About
The thesis.
Canonical is built around an observation: operational AI doesn't fail at the model layer. It fails underneath it, in the canonical models that don't exist, the entity resolution that was never done, the taxonomies that were documented but never implemented, and the retrieval systems that return plausible but wrong answers because the underlying representation is incoherent.
The hard part of operational AI is not the AI. It's the representation of the business itself. That's the work I do.
About me
I'm Fahad Baig. Fifteen years in data, data science, and machine learning. Most recently I founded and led the AI practice at Gibson Consulting, where I designed and shipped canonical data models, supplier consolidation pipelines, classification systems, agentic ETL workflows, semantic retrieval architectures, and MCP-based operational intelligence systems for industrials, manufacturers, distributors, and PE-backed portfolio companies.
I work hands-on. I write the code, design the schemas, build the pipelines, and ship the systems alongside your team. There is no junior implementation layer. There is me, occasionally working with named senior collaborators when a project's scope exceeds what one person can ship.
How I work
- Two or three retainers at a time.Enough to stay deep, few enough to stay senior.
- Three- to six-month engagements.Typically two days per week per client.
- Remote-first.With occasional onsite when the work requires it.
- Architecture Review as the entry point.Fixed-fee, two weeks, written deliverable.
- Senior-only.When scope demands more hands, I bring in named senior collaborators on the engagement. Never anonymous team labels.
Platforms
I default to Databricks. Most engagements ship on Lakehouse, Delta, Unity Catalog, and the workflow tooling around it. I also work on Snowflake, plain Postgres or DuckDB stacks, and whatever data infrastructure you already have.
Who I work with
You're likely a fit if your organization is:
- ·Operating in classification-heavy, data-fragmented, or regulated environments where generic AI patterns are starting to show their limits.
- ·Past the pilot phase but unsure why production reliability is harder than the demos suggested.
- ·Considering a larger AI investment and wants a senior, independent read before committing.
I work most often with industrial manufacturers and distributors, regulated industries, and PE-backed portfolio companies. These are places where fragmented master data, supplier and item sprawl, classification complexity, and retrieval quality are real operational concerns rather than future considerations.
If you're earlier than that (at “should we explore AI?” or “should we build an internal chatbot?”), I'm not the right fit, and I'll say so.