Loading Resource...
Connect AI securely to organizational knowledge, documents, data, and decision workflows.
Enterprise artificial intelligence is only as valuable as the internal knowledge it can reliably access. Evergreen architects high-precision Retrieval-Augmented Generation (RAG) systems utilizing private deployment architectures designed to keep sensitive data within controlled processing boundaries. We integrate dense-sparse hybrid search, access-control-aware retrieval, exact citation provenance, and local vector indexing so employees get instant, verified answers without exposing sensitive data.
We focus on solving concrete engineering, data, and organizational bottlenecks rather than speculative generative AI hype.
Critical technical manuals, policy documents, and engineering guidelines are scattered across disparate cloud drives, wikis, and local shares, requiring extensive manual search time.
Public LLMs answer domain-specific questions with fabricated or outdated information because they lack access to real-time, authoritative internal documentation.
Standard search tools risk surfacing confidential HR, legal, or executive documents to unauthorized personnel who lack requisite security clearance.
Users cannot verify the source document, page number, or paragraph supporting an AI response, making generated answers unusable in regulated audits.
Sending proprietary engineering drawings or customer records to public cloud embedding APIs violates compliance policies and risks intellectual property loss.
Instant, verified semantic search across millions of documents with responses grounded directly in organizational source materials.
Every generated statement accompanied by direct, clickable links to exact document chapters, page numbers, and verified paragraph excerpts.
Query filtering that enforces Active Directory, LDAP, or RBAC permissions at the vector index level so users only retrieve documents they are authorized to view.
Embedding generation, vector indexing, and model synthesis hosted within private cloud VPCs or air-gapped on-premise infrastructure with controlled external transmission paths.
Automated pipelines measuring groundedness, context relevance, and faithfulness to ensure retrieval quality does not degrade over time.
Evergreen designs knowledge architectures from a systems engineering perspective. We optimize vector index density, chunking strategies, and database row-level security to deliver sub-second search with verifiable data isolation.
Combining dense vector embeddings for semantic concept matching with sparse lexical search (BM25) for exact keyword and part-number precision.
Inheriting enterprise identity groups and applying PostgreSQL Row-Level Security (RLS) directly to vector tables to prevent permission leakage.
Preserving document structure, parent-child relationships, tables, and section hierarchies during document ingestion.
Deploying private pgvector nodes and dedicated embedding models designed to keep sensitive organizational data within customer-controlled infrastructure boundaries.
Every client engagement produces tangible architectural artifacts, verifiable codebases, or defensible strategic roadmaps.
End-to-end design of high-density knowledge retrieval systems tailored for technical, legal, and operational document corpora.
Configuring reciprocal rank fusion (RRF) combining dense neural embeddings with exact lexical matching for domain-specific terminology.
Compiling and tuning low-latency semantic indexing using pgvector with HNSW (Hierarchical Navigable Small World) graphs.
Extracting structured entity relationships from unstructured documentation to resolve multi-hop queries and complex organizational dependencies.
Binding Active Directory / OAuth user tokens directly to vector search filters to enforce strict document-level permissions.
Automated extraction pipelines handling complex PDFs, scanned tables, technical drawings, and multi-column engineering reports.
Deploying automated evaluation frameworks measuring faithfulness, answer relevance, context precision, and hallucination rates.
Deterministic architectures enforcing data perimeter containment, least-privilege tool execution, and clear trust boundaries.
Dense-sparse hybrid search with access-control filtering and citation provenance.
Parses PDFs, docs, and tables; enriches with metadata and hierarchical chunking.
Generates 1024-dimension vectors using private on-premise embedding models.
Stores vectors in PostgreSQL with Row-Level Security bound to user identity.
Applies lexical BM25 + dense search fusion, filtering results by user permissions.
Synthesizes answer with mandatory clickable citation links to source documents.
Informed by NIST SP 800-53 security controls, HIPAA privacy safeguards, and zero-trust data perimeter standards.
Enforced Validation Controls:Evergreen designs private knowledge retrieval architectures. Client data remains within customer-controlled infrastructure and is never retained, logged, or used for model retraining by Evergreen.
Strictly classified in accordance with Evergreen's published claim-governance rules.
Context: Evergreen published a tested reference architecture blueprint detailing secure vector search using pgvector, HNSW indexing, and Row-Level Security designed to keep sensitive data within controlled processing boundaries.
Approach: Features dual-tier chunking, metadata enrichment, reciprocal rank fusion, and dedicated private embedding generation for regulated sectors.
Outcome / Spec: Available in the Evergreen technical library with complete schema specifications and trust boundary models.
Context: A regional electric utility operator required private document retrieval to assist 1,200+ field technicians across complex grid maintenance manuals.
Approach: Designed local pgvector indexing and dedicated embeddings to support rapid technical lookup without external cloud API exposure.
Outcome / Spec: Maintained sensitive data within controlled boundaries and validated rapid search across high-volume maintenance manuals.
Initiate a confidential technical consultation with our engineering and strategy team. All inquiries are routed directly to Senior Principal Dr. Felix Njeh and our technical architecture practice.
Confidential intake • Zero client data used for model training • Austin, Texas • Nationwide Enterprise & Public Sector Deployments