AI Consulting · Canada
I solve the business problem first.
Code is the last thing I write.
Most engineers build what they're asked. I find out why the business is actually broken — then architect the system that fixes the root cause. Three real clients. Three measurable outcomes. Zero slides without receipts.
The Framework
01
Diagnose Before Building
Discovery interviews with stakeholders. Map the real constraint. Find what's actually blocking the business — not what they think is blocking it.
02
Root Cause, Not Symptom
Apply 5-Why and constraint mapping. Define the structural failure. Agree success metrics before any code is written.
03
Architect the Fix
Design the simplest system that addresses the root cause. Select models, infra, and data architecture against the real constraint, not convention.
04
Measure & Report
Automated eval pipelines report agreed metrics weekly. Executive dashboards in plain language. Outcome validated against pre-agreed criteria.
Real Engagements
3 case studies — click any row to explore the full diagnosis & solution
Resso.ai
EdTech · AI Interview Platform · Canada (Remote)
HariKrushna Software Developers
Regulated Industry · Enterprise AI · Toronto, Canada
Corol.org × NunaFab
Civil Engineering Research · UHPC · Toronto, Canada
Overlapping Capabilities
Where business strategy meets engineering execution
Most consultants stop at the slide deck. Most engineers don't attend the client call. I do both — and that's where the value is created.
01
Business Analysis
- Stakeholder discovery & requirements scoping
- Root cause diagnosis (5-Why, constraint mapping)
- Success metric definition before any build starts
- Executive reporting & dashboard design
02
AI / ML Engineering
- LLM systems (GPT-4o, Claude, GGUF, fine-tuning)
- RAG architectures (HNSW, vector DB, hybrid search)
- Agentic pipelines & MCP integration servers
- Evaluation frameworks (hallucination, retention, latency)
03
Systems & Delivery
- Full-stack (Next.js, FastAPI, Go) + cloud (Azure, AWS)
- Data infra (PostgreSQL, Redis, Prisma, Kafka)
- CI/CD, Docker, Kubernetes, multi-env deployment
- Regulated industries: PIPEDA, data residency, on-prem
Value to a Consulting Firm
What I bring to Deloitte, McKinsey, Accenture
Rare Overlap
Business analyst who can ship production code
I've sat in the discovery interview and written the API that fixed the problem the same week. No handoff loss. No translation gap between strategy and engineering.
AI-Native
I don't bolt AI onto existing processes
I start from the business constraint and work backwards to decide if AI solves it — and which architecture. Three clients found that the market consensus was completely wrong for their actual constraint.
Regulated Industry
PIPEDA, data sovereignty, on-premise deployments
7 regulated-industry clients where the standard cloud-LLM playbook was ineligible. I know how to scope AI projects that pass procurement in finance, legal, and healthcare-adjacent environments.
Delivery Track Record
Shipped production systems as a solo engagement
Not demos. Sub-800ms real-time conversation engine. On-premise RAG with production traffic. ML model used daily by 12 researchers. All of these are live right now.
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