- Specialized services
The Work, What I Build and How I Help
Most AI consultants sell strategy decks. Most agencies sell retainers. I do neither. Every engagement starts with a specific problem and ends with a working outcome — a deployed system, a ranked content architecture, a trained team, or a clear decision framework. The work is precise, accountable, and built around your data.
Specialist and consulting services
- Main Service
AI & ML Consulting
Local & Private LLM Creation & Deployment
AI Search Systems for Business
Custom NLP, Agents & Models
Prompt Engineering
Custom AI Solutions
SEO & AIO Strategy
Data Analytics & Decision Intelligence
Three engagements
No retainers for their own sake. No 90-day strategy phases before anything gets built. Each one produces something you keep.
01_ Diagnosis
I examine what you have built, where it breaks, and whether agents are the right answer at all. Sometimes they are not, and you should hear that before you spend a quarter finding out.
You get: a written architecture assessment covering your failure surface, tool and data readiness, a red-team report against your existing agents, and a build recommendation with sequencing and effort estimates.
Most teams start here.
02_ Agent Harness Build
I architect and ship the system. Orchestration and control loop. Tool layer and MCP server design. Skills library with scoped capability. Context and memory strategy under real token pressure. Evaluation harness. Guardrails and permission boundaries. Observability, tracing, and cost control. Deployed to your infrastructure or your cloud, depending on where your data is allowed to live.
You get: a running system, and the evaluation harness your team uses to extend it without me.
03_ Embedded Agent Operations
Agents are not a project. They are infrastructure that degrades.
Ongoing evaluation and regression testing as models change underneath you. New skills and tools as your scope grows. Quarterly red-teaming. Team enablement, so your engineers own the system rather than depend on the person who built it.
Every frontier model release silently breaks something in a production agent. Most teams find out from a customer.
How engagements work
Remote, worldwide
Your data stays where you need it
Model-agnostic by default
Scoped before it starts
One named owner on your side
What a harness actually contains
— WHAT AN AI CONSULTANT ACTUALLY DOES
The role most organizations misunderstand.
An AI consultant is not a software vendor. Not a data scientist for hire. Not someone who configures off-the-shelf tools and calls it transformation. The role is a bridge — between where your business is today and what it could do with the right intelligence infrastructure in place. That means understanding your operations well enough to identify where AI creates real leverage. Then designing the path from current state to working system, without the detours that cost most organizations 12 months and a failed pilot.
Here’s what that work actually covers.
01
Strategy & Discovery
Before any model gets trained or tool gets deployed, the most important question has to be answered: where does AI actually move the needle for this specific business?
That’s not a technical question. It’s a strategic one.
This phase covers an honest AI readiness assessment — evaluating your data infrastructure, technical capacity, and organizational culture. It includes a workflow audit to surface high-impact use cases, and the development of a phased implementation roadmap aligned to your KPIs.
Most organizations skip this. That’s why most AI projects stall at the pilot stage.
02
Data Strategy & Architecture
AI is only as good as the data behind it. That’s not a disclaimer — it’s the central constraint.
Before building anything, the data environment has to be understood and prepared. That means auditing existing data quality, engineering the pipelines that give models reliable access to accurate information, and making the right infrastructure decisions — cloud versus on-premise, platform selection, storage architecture.
Skipping this phase produces models that perform well in demos and fail in production.
03
Solution Development & Integration
This is where strategy becomes working technology.
Custom model training and fine-tuning. NLP pipelines built for your domain. Generative AI agents that handle autonomous tasks — document processing, customer interactions, internal knowledge retrieval. Proof of concepts developed fast enough to validate the approach before full-scale investment.
And when the right tool already exists — Microsoft Copilot, Salesforce Agentforce, or a vertical-specific platform — integration guidance that ensures it actually fits your workflow rather than sitting unused after onboarding.
04
Governance, Risk & Compliance
AI introduces risk categories most legal and compliance teams aren’t yet equipped to assess.
Regulatory exposure under frameworks like the EU AI Act and GDPR. Algorithmic bias embedded in training data. Model drift that quietly degrades performance over time. Data leakage vulnerabilities in systems handling sensitive information.
Responsible AI deployment requires guardrails built in — not bolted on after an incident.
05
Implementation, Enablement & Ongoing Optimization
Deployment is not adoption. The gap between a working system and a used system is where most AI investments fail to deliver ROI.
This phase covers change management — helping teams transition to AI-enabled workflows without resistance. Role-based training that makes new tools usable rather than intimidating. And post-launch monitoring that catches model drift, retrains on new data, and ensures performance holds as the business evolves.
The goal is a system your team owns. Not one they depend on a consultant to maintain indefinitely.