Consulting & Planning
Before building, you need clarity — what to build, how to deploy it, and what the security implications are. I help you figure that out.
Discovery & Scoping
Understanding your current state, goals, and constraints before recommending solutions.
Workflow Audit
I map your existing processes to identify where AI and automation can have the most impact — not just where it's technically possible, but where it actually makes business sense.
Requirements Gathering
Structured conversations with your team to understand data flows, integration points, security requirements, and success criteria.
Feasibility Assessment
Honest evaluation of what's realistic given your timeline, budget, and existing infrastructure. I'll tell you if something isn't worth building.
Scope Definition
Clear documentation of what will be built, what's out of scope, and what the phased roadmap looks like.
Architecture Design
Designing the technical architecture before writing any code.
System Architecture
How agents, pipelines, data stores, and frontends connect. Clear diagrams and specifications that your team can review and your developers can build from.
Integration Mapping
Which systems need to talk to each other, via what protocols (API, MCP, webhooks, message queues), and what data flows between them.
Model Selection
Choosing the right models for each task — frontier vs. open-source, general vs. fine-tuned, cloud vs. self-hosted. Based on your requirements, not hype.
Data Architecture
How your data is stored, indexed, and accessed by agents. Vector stores, knowledge bases, caching strategies, and retention policies.
Security Assessment
AI systems handle sensitive data and make autonomous decisions. Security isn't optional.
Data Exposure Analysis
Understanding what data flows through your AI systems — where it goes, who processes it, and what the exposure profile looks like across different deployment models.
Deployment Posture
SaaS, self-hosted, air-gapped, or hybrid — each has different security implications. I assess which posture matches your regulatory and operational requirements.
Access Control Design
Defining who and what can access your AI systems, how agents authenticate to external services, and how to implement least-privilege patterns.
Deployment Planning
Getting from prototype to production without surprises.
Infrastructure Requirements
Specifying compute, storage, networking, and GPU requirements for your deployment model. Avoiding over-provisioning while ensuring headroom for growth.
Migration Strategy
If you're replacing existing systems, I plan the cutover — parallel running, data migration, rollback procedures, and user transition.
Monitoring & Observability
Designing the instrumentation layer — what to measure, how to alert, and how to diagnose issues in production AI systems.
Cost Modelling
Projecting operational costs across different deployment options — API tokens, compute, storage, and third-party services. So you know what you're signing up for.
Technology Evaluation
Cutting through vendor claims to find what actually works for your use case.
Tool & Platform Assessment
Evaluating AI platforms, orchestration frameworks, vector databases, and deployment tools against your specific requirements — not industry benchmarks.
Build vs. Buy Analysis
For each component, I assess whether to build custom, use open-source, or buy SaaS. The answer is usually a mix — I help you find the right one.
Need clarity before you build?
Tell me about your situation and I'll outline how a consulting engagement could help.