AI & DATA SCIENCE CONSULTING
We Decode the Noise,
So You Get the Signal.
Enterprise AI, knowledge graph, and data science consulting for mid-to-large organizations building production-grade GenAI — not demos.
01 — SERVICES
Where We Create Signal
From first strategy conversation to production system, we work across the full stack of enterprise AI — not just the parts that make a good slide.
Knowledge Graph & Ontology Architecture
Design enterprise ontologies and knowledge graphs (Neo4j) that connect fragmented data into a single, AI-ready foundation.
RAG / GraphRAG / Hybrid RAG Systems
Build retrieval architectures that ground LLMs in your real enterprise data — not the open internet.
AI Strategy & Roadmapping
Translate ambition into a sequenced, fundable plan for GenAI investment, including build-vs-buy vendor evaluation.
Enterprise Data Governance & Architecture
Unify fragmented data across systems, APIs, and instruments into a connected, governed foundation.
Fractional AI / Data Science Leadership
Embed experienced technical leadership to build, mentor, and scale a high-performing data science team.
AI Vendor & Partnership Management
Evaluate, structure, and manage AI vendor relationships and statements of work so strategy and spend stay aligned.
02 — APPROACH
The Decode Approach
A straightforward, four-stage way of working — built for organizations that want real systems, real ROI, and a team that can own it after we're gone.
Listen & Diagnose
Audit the data landscape and separate real opportunity from AI hype.
Architect
Design the ontology, graph, or RAG architecture suited to your actual use case — not a generic template.
Build & Validate
Stand up a working system with measurable ROI, not just a proof of concept.
Scale & Transfer
Train your internal team and hand off for long-term ownership.
03 — RESULTS
Signal, Delivered
A sample of outcomes from enterprise engagements. Client details are kept confidential; the results speak for themselves.
Enterprise Knowledge Graph
Architected an enterprise knowledge graph and ontology for a Fortune 500 global manufacturer, linking product, chemical, and sensory data to power an AI platform attributed to nine-figure-plus incremental sales.
High-Performing AI Team
Built and led a data science team spanning dashboards to GraphRAG agents, with multiple team members promoted under direct mentorship.
AI Vendor Strategy
Owned multi-million-dollar AI vendor contracts, directing enterprise AI strategy and sign-off for a global R&D organization.
Data Unification at Scale
Led a first-of-its-kind data governance initiative, unifying siloed lab, API, and spreadsheet data into one AI-ready environment.
Predictive Cost Modeling
Delivered a predictive model that generated $500K in validated cost savings through reduced physical testing.
Analytics Automation at Scale
Automated enterprise reporting pipelines, saving 1,000+ analyst hours annually and unlocking millions in budget and agency efficiency.
04 — ABOUT
Built by a Practitioner, Not a Theorist
DecodeNoise.ai is led by Robert Brasso, an AI and data science leader with 10+ years of experience building enterprise knowledge graphs, GenAI/RAG architectures, and high-performing data science teams inside a Fortune 500 global manufacturing enterprise.
Robert holds an M.S. in Computer Science from Towson University and is based in Baltimore, MD. His work spans the full lifecycle of enterprise AI — from ontology design and knowledge graph architecture to AI strategy, vendor management, and building the teams that keep systems running long after the strategy deck is closed.
DecodeNoise.ai brings that same rigor to organizations that want AI systems built on real architecture — not hype.
Why DecodeNoise
- ✓ Enterprise scale experience, not prototypes
- ✓ Architecture and leadership in one person
- ✓ ROI-driven, not hype-driven
- ✓ Full lifecycle: strategy → build → team scaling
05 — CONTACT
Let's Decode Your Noise
Tell us about the problem you're trying to solve. We'll tell you, honestly, whether AI is the right answer — and what it would take to do it well.