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

CHOOSE YOUR INFRASTRUCTURE DEPTH

WE DEPLOY PRODUCTION-READY AI SYSTEMS THAT YOU OWN INDEFINITELY. NO RENTED SEATS, NO VENDOR LOCK-IN, JUST RAW CAPITAL ASSETS.

START_AUDIT_($500) EXPLORE_TIERS
LOG: AUDIT_DISCOVERY

Infrastructure Audit

Map your operational search leaks and technical constraints in 60 minutes.

LOG: CAPITAL_SPRINT

Systems Build

2–4 weeks to deploy a live retrieval system wired into your CRM and docs.

LOG: STEWARDSHIP

Lifecycle Support

Ongoing signal tuning and technical monitoring for your private AI engine.

DEPLOYING: RAG_ENGINE_v4.2 STATUS: OPTIMAL ORCHESTRATING: AZURE_OPENAI_VECTORS STREAMING: SSE_PROTOCOL_ACTIVE IDENTITY: CLERK_SECURED DEPLOYING: RAG_ENGINE_v4.2 STATUS: OPTIMAL ORCHESTRATING: AZURE_OPENAI_VECTORS STREAMING: SSE_PROTOCOL_ACTIVE IDENTITY: CLERK_SECURED DEPLOYING: RAG_ENGINE_v4.2 STATUS: OPTIMAL ORCHESTRATING: AZURE_OPENAI_VECTORS STREAMING: SSE_PROTOCOL_ACTIVE IDENTITY: CLERK_SECURED

Where is your Information Tax highest today?

Field & Ops Search Tax Crews hunt for steps across PDFs, email, and chat. We embed a retrieval system that serves exact SOP and permit steps instantly.
Sales & Estimating Question Tax Teams repeat technical answers and rebuild proposals manually. We build a technical defense system that pulls approved answers directly from your corpus.
Management & Admin Glue Work Leaders and admins inspect 5 tools to perform manual checks. We implement a signal and automation loop that prepares briefings and closes loops automatically.
Vertex AI
OpenAI
Stripe
HubSpot
Postgres
[DEFAULT_ARCHITECTURE]

Sovereign Persistence: Every intelligence layer is deployed into your existing cloud infrastructure (GCP, AWS, or Azure). We leverage high-concurrency vector stores (Citus Sharded pgvector, HNSW via pgvectorscale) and Dagster-driven orchestration (DAGs) to transform institutional knowledge into a permanent, version-controlled repository asset. No brittle no-code workarounds.

[FEATURE_HIGHLIGHT]

From Documentation to Strategy in 1 Second.

We built a private RAG engine that synthesizes 500+ pages of Stripe OpenAPI specs into executive strategies and ready-to-sprint Jira tickets. We can deploy this same intelligence for your internal knowledge base.

Explore the Architect Demo

How Your Data Flows

We don't ignore schemas or folder structures; we build pipelines that manage them.

01

Extract

We connect to live systems (CRM, Google Drive, field apps) using APIs or approved connectors to pull records that power your workflow.

02

Normalize

We parse PDFs, run OCR on scans, and normalize fields so data from CRM and docs fits a consistent structure.

03

Index

Content is broken into semantic chunks, embedded, and stored in a vector index with metadata pointing back to original systems.

04

Serve

Staff ask questions via Slack or a custom UI and get answers grounded in your live, underlying records.

[INTELLIGENCE_STACK_v2.5]

Intelligence Decision Matrix

Architecture implies choice. We choose models based on cognitive load, data-plane latency, and long-term sovereignty—never hype.

01 // THE_KNOWLEDGE_BASE

Embedding Models

Defines how your AI system encodes context and meaning across unstructured data. This is the cornerstone of retrieval accuracy and generalization.

  • OpenAI 3-Large / Small: High semantic density (3072 dims). Reliable for broad corpora and multilingual corporate content.
  • Voyage-2 / Cohere-v3: Optimized for domain-specific, long-context RAG. Excellent for technical specs, manuals, and structured docs.
  • BGE-M3 / Mistral / Instructor: Open models suited for private or sovereign deployments. Balances efficiency with controllable hosting.
  • Jina-v2 / Fireworks / Nvidia-NEMO: Next-gen high-throughput options for GPU inference or enterprise-scale RAG clusters.
// STATUS: EMBEDDED_DATA_REMAINS_FIXED_GROUND_LAYER
02 // THE_REASONING_ENGINE

LLM Selection

We decouple reasoning from knowledge. Switch models—or vendors—without re-indexing your data as the cognitive frontier moves.

  • Claude 3.5 Sonnet / Opus: Exceptional reasoning chains and contextual memory. Ideal for research and complex compliance audits.
  • Gemini 1.5 Pro / Flash: Unmatched 2M-token context; built for querying full repositories or massive CAD/document archives.
  • GPT-4o / GPT-4-Turbo: Balanced latency and depth; strong tool-use integration and native multi-modal input pipeline.
  • Llama 3 / Mixtral / Command-R+: Open architectures for self-hosted reasoning layers. Ideal for on-prem orchestration.
// STATUS: DECOUPLED_LOGIC_PREVENTS_VENDOR_LOCKIN
03 // THE_INTEGRITY_LOOP

Knowledge Safeguards

A closed-feedback QA system that enforces provenance and factuality. Integrity is a function, not an opinion.

  • Context Grounding: Every answer cites verifiable document vectors by ID or source reference with provable provenance.
  • Zero-Guess Rejection: If the answer is not found in the Knowledge Base, the system refuses output. No hallucinations permitted.
  • Hybrid Retrieval: SQL + Vector + Metadata filtering ensures exhaustive access across structured and semantic layers.
  • Reinforced Feedback: Optional integration of human review signals to tune ranking scores for continuous precision improvement.
// STATUS: TRUTH_ENFORCED_VIA_PROVABLE_ATTRIBUTION
[OFFER_01: AUDIT]

Infrastructure Audit

$500
PAID TECHNICAL DISCOVERY
Outcome: 1-2 Page Technical Plan & Build Quote

Wait for whether an internal AI retrieval system is actually worth building for ONE specific workflow. A 45–60 minute paid technical discovery, credited to the build.

The Process:

  • Intake: Short form completed before the session.
  • Session: 45–60 minute deep dive into one real workflow.
  • Mapping: 48–72 hours of offline technical mapping.
Book Your Audit
[OFFER_03: RETAINER]

Stewardship

Retainer
ONGOING OPERATIONAL CARE
Outcome: Maintained & Evolving Pipelines

Internal engineers are expensive. Stewardship keeps your system alive as APIs change, folder structures drift, and schemas evolve.

Responsibilities:

  • Monitor Ingestion: Fix connectors when they fail.
  • Maintain Parsing: Update OCR for new documents.
  • Tune Retrieval: Adjust chunking as corpus grows.
Apply for Stewardship
[PREP_REQUIRED]

Preparation for Audit

  • One Workflow: A concrete loop where people constantly hunt for information.
  • One Real Example: A live ticket, job, or deal you can pull up on screen.
  • Access: Screen‑share access to CRM / docs / tools that hold that data.
  • Decision Maker: Someone on the call who can speak for budget and access.
[README.md]

Systems Documentation

> Do we own the system and data?

// YES. You own the Capital System as an asset. All code, keys, and documentation are transferred to you. We believe in Sovereignty: blocks of code you control, not rented platform features.

> Is this custom software or a platform?

// ARCHITECTURE. It is a Capital System architecture composed of retrieval, signals, and automation loops. You pay for the underlying infrastructure (cloud hosting, LLM tokens) directly.

> Who maintains this system?

// TRANSITION. We provide a 30-day warranty. Most partners transition to a Stewardship Retainer for ongoing monitoring, signal tuning, and extending the architecture.

Ready to kill your worst Information Tax?

Start with a paid Infrastructure Audit. We’ll map the manual search leaks in your core workflow and provide a clear 1–2 page technical plan for the build.

START_AUDIT_($500)