CAPABILITIES · ENGINEERING + FINOPS

THE ARSENAL

We fuse Senior Engineering with Financial Strategy (MBA) to audit your code and your balance sheet simultaneously. Every architectural decision is measured against ROI. No experiments on your dime.

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THE PROBLEM

You're burning runway on the wrong problems.

  • 01Cloud and AI API costs scaling faster than revenue—infrastructure eating margin.
  • 02Technical decisions made by trend-followers, not balance-sheet readers.
  • 03The gap between 'working MVP' and 'fundable architecture' is wider than you think.
THE ARSENAL

Engineering + FinOps Fusion

Three capabilities. One integrated vision. We don't do isolated projects—we architect systems that compound value.

01

PRODUCT ENGINEERING

Your MVP doesn't scale—or it's bleeding money

Full-Stack SaaS & Cloud Architecture

We take your messy MVP and turn it into an asset investors can underwrite. Backend optimization, database caching, high-throughput API design. Your architecture survives the first spike of users without burning AWS credits.

Output:

Production-Ready MVP • CI/CD Pipelines • Low-Latency DB

02

AI & AGENTIC WORKFLOWS

You can't afford the team you need

MCP Protocol & AI Engineering

We don't 'wrap ChatGPT'. We engineer Agentic Workflows that act as force multipliers for your skeleton crew. MCP servers connect LLMs directly to your internal data and tools, creating autonomous loops that handle support, analysis, or content at machine scale.

Output:

Custom RAG • Multi-Agent Systems • MCP Infrastructure

03

FRACTIONAL CTO SERVICES

You don't know who to hire or how to spend

Hiring, Code Reviews & FinOps

You don't need a full-time CTO yet, but you need the seniority. We architect the roadmap, vet your first hires through technical interviews, and perform FinOps evaluations to slash your cloud bill. We stop you from hiring the wrong people.

Output:

Vetted Hires • Cloud Cost Reduction • Tech Strategy

TRUSTED STACK
Next.js
React
Node.js
Python
LangChain
MCP
Rust
Go
AWS
Gen AI
RAG
R&D
R&D ASSET

ML Applied to Dynamic Price Discrimination

A full thesis on machine learning models for real-time pricing optimization in SaaS. Not a blog post—peer-reviewed methodology applicable to your revenue stack. This is what 'R&D First' looks like.

PDF · Academic Research

Download Thesis (PDF)
CASE STUDIES

Proof of Execution

Names stay private. Outcomes don't.

Legal Tech

Boutique Law Firm

20 years of legal opinions unified into a searchable archive. Private semantic search with automatic clause alerts. The partner who used to dig through filing cabinets now queries in natural language.

20 years of documents indexed

90% reduction in research time

Zero data exposure

RAG PipelinePrivate LLMVector DB
AI Infrastructure

Early-Stage AI Startup

From zero to market-fit infrastructure in 4 months. We built the entire AI backend—MCP servers, RAG pipelines, agentic workflows—and the fullstack product layer. The founder focused on customers while we handled the silicon.

4 months to market-fit

Full AI backend

Production-ready at seed

MCP ProtocolMulti-Agent SystemNext.jsAWS

2 Projects Per Quarter

We deliberately limit capacity to guarantee engineering density. If we're a fit, we'll know in 30 minutes.

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