Program
Data for AI Teams (GPTs & Gemini Gems)
A practical program for teams who want assistants trained on their own data — with clear governance, repeatable pipelines, and analysis loops that don't fall over in production.
We'll connect storage, embeddings, and models into a coherent, observable stack so your team can ask better questions of your data without reinventing the wheel every sprint.
Program overview · Audio
A short overview of the program: who it's for, what we cover, and how to get the most value out of it as a busy professional.
Outcomes for your team
Less “random RAG experiments,” more reliable assistants over real data.
- • Map your data sources and choose what should power AI assistants (and what shouldn't).
- • Design ingestion pipelines with clear ownership and refresh policies.
- • Build retrieval flows that survive real workloads, not just toy demos.
- • Wire assistants into Chat-SQL/Python loops for deeper analysis.
- • Ship dashboards and scheduled reports driven by the same stack.
Who this is for
- • Data, platform, or analytics teams
- • Product teams with internal copilots or ops assistants
- • Organizations with “data lakes” that aren't yet powering AI safely
Typical contexts
- • Support & CX analytics
- • Revenue and ops performance reporting
- • Compliance & audit-ready data views
Curriculum at a glance
Four modules taking you from messy sources to governed, queryable assistants.
Module 1
Data mapping & governance
- • Inventorying sources & identifying owners
- • PII handling, redaction, and access control
- • Deciding what data should power AI flows
Module 2
Ingestion & embeddings
- • Batch vs. streaming ingestion
- • Chunking strategies that fit your domain
- • Embeddings stores & index design
Module 3
Retrieval & assistants
- • Retriever patterns (search, filter, hybrid)
- • Connecting GPTs / Gems to your data safely
- • Guardrails for hallucination & stale data
Module 4
Analysis loops & reporting
- • Chat-SQL/Python workflows for analysts
- • Dashboards & scheduled reports from the same stack
- • Monitoring, costs, and capacity planning
Capstone: one assistant + one analytics loop
We focus on a single, high-leverage use case so your team leaves with something real.
Together we pick one assistant or analytics flow—support analytics, revenue reporting, internal research copilot, etc.—and build:
- • A minimal but correct ingestion pipeline
- • An embeddings / retrieval layer tuned to your data
- • A GPT / Gemini assistant or analysis notebook
- • A simple dashboard or scheduled report on top
How we evaluate success
- • Data refresh & access model is clearly defined
- • Retrieval quality is measured on real questions
- • At least one analytics loop is reproducible
- • The team can extend the stack without us
Format & logistics
Built for busy data and platform teams.
Schedule
- • 3–4 weeks total
- • Weekly live working sessions
- • Async implementation time between calls
Team size
- • 3–10 participants
- • Data, platform, and product folks together
- • Private cohorts for a single org
Deliverables
- • Data map & governance outline
- • Ingestion + embeddings configuration
- • Assistant / analysis loop + dashboard
FAQ: Data for AI Teams
Things teams usually ask before we start wiring data into assistants.
Do we need a data warehouse in place first?
A warehouse helps, but it's not required. We'll work from whatever you have today—warehouse, lake, or app databases—and recommend consolidation steps along the way.
How do you handle sensitive data and PII?
We design redaction and access-control up front, and we'll happily collaborate with security / compliance to document exactly what flows where.
Will this replace our existing BI stack?
No. The goal is to complement existing BI with assistants and analysis loops that sit closer to the work—not to rip out dashboards your org already depends on.
Want your data powering reliable AI assistants?
We partner with your data and platform teams to design the smallest stack that actually works—and leaves you able to expand without a rewrite.
We'll usually start with a quick inventory of your data stack and a shortlist of candidate use cases.