ARCHITECT_BRIEFING_v2.5 • [STATUS: PRODUCTION READY] • [LOCATION: AUSTIN, TX]
// ROOT_DIRECTORY: /SLAKE/TECHNICAL_ARCHIVE

ENGINEERING REPOSITORY

ACCESS_LEVEL: UNRESTRICTED // DATA_INTEGRITY: VERIFIED // ARCHITECTURAL_DEFENSE: ACTIVE

01_TECHNICAL_DECISION_MATRIX

LAYER PRIMARY_TECH STRATEGIC_LOGIC GOTCHA
Embeddings OpenAI v3 (3072d) / Voyage-2 Use 3072d for maximum semantic resolution. Voyage-2 for high-rank RAG precision. 3072d adds 3x indexing latency vs 1024d.
Reasoning Command-R+ / Gemini 1.5 Pro Gemini for repo-wide 2M context. Command-R+ for native citation grounding. Long-context windows dilute attention; RAG still wins for precision.
Orchestration Dagster (DAG) + FastAPI Modular pipeline lifecycle. No no-code duct tape. Full audit trails/retry logic. LangChain for prototypes; code-centric DAGs for production.
Sovereign BGE-M3 / Local VPC Self-host when residency or API cost at scale (1B+ docs) outweighs infra. Requires 16GB-24GB VRAM per GPU executor.

03_MODEL_FAMILY_INTEROPERABILITY

We architect for a composition of specialized layers, not a monolithic model call. This ensures your capital is not tied to a single provider.

  • Embeddings = Your Semantic Index
  • Reasoning Models = Your Generation Layer
  • Guardrails = Your Operational Truth

04_CONTINUED_VECTORIZATION

Our systems treat institutional data as a living semantic asset. New text is embedded once into searchable units (chunks) and stored forever.

  • Real-time Ingestion via FastAPI
  • Semantic "Fingerprinting" (Embeddings)
  • Permanent Vectorized Persistence

02_INFRASTRUCTURE_SPECIFICATIONS

Storage & IOPS

SSD RAID-10 architectures with pgvectorscale. Optimized for disk-heavy HNSW indexes reaching >1M IOPS on 64-core hardware.

Horizontal Scaling

Read replicas for similarity search fanning. Citus sharding for 10TB+ corpora spanning billion-vector tables.

Integrity Protocols

Hybrid retrieval (Vector + Keyword) + Reranking. "Zero-Context Rejection" to enforce truthfulness over generation.

SECURITY_LAYER_v2.1

DATA_ISOLATION

Architectures utilize VPC-level isolation. Dedicated indexes per environment.

NO_TRAINING

Zero-retention policy for model training. Proprietary IP stays proprietary.

RAG_GROUNDING

Specifications enforce document-only retrieval. No generative hallucinations.

VECTOR_EXIT

Full client ownership of embeddings, vector weights, and system metadata.

OBJECT_COUNT (N=9) LAST_REPO_SYNC: 2026-04-08
[TYPE: ARCH_PATTERN]
Vertex AI Search Capital System (Ops Layer)
SLK_SPEC_VERTEX.LOG
[TYPE: ARCH_PATTERN]
Automated Lead Qualification Infrastructure
SLK_SPEC_LEADS.LOG
[TYPE: ARCH_PATTERN]
Modular B2B SaaS Automation Core (2026)
SLK_SPEC_STACK.LOG
[TYPE: BUILD_SPEC]
Data Entry Automation: LLM Pipeline Build Spec
SLK_LOG_DATA_ENTRY.LOG
[TYPE: BUILD_SPEC]
AI for Customer Success: Smart Routing Logic
SLK_LOG_ROUTING.LOG
[TYPE: BUILD_SPEC]
Vision AI & GPT-4: Reply Intent Detection
SLK_LOG_OUTREACH.LOG
[TYPE: BUILD_SPEC]
WhatsApp to CRM: Vision AI Data Extraction
SLK_LOG_WHATSAPP.LOG
[TYPE: SYSTEM_DESIGN]
Zapier vs. Custom Code: Cost & Scale Analysis
SLK_BRIEF_ZAPIER.LOG
[TYPE: SYSTEM_DESIGN]
Marketing for Engineers: Programmatic Lead Gen
SLK_BRIEF_REDDIT.LOG
// INITIATE_SYSTEMS_MODERNIZATION

SECURE YOUR CAPITAL SYSTEM.

INIT_SYSTEMS_AUDIT