ARCHITECT_BRIEFING_v2.5 • [STATUS: PRODUCTION READY] • [LOCATION: AUSTIN, TX]

Engineering Capital Architecture for High-Execution Teams.

I design and deploy the low-latency intelligence layers that activate your internal data. No brittle no-code workarounds. No agency bloat. Just audit-ready systems code.

REQUEST_COMMAND_AUDIT ()

System Integrity Profile // MOD_ID: PIPELINE_V3

[01] THE_IDEMPOTENT_LOOP

TRIGGER: Dagster DAG (Orchestration)

PROXY: FastAPI Microservice (Embed)

STORE: pgvector (HNSW Indexing)

ASSET: Continued Vectorization (Store Forever)

[STATUS: 200_OK] // [LATENCY: < 15ms]

// GLOSSARY_OF_AUTHORITY

Weights vs. Embeddings

Weights are the static parameters trained once into the model. Embeddings are the dynamic outputs (semantic fingerprints) generated per-input. We build systems that manage embeddings as sovereign assets.

Searchable Unit (Chunks)

We don't vectorize words; we vectorize 1000-char Semantic Chunks. This ensures the reasoning layer has contextually rich candidate evidence for grounded generation.

// THE_SOVEREIGN_MANDATE

Code is the only way to build for production.

Agency-style "No-Code" automations (Zapier, Make) are excellent for prototyping, but they break at scale. They lack version control, audit trails, and predictable failure modes.

"Slake Design builds with actual code because your core operations deserve Institutional Reliability, not drag-and-drop luck."

01_What_I_Build

[01]

I design and deploy Internal Applications wired directly into your operations. These aren't bolted-on generic tools; they are the custom logic layer for your business.

[02]

I architect Capital Systems. These tools act as high-performance dashboards and deal desks that answer active business queries without requiring a Slack thread.

[03]

The objective is simple: Information Tax Elimination. I reduce manual search cycles and increase execution density per team member.

02_Technical_Capabilities

Capital Architecture

Orchestration of distributed ingestion pipelines. We use Ray Data and Docling to coordinate CPU-heavy parsing with GPU-heavy embedding, ensuring semantic structure is preserved at scale.

Ray Data Docling Python

RAG / Intelligence

Moving retrieval hit rates from 30% prototypes to over 90% production-ready systems. We implement advanced partitioning and metadata filtering to eliminate Semantic Drift.

pgvector Metadata Partitioning

Schema Contracts

Every system is built on Strict Schema Contracts. We prioritize version-controlled ingestion paths and audit trails over brittle "black box" no-code automations.

TypeScript Git Ops Audit Trails

Observability

Continuous evaluation of system Faithfulness. We build "Safe Execution Paths" that prefer admitted uncertainty over hallucination, maintaining 100% institutional trust.

RAGAS Observability Grounded AI

Why work with me directly?

You aren't dealing with an agency layer. You work with the person who designs and builds the system. There are no hand-off issues or junior dev surprises. I build production systems with clear ownership and audit trails.

Operational Discipline & Controls:

Predictable Failure: I map every failure mode before writing code. Recovery paths are built for business continuity.
Auditability: I treat data like a ledger. Every change is logged and traceable.
Maintainability: I document and structure tools so your next senior engineer can own them without a two-week oral history.

Austin, TX.