Consulting & Artificial Intelligence

AI Architecture · Governance · Agentic Automation · Education

Who I am in one paragraph

Twenty‑one years in software, a Ph.D. in machine learning, and production models now running in millions of consumer devices. Former Head of AI at HTEC, organizing AI infrastructure for 2000+ people; today an independent consultant. I design AI ecosystems, audit half‑built ones, steer teams, and craft curricula that stick—from engineers in the IDE to executives in the boardroom. Implementation is your team’s job; ensuring the blueprint is correct (and teachable) is mine.

Selected impact

  • Retail‑giant agentic architecture – Agent swarm orchestrating workflows across four countries; compliance layer baked in, models hot‑swappable; guidelines, policy, protocols, monitoring and control, education all in a single architecture to support a multi-billion-dollar international company.
  • 2000‑engineer up‑skilling – End‑to‑end AI education program: LMS curation, automated exam bots, mentor train‑the‑trainer pipeline.
  • RFP accelerator – NLP pipeline that drafts first‑pass responses; 70% time and cost cut for a global pump‑and‑compressor manufacturer.
  • Super‑resolution firmware – CNN inference on embedded DSP; now shipping in millions of consumer devices (Fortune‑50 NDA).

Check my home page for an exhaustive list.

What I deliver

Strategy
solution analysis

Screening (2-5 d) → discovery (2-8 w) → current-vs-future roadmap. You get a crystal-clear “why, what & when” before any build starts.

Architecture
platform design

Vendor-neutral blueprints, IaC (Terraform / Helm), CI/CD(ML) and observability—cloud, on-prem or hybrid. Delivered with diagrams, risk register and cost model.

Governance
compliance & risk

Gap analysis against GDPR, HIPAA, PCI-DSS and local regs. Outputs: maturity scorecard, policy pack, phased remediation plan and budget envelope.

Agentic
automation

Multi-agent systems that read policies, draft code, triage tickets and self-critique. Swap models at will—architecture isolates checkpoints from workflow logic.

Education
enablement

Hands-on workshops, exec briefings, LMS curricula, automated exam bots and mentor-the-mentor pipelines—proven across 2 000 engineers.

Bespoke
advisory

Pick-and-mix sessions—architecture reviews, bias audits, agent prototypes, exec coaching—tailored to thorny, one-off constraints. Ad-hoc or retainer.

Engagement models

ModePhases & durationTypical deliverables
Full‑solution audit & redesign Screening 2‑5 d → Discovery 2‑8 w → Architecture & Governance → Report & roadmap Diagrams, policy pack, SRS, cost model, implementation spec
Targeted consulting Screening 2‑5 d → Bespoke sessions (on‑site / remote)
Sprint‑priced, milestone‑invoiced		 Meeting notes, risk list, action items, optional code snippet
Education track Screening 2‑5 d → Curriculum design → Delivery (workshops, LMS, mentoring) Course materials, recorded sessions, progress analytics

Pricing —milestone or time‑and‑materials once KPIs are locked; flexible thereafter. Ongoing support can be retained under a separate SLA.

Toolchain & stacks

Languages & core libs: Python · C# · C++ · Rust · PyTorch · TensorFlow · JAX · Triton

LLM & agent frameworks: LangChain · LlamaIndex · AutoGen · CrewAI · Semantic Kernel · Ray Serve · custom chains

Models & services: GPT-4o / o-series · Claude 4 Sonnet / Opus · Gemini 2.5 Pro · Mistral Large · Phi-3 · DALL·E 3

DevOps & observability: MLflow · Weights & Biases · Airflow · Kubernetes · Helm · Terraform · Docker · Grafana · Prometheus

Cloud & vector infra: Azure · AWS · GCP / Vertex AI · Hugging Face Hub · Pinecone · Weaviate · Qdrant · Chroma

Coding AI & assistants: ChatGPT · Claude.ai · GitHub Copilot / Copilot Chat · Code Interpreter · NotebookLM

Custom-built AI toolchains and assistants and whatever tomorrow brings.

Why me, not a big consultancy?

  • You speak with the principal, not a pyramid of juniors.
  • Two decades hands‑on—I see the failure modes coming.
  • Original method and approach documented in Engineering Intelligence (100‑page deep dive into an integrated system for developing hybrid intelligence) and a shorter summary essay Epiphanies on AI & Cognition.
  • Education pedigree: university lecturing, 2 000‑engineer enablement at scale, dozens of conference talks
  • NDA‑friendly; security‑clearance ready.

Frequently‑asked questions

See “Selected impact” above. Each item includes the KPI: 70 % RFP cost reduction, 30 % scrap reduction on a PCB line, multi‑country workflow orchestration, etc. Where NDAs allow, I share redacted case studies on request.
Deepest trenches: software engineering itself, medical imaging and diagnostics, education, creative tooling (writing, music, design), gaming/graphics, and pockets of finance and legal. The common thread is complex data and strict constraints—exactly where AI shines when treated correctly.
First week: audit data flows, retention, and locality. Then map to the relevant frameworks—GDPR, HIPAA, PCI‑DSS, or regional law. Outputs include a compliance gap matrix and a policy pack your legal team can review before engineering bakes guard‑rails into the pipeline.
Brownfield is the norm. I wrap legacy components where ROI favors preservation and replace only when the long‑term cost of keeping them exceeds the rebuild.
Screening in 2‑3 days confirms fit. Discovery runs 2‑8 weeks depending on scope. Architecture & governance follow in parallel sprints. A production‑grade blueprint is usually handed off inside three months; delivery by your team continues under my shadow‑support if retained.
Milestone billing tied to deliverables, or time‑and‑materials when scope is fluid. Success‑fee options for well‑defined KPIs. We pick the model during scope lock.
Yes—retainer‑style SLAs for architecture guardianship, model re‑training, or rapid incident response.
Embedded workshops, mentor‑the‑mentor programs, recorded sessions, curated LMS playlists, and automated exam bots that track mastery. The goal is obsolescence—mine.
Depends on the shop. MLflow + Airflow on Kubernetes covers 80 %. Hugging Face Hub with Ray Serve is my default for LLMs. If you are deep‑Azure, I align with AML; GCP gets Vertex; AWS can stay native or go self‑hosted.
Start with an audit: task, data, latency, and privacy. Often a Llama‑family model with RAG beats fine‑tuning. When tuning makes sense, low‑rank adapters keep costs in check; full‑stack fine‑tune is reserved for edge or IP‑sensitive deployments.

Bias audits, anonymization, synthetic counter‑samples, anti‑bias agents, rule‑based post‑filters, and—crucially—human reviewers on the critical path. Techniques are documented in the Engineering Intelligence playbook.

Yes. I sign yours; you sign mine. Clearance paperwork on request.
Everything delivered: diagrams, risk registers, policy packs, code snippets, and the final report. Source files, not PDFs, unless we agree otherwise.
Reverse‑engineering if docs are missing, protocol adapters if they exist, or a strangler facade that lets new services grow while old ones fade.
Under NDA I can share anonymized diagrams and metrics. Named references are available once mutual NDAs clear legal.
Primarily solo. For large scopes I bring trusted subcontractors—former colleagues with years of joint delivery.
Simulation harnesses with synthetic tasks, behavior diffing against a golden set, guard‑rail unit tests, and staged roll‑outs with circuit breakers.
Plain‑language workshops, boardroom demos, scenario drills, and a self‑paced primer that fits into a lunch break.
Worst‑/best‑case token simulations during architecture, followed by real telemetry once in staging. Alerts fire when drift exceeds the agreed envelope.

A cross‑disciplinary lens—music theory to mechanics—plus two decades of shipping code. The result is systems that work and teams that understand why. My approach is broader than just AI—it is integrative hybrid intelligence.

Let’s build something great.

Ready to start the conversation? Get in touch to schedule a discovery call.

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