AI Enhanced Engineer

Company Vision & Service Offerings - aiee.io

Engineering precision, not slop.

aiee.io | leo@aiee.io | Montreal, QC, Canada

Who We Are

AI Enhanced Engineer (AIEE) is a Montreal-based engineering and product company. We build, fix, and operate Artificial Intelligence at scale — through a human-orchestrated, agent-built delivery model.

Founded by Leopoldo G Vargas, AIEE combines client services with proprietary product development. The same team and methodology that delivers client engagements also builds and operates AIEE's own production products (Bot Brewers, Breathspace), proving the engineering process works before applying it to client systems.

Founded: 2025 | Location: Montreal, Quebec, Canada | Website: aiee.io

Mission & Philosophy

Mission: We build, fix, and operate Artificial Intelligence at scale — with the rigor of a senior engineering team and the throughput of an entire department.

Brand promise: Engineering precision, not slop.

Philosophy: Engineer, not consultant. AIEE writes code, reads codebases, and finds bugs at the line-number level. Every claim is tied to specific files, scores, or findings. No hand-waving, no slide decks, no strategy documents disconnected from implementation.

Three Operating Principles

1

Evidence over opinion

Assessments produce scored rubrics (0-100 per dimension), not qualitative judgments. Findings cite file paths and line numbers. Recommendations include time estimates and severity levels.

2

Diagnose before prescribing

Every engagement starts with a comprehensive diagnostic. No remediation work begins until the system's health is understood. This protects the client from unnecessary work and protects AIEE from building on false assumptions.

3

Human-orchestrated, agent-built

95% of the code produced for clients is built by specialized AI agents under human direction. The founder orchestrates — setting strategy, making architectural decisions, reviewing all outputs, and approving every deliverable.

The Agent Delivery Model

AIEE's workforce is a team of many specialized AI agents, each engineered for a specific engineering domain. The founder operates as the orchestrator — the single human who directs, reviews, and approves all agent output. Here are some examples.

Human Engineer-Orchestrator

Strategy, architecture, client relations (Human)

Backend Engineer

FastAPI, PostgreSQL, DDD, async patterns

Data Engineer

PostgreSQL, MySQL, RLS, multi-tenant isolation

DevOps Engineer

Terraform, GitHub Actions, Cloud Run, Docker

Frontend Engineer

Angular, Svelte, Web Components, accessibility

Python Expert

Modern Python 3.12+, async, type hints, profiling

Security Engineer

OWASP, SOC 2, GDPR, penetration testing

Systems Architect

Microservices, DDD, event-driven, CQRS

Service Lifecycle

Every engagement enters through the diagnostic phase. The assessment report prescribes specific services based on evidence from the client's codebase.

Service 1: AI System Assessment

The entry point to every engagement. A comprehensive, engineering-grade diagnostic that audits the client's software system across multiple dimensions and scores each one.

What's Included

  • Production Readiness Audit — Multi-dimension scored assessment (0-100 per dimension). Overall score = minimum dimension score.
  • Scientific Rigor Audit — ML methodology validation: reproducibility, model quality, data pipeline integrity.
  • Root Cause Investigation — Hypothesis-driven diagnosis when something is broken.
  • Architecture & Integration Analysis — System topology mapping, service relationships, dependency analysis.
  • Remediation Roadmap — Prioritized action plan with time estimates, grouped by severity.
  • Executive Report — Professional PDF with GO/NO-GO deployment decision.

Differentiators

Scored rubric

Actual numbers out of 100

Code-level evidence

File paths and line numbers

Scientific rigor audit

ML methodology validation

Multi-application scope

Full system, not one app

Service 2: Remediation & Engineering

After the assessment identifies what needs fixing or building, AIEE offers four specialized service tracks:

AI Application Modernization

Upgrade outdated frameworks, harden security, modernize build pipelines. When your AI system works but runs on outdated stacks.

ML Model Recovery & Redeployment

Retrain, reconnect, and redeploy ML models that are broken, disconnected, or degraded. When your ML feature stopped working and nobody knows why.

AI Integration & Pipeline Engineering

Wire ML services together, add observability, build fallbacks and monitoring. When your ML model works in isolation but isn't connected to your product.

Custom AI Development

Build new AI-powered features from scratch — full-stack, from data pipeline to user interface. When you have a problem that AI can solve but no AI system yet.

Service 3: AI Operations & Managed Support

The ongoing relationship after remediation or development. After project work completes, AIEE stays on as an AI operations partner through a monthly retainer. This prevents regression when remediated systems are left unmanaged.

What's Included

  • Model monitoring and drift detection
  • Automated retraining pipeline management
  • Security patching and dependency updates
  • Performance optimization
  • SLA-backed incident response
  • Quarterly health check reports

Key Advantage

The same team that diagnosed and fixed the system continues to operate it, with full context and no knowledge transfer overhead.

Target Market

Primary: Mid-Market Companies with Production Software

  • Engineering team of 5-50 people
  • Production software systems — often including AI/ML features
  • Need engineering capability beyond what the current team can deliver
  • Budget for project-based remediation, not full-time ML hires
  • Urgency — something is broken or at risk

Secondary: Companies Building New AI Products

  • Have a product with an identified AI use case
  • Need full-stack AI development (data pipeline through UI)
  • Want production-grade systems, not prototypes

Industry Verticals

Oil & gas field services, B2B SaaS, Consumer mobile

Competitive Positioning

Scored production readiness rubric

Every dimension rated 0-100 with a minimum-score rule. Objective measurement, not subjective opinion.

Code-level evidence

Findings cite specific files, line numbers, and code paths. Actionable from day one.

Scientific rigor audit

ML methodology validated for reproducibility and data integrity.

Full-stack remediation

Mobile + web + API + ML + infrastructure in a single engagement. One vendor for the entire system.

Agent delivery model

Speed and volume of a large team with the consistency and context of a single engineer.

Products prove expertise

AIEE builds and operates its own production systems (Bot Brewers, Breathspace).

Open source credibility

Production-tested tools and templates published on GitHub. Six public repositories.

Engineering precision, not slop.
aiee.io | leo@aiee.io

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