beyond 10,000 documents and the organization expects the system to answer questions that require: (a) exact term matching, (b) cross-document reasoning (“multi-hop”), and (c) point-in-time correctness.

The core economic constraint is that LLMs are stateless and bounded by a finite context window; therefore, the system’s performance is dominated by what is retrieved and assembled into that window (“context engineering”). Recent production guidance emphasizes that robust systems combine keyword search, vector search, and graph traversal to maintain recall and precision across query types and to support relational reasoning.

Engram.work is positioned as an enterprise context engineering platform that operationalizes this shift: unifying ingestion, memory, retrieval planning, context assembly, and governance across tenant-scoped data and 10k+ document corpora.

2. Related Work and Evidence Base

Hybrid retrieval has matured from a best practice into a documented standard across major enterprise search stacks. For example, Azure AI Search defines hybrid search as a single query that runs full-text (BM25) and vector search in parallel, merging rankings using Reciprocal Rank Fusion (RRF).

In parallel, GraphRAG research has shown that questions over semi-structured knowledge bases often require both textual and relational evidence; hybrid systems that coordinate a bank of retrievers and refinement mechanisms can outperform single-modality baselines on hybrid QA tasks.

Temporal knowledge graph memory is an emerging solution to enterprise-grade context persistence. Zep and its graph engine Graphiti are designed for episodic ingestion, bi-temporal relationship tracking, and hybrid semantic + BM25 search with graph-aware reranking and point-in-time queries.

3. Research Questions and Hypotheses

This work is organized around three applied research questions:

·       RQ1: How can retrieval accuracy and answer faithfulness be sustained as document collections grow beyond 10,000 items?

·       RQ2: What architectural controls enable temporal correctness (what was true, when) for enterprise knowledge?

·       RQ3: How can a context engineering platform be positioned and packaged to meet both engineering and governance buyers, aligned to NIST AI RMF?

We propose the following falsifiable hypotheses:

·       H1: Tri-modal retrieval (keyword + vector + graph) increases top-1 hit rate and reduces hallucination compared to vector-only retrieval on heterogeneous enterprise queries.

·       H2: Temporal graph memory improves performance on knowledge-update and temporal-reasoning queries without materially increasing latency when combined with context assembly.

·       H3: Governance-by-design (provenance, policy filters, audit logs, evals) reduces deployment friction in regulated enterprises and shortens pilot-to-production timelines.

4. Reference Architecture for 10k+ Documents

The proposed architecture decomposes context engineering into six modules: Ingest, Index, Graph, Retrieve, Assemble, and Govern. Temporal provides durable execution for ingestion and refresh workflows; Unstructured provides layout-aware partitioning and chunk normalization; Zep/Graphiti provides temporal memory and hybrid retrieval over entities, relationships, and source text.

4.1 Ingestion and Normalization (Temporal + Unstructured)

Ingestion is implemented as replayable workflows (e.g., Temporal), ensuring every document and refresh operation has deterministic state, retry semantics, and auditable lineage. Documents are partitioned using Unstructured’s routing-based partition() capability, producing structured elements (headings, paragraphs, tables) and preserving provenance required for citations.

4.2 Dual Indexing: Keyword and Vector Retrieval

Each chunk is indexed into (a) a lexical engine (BM25/BM25f) for exact matching and (b) a vector engine for semantic similarity. Hybrid ranking uses Reciprocal Rank Fusion (RRF) to merge the two ranked lists, improving robustness without brittle manual weighting.

4.3 Temporal Knowledge Graph: Entities, Relations, and Time

A temporal knowledge graph stores extracted entities and relationships with validity intervals. This enables point-in-time retrieval and explicit contradiction handling (e.g., edge invalidation when facts change). The graph becomes the substrate for multi-hop retrieval: questions can be answered by traversing relations (system -> control -> evidence -> exception -> owner) rather than relying on vector proximity alone.

4.4 Retrieval Planning and Context Assembly

At query time, a lightweight classifier routes the request into one of four retrieval plans: (i) exact lookup (keyword-heavy), (ii) conceptual exploration (vector-heavy), (iii) relational/multi-hop (graph-heavy), (iv) temporal (graph + time filters). Candidate evidence from each modality is fused, reranked, and assembled into token-efficient context blocks suitable for agent frameworks, with provenance and policy metadata retained for auditability.

5. Evaluation, Observability, and Reliability Engineering

Agentic systems require more than standard uptime monitoring: organizations must be able to explain why an agent took an action, whether retrieved context was relevant, and whether the model’s answer remained grounded in cited evidence. A rigorous evaluation pipeline (“golden dataset” + offline metrics + online drift monitoring) is treated as a first-class production capability.

5.1 Metrics

·       Retrieval metrics: Hit@k, context precision, context recall, and RRF contribution analysis.

·       Generation metrics: faithfulness/groundedness (no unsupported claims), answer relevance, and citation correctness.

·       Performance metrics: p50/p95 retrieval latency, token cost per request, and throughput under concurrent load.

·       Governance metrics: policy filter effectiveness (blocked leakage), audit completeness, and incident response awareness.

5.2 Observability and Control Loops

Distributed tracing (e.g., OpenTelemetry patterns) should capture end-to-end execution: user input -> router decision -> retrieval calls -> tool execution -> final answer. A circuit breaker pattern halts repeated failures or budget overruns and escalates to humans.

6. NIST AI RMF Alignment for Context Engineering

NIST AI RMF organizes AI risk management into four functions: GOVERN, MAP, MEASURE, and MANAGE, emphasizing that governance is cross-cutting across the lifecycle. Context engineering platforms can map directly to these functions by treating retrieval, memory, and context assembly as controlled, measurable, auditable processes.

6.1 GOVERN: Accountability, Policy, and Oversight

·       Define owners for data sources, entity schemas, and retrieval policies; codify acceptable-use constraints and tenant boundaries.

·       Maintain audit logs for ingestion, indexing, query execution, and context assembly; expose provenance in the user experience.

·       Establish supplier risk posture for external models, parsers, and connectors; maintain SBOM-like inventories for AI components.

6.2 MAP: System Context and Risk Scenarios

·       Inventory knowledge sources and classify by sensitivity; map which AI tasks are supported (Q&A, summarization, decision support, automation).

·       Model threat scenarios: prompt injection, data exfiltration, cross-tenant leakage, and stale/incorrect temporal answers.

·       Document intended users, impacted stakeholders, and downstream business processes; define “high-impact” actions requiring human approval.

6.3 MEASURE: Evals, Drift, and Security Testing

·       Maintain golden question sets per domain; track retrieval and faithfulness metrics across releases.

·       Monitor embedding/model changes and ingestion pipeline changes; run regression tests before rollout.

·       Perform security evaluation: red-team prompt injection tests and leakage tests; validate access control filters.

6.4 MANAGE: Controls, Incident Response, and Continuous Improvement

·       Deploy circuit breakers and escalation paths for failed sessions; maintain rollback and decommissioning plans.

·       Operationalize feedback loops: user corrections become labeled data for evals; retrieval misses become new test cases.

·       Maintain ongoing monitoring of third-party dependencies and connectors; patch and rotate secrets under standard IR playbooks.

7. Market Analysis and Competitive Landscape

Market signals suggest strong and accelerating investment in RAG and AI-driven search as enterprises shift from experimental copilots to production systems requiring accuracy, speed, and governance. Third-party analysts forecast rapid growth for RAG as a category; while absolute numbers vary by methodology, multiple reports converge on high double-digit CAGR through 2030.

7.1 Demand Drivers

·       Explosion of private corpora (policies, tickets, code, contracts, emails) where “answers” must be auditable and permissioned.

·       Cost pressure: token spend makes context efficiency a financial requirement, not an optimization.

·       Risk pressure: regulators and internal GRC teams demand evidence of trustworthiness, oversight, and incident management.

·       Agentic workflows: as agents take actions, retrieval correctness becomes safety-critical.

7.2 Competitive Landscape (Category Map)

The competitive field clusters into five categories. Engram’s differentiation should be described as an integrated platform layer that coordinates multiple categories, rather than competing head-on in any single tool niche.

Category

Representative Solutions

Engram Differentiation Angle

Vector DBs / ANN Stores

Pinecone, Weaviate, pgvector stacks

Engram is not “just vectors”; it orchestrates lexical + vectors + graph + temporal correctness.

Enterprise Search

Azure AI Search, Elastic, OpenSearch

Agent-ready context assembly + temporal graph memory + governance instrumentation.

Graph Platforms

Neo4j, Stardog, RDF stacks

Operational graph extraction + episodic updates + fused retrieval for Q&A at scale.

Agent Frameworks

LangGraph, LangChain, LlamaIndex

Pluggable memory + retrieval planning + eval harness to harden agents in production.

Context Memory Vendors

Zep, bespoke in-house

Engram positions as enterprise “context engineering system of record” integrating workflows, security, and GTM packaging.

8. Go-To-Market Strategy and Execution Plan

8.1 Ideal Customer Profile (ICP) and Segmentation

Primary ICP (highest willingness to pay):

·       Regulated, knowledge-dense enterprises (insurance, banking, healthcare, critical infrastructure, government contractors).

·       Teams with internal copilots/agents already in pilot and suffering accuracy + auditability issues.

·       Organizations with multi-tenant or business-unit isolation requirements (strict RBAC, data residency, VPC/BYOC).

Secondary ICP (volume growth):

·       Enterprise software vendors embedding RAG into products who need ‘retrieval quality + governance’ as a differentiator.

·       Consulting and SIs delivering AI modernization and needing a repeatable context engineering backbone.

8.2 Positioning and Messaging

Core positioning statement:

Engram is the context engineering platform that makes enterprise AI reliable at scale: it fuses keyword, semantic, and temporal graph retrieval to produce traceable, policy-compliant context bundles for agents—aligned to NIST AI RMF and ready for regulated production.

Messaging pillars:

·       Reliability beyond vectors: tri-modal retrieval and multi-hop reasoning for 10k+ documents.

·       Temporal correctness: point-in-time answers and controlled knowledge updates.

·       Governance and auditability: provenance, policy filters, and evidence trails by design.

·       Time-to-value: durable ingestion workflows and reusable evaluation harnesses accelerate pilots.

8.3 Packaging and Pricing Logic

A modular packaging strategy reduces procurement friction by letting buyers start with ‘retrieval reliability’ and add governance modules as they move to production.

·       Starter: Ingest + Hybrid Search + Basic Context Assembly (single workspace).

·       Professional: Adds graph extraction, temporal queries, reranking, and standard observability.

·       Enterprise: Adds SSO/SAML, BYOC/VPC, audit exports, policy engine, eval pipeline automation, and SLAs.

Pricing should align to the value drivers: (i) number/size of indexed documents or tokens ingested, (ii) retrieval calls per month, (iii) governance features (audit, residency), and (iv) number of workspaces/tenants. For enterprise buyers, procurement-friendly annual contracts with usage bands are recommended.

8.4 Sales Motion: Pilot-to-Production

A repeatable 6–10 week pilot should be productized as the primary acquisition vehicle.

·       Week 1–2: Ingest 10k+ documents; implement access controls; establish baseline vector-only performance.

·       Week 3–4: Enable hybrid retrieval and graph extraction; define golden dataset and business-critical question sets.

·       Week 5–6: Run evals; quantify improvements (Hit@k, faithfulness, latency, time-to-answer); produce risk and governance report mapped to NIST AI RMF.

·       Week 7–10: Production hardening: audit exports, monitoring, circuit breakers, and rollout plan.

8.5 Channels and Partnerships

·       Direct enterprise sales to regulated verticals; co-sell with cloud partners where possible.

·       Solutions partners/SIs: package Engram as the standard context layer for enterprise agent delivery.

·       Developer-led adoption: open-source reference implementations and eval harnesses; publish hybrid retrieval benchmarks.

·       Strategic alliances: integrations and joint content with Temporal (durable workflows), Zep/Graphiti (temporal memory), and Unstructured (ingestion).

9. Risks, Constraints, and Mitigations

·       Data leakage and cross-tenant risk: enforce ACL-aware retrieval, pre-LLM policy filters, and audit logging.

·       Prompt injection and tool abuse: sandbox tools, validate outputs, and add guardrails and circuit breakers.

·       Staleness and contradictory facts: use temporal graph invalidation and scheduled refresh workflows.

·       Evaluation gaps: treat evals as unit tests; require regression gates for retrieval changes.

10. Conclusion

The evidence base and operating reality of large enterprise corpora indicate that vector-only retrieval is insufficient for reliable, auditable RAG once collections exceed 10,000 documents and questions require exactness, relational reasoning, and temporal correctness. A tri-modal context engineering architecture—keyword + vector + temporal knowledge graph—offers a principled and measurable path forward. For market adoption, Engram’s GTM strategy should foreground reliability and governance outcomes (not embeddings), productize a pilot motion with measurable evals, and align messaging to NIST AI RMF for regulated enterprise readiness.

References

[1] National Institute of Standards and Technology (NIST). (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0), Part 2: Core and Profiles (NIST AI 100-1). https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf

[2] NIST. (2025). NIST AI RMF Playbook. https://www.nist.gov/itl/ai-risk-management-framework/nist-ai-rmf-playbook

[3] Microsoft. (2025). Hybrid search using vectors and full text in Azure AI Search. https://learn.microsoft.com/en-us/azure/search/hybrid-search-overview

[4] Unstructured. (2025). Partitioning (Open Source). https://docs.unstructured.io/open-source/core-functionality/partitioning

[5] Zep. (2025). Graphiti overview (temporal knowledge graphs, hybrid search). https://help.getzep.com/graphiti/graphiti/overview

[6] Rasmussen, P., et al. (2025). Zep: A Temporal Knowledge Graph Architecture for Agent Memory (arXiv:2501.13956). https://arxiv.org/abs/2501.13956

[7] Lee, M.-C., et al. (2024). HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Bases (arXiv:2412.16311). https://arxiv.org/abs/2412.16311

[8] Sarmah, B., et al. (2024). HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation (arXiv:2408.04948). https://arxiv.org/abs/2408.04948

[9] MarketsandMarkets. (2025). Retrieval-augmented Generation (RAG) Market worth $9.86 billion by 2030 (press release). https://www.prnewswire.com/news-releases/retrieval-augmented-generation-rag-market-worth-9-86-billion-by-2030–marketsandmarkets-302580695.html

[10] Grand View Research. (2025). Retrieval Augmented Generation (RAG) Market Report. https://www.grandviewresearch.com/industry-analysis/retrieval-augmented-generation-rag-market-report

[11] Temporal. (2025). Simplifying Context Engineering for AI Agents in Production (webinar page). https://pages.temporal.io/webinar-simplifying-context-engineering

[12] Temporal. (2025). Temporal for Platform Engineering (solution overview). https://temporal.io/solutions/platform-engineering

 

Appendix A: Pilot Blueprint and Metrics

This appendix provides a business-analyst-ready pilot template that translates technical measurement into GTM outcomes.

A.1 Pilot Inputs

·       Corpus: 10,000+ documents across at least 5 source types (policies, tickets, PDFs, wikis, code).

·       Security: RBAC/ACL mapping; sensitivity labels; tenant/workspace boundaries.

·       Use cases: 30–100 golden questions spanning lookup, conceptual, relational, and temporal categories.

·       Baseline: vector-only retrieval with current chunking and embeddings.

A.2 Pilot Success Criteria (quantified)

·       Retrieval: +X% improvement in Hit@1/Hit@3; reduced ‘no-answer’ or irrelevant context rate.

·       Faithfulness: reduction in unsupported claims (hallucination rate) measured by evals and human review.

·       Latency: maintain sub-second retrieval p50 where feasible; document p95 and scaling behavior.

·       Governance: audit log completeness; demonstration of policy filters preventing cross-tenant leakage.

·       Business: measured time-to-answer reduction; reduced SME escalation; ROI estimate tied to labor savings.

A.3 Deliverables (for procurement and security review)

·       Architecture diagram and dataflow with trust boundaries.

·       NIST AI RMF mapping matrix (GOV/MAP/MEASURE/MANAGE) with implemented controls.

·       Evaluation report: metrics, sample traces, and error taxonomy.

·       Rollout plan: phased deployment, training, and ongoing monitoring.