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.
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
Quality gates: No agent output reaches a client without human review. Code review, test validation, security scanning, and final sign-off at every phase boundary. Find out more about our approach in our blog.
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