AI Engineering
AI Systems That
Actually Ship
Custom pipelines, intelligent automation, and production-grade AI infrastructure — engineered for businesses that need systems that work, not science projects.
What We Build
AI Engineering Services
AI Content Pipelines
Full-stack article generation from seed documents. Multi-LLM orchestration with Claude and GPT-4. 16-phase expansion engine with grounding validation and SEO optimization.
NLP & Text Analytics
Topic segmentation, discourse parsing, and semantic analysis. ILP-optimized sentence selection, entity coherence analysis, and macrostructure extraction.
YouTube & Media Processing
High-throughput transcript harvesting (500k/3h). Whisper ASR fallback, channel authority analysis with graph analytics, and competitive positioning reports.
Lead Generation Systems
6-stage pipeline from search to CRM export. Content gap detection, intent scoring, and contact resolution. Automated lead qualification with scoring algorithms.
Search & Retrieval
Hybrid retrieval systems combining BM25, SBERT embeddings, and FAISS vector search. K-means clustering for content categorization and MMR diversity selection.
Infrastructure & Tooling
MCP server development, pipeline monitoring with stall detection, proxy fleet simulation, and distributed processing with Ray. Production-grade error recovery.
Sample Work
Sample Projects
These are samples — not limits. Each project solved a specific problem; the engineering patterns transfer to whatever you're building.
Multi-Phase Pipeline Architecture
A 16-phase orchestration system with checkpoint recovery, auto-resume, and multi-LLM coordination. Built for content generation — applicable to any workflow requiring state management, fault tolerance, and auditability.
NLP & Semantic Analysis
Eight integrated algorithms for extracting meaning from unstructured text: TextTiling segmentation, RST discourse parsing, LDA/LSI topic modeling, and ILP-based selection optimization.
Distributed Systems & Scale
A Ray-powered extraction system with two-tier proxy scheduling, rate limiting, and automatic IP block recovery. Target: 500K items in 3 hours.
Analytics & Scoring Systems
Graph-based analytical pipelines using PageRank, HITS, and eigenvector centrality to quantify authority. Delivered 42.55% consolidation upside for a 100K+ subscriber channel.
Automated Lead Generation
A 6-stage pipeline: search → entity extraction → gap analysis → intent scoring → contact resolution → export. Built for B2B outreach automation.
Simulation & Numerical Analysis
Monte Carlo simulation systems modeling distributed infrastructure under load. Utilization penalties, token bucket rate limiting, Gini fairness coefficients, P95/P99 latency tracking.
Capabilities
Technical Depth
Multi-LLM Orchestration
Coordinated workflows across Claude and GPT-4 with fallback logic and model-specific optimization.
Advanced NLP
TextTiling segmentation, RST discourse parsing, and LDA/LSI topic modeling for semantic content analysis.
Hybrid Search & Retrieval
BM25 sparse + SBERT dense embeddings with FAISS indexing for production-scale similarity search.
Grounding Validation
Claim classification, entity consistency checking, fact-checking, and source registry management.
Voice Preservation
ADE/ERS algorithms for maintaining speaker authenticity, prosody analysis, and emotional rhythm simulation.
ILP Optimization
Integer linear programming for max-coverage sentence selection and MMR diversity-aware content curation.
Distributed Processing
Ray-powered parallel execution with two-tier proxy scheduling, rate limiting, and IP block recovery.
Production Resilience
Checkpoint recovery, stall detection, schema validation with retry logic, and manifest tracking.
About
Where AI Meets
Engineering Rigor
DLux builds the intelligent systems that power modern businesses. We focus on production-grade AI — systems that ship, scale, and deliver measurable results. No prototypes. No science projects. Just engineering that works.
Client Feedback
What Clients Say
These testimonials are representative examples based on documented project outcomes and typical client experiences. Names and company descriptions are illustrative.
Representative example
I had a backlog of long-form videos and no written assets. I expected the output to feel generic, so I held off. The system turned the backlog into 36+ publication-ready articles, and the 16-step workflow meant I wasn't chasing files or fixing formatting. It still sounded like my voice. If you want your content to exist beyond the video platform, this solved it.
Rachel Thornton
Creator & Host
Personal finance education channel
Austin, US
Representative example
We needed an automation pipeline we could ship into production, not a demo script. Mario delivered a 16-phase orchestrator with explicit JSON state, checkpoint recovery, and clean interfaces between phases. In our testing it handled failures predictably and resumed from the last good checkpoint instead of rerunning everything. Knowing he'd already put the pattern through 61 production runs gave us confidence, and our team integrated it into our stack with minimal rework.
David Chen
CTO
Developer tools startup
Vancouver, Canada
Representative example
Our content library had grown messy, and we didn't know what to consolidate. The authority audit surfaced a 42.55% consolidation upside and mapped which topics were actually carrying authority. The deliverable was clear, defensible, and easy to hand to writers and stakeholders.
Elena Varga
Director of Marketing
Mid-market software company
London, UK
Representative example
Lead qualification was the choke point in our client delivery. DLux built a 6-stage lead discovery and scoring pipeline that fit our intake process and produced 24 qualified leads we could action immediately. What mattered most was consistency: every lead came with the same fields, notes, and a score, so our team stopped debating what 'qualified' meant.
Sofia Morales
Agency Owner
Content and demand-gen agency
Mexico City, Mexico
Representative example
We brought Mario in to rebuild a brittle collector into something we could operate. He used Ray with async I/O, retries, and idempotent state so the job could recover cleanly. The design is explicitly sized for a 500K-item target and a 50 RPS tier, with backpressure and checkpoints instead of silent drops. He also handed over a MATLAB simulation suite (90+ files) that we used to sanity-check latency and fairness assumptions.
Arjun Nair
Senior Data Engineer
Payments and risk analytics company
Singapore
Representative example
I needed NLP capability I could resell without becoming an NLP researcher. Mario packaged a semantic analysis layer with a suite of 8 algorithms, plus clear inputs/outputs I could explain to clients. It let me deliver consistent audits without overpromising.
Tomas Novak
Independent Consultant
Compliance and policy advisory practice
Berlin, Germany
Let's Build
Ready to Ship
Your AI System?
Tell us about your pipeline, automation, or AI infrastructure needs. We'll scope the project and deliver a production-ready solution.