BrainForge 30-Day Learning Curriculum

For Go-to-Market, Sales, and Non-Technical Team Members

Last Updated: January 7, 2026
Created by: Luke’s 30-Day Challenge
Maintained by: GTM & Operations Teams

🎯 Purpose of This Document

This curriculum is designed to take someone from zero technical knowledge to confidently explaining BrainForge’s services in 30 days. Whether you’re new to the team, transitioning roles, or just curious about what we do — start here.

What You’ll Learn:

The basics of data and AI (starting from zero)
How BrainForge helps clients
Real client stories and case studies
How to talk about our services confidently

What You Won’t Learn:

How to code (that’s not the goal)
Deep technical implementation details
Every tool and technology (we focus on concepts)

Week 1: Data Fundamentals

Goal: Understand what data is, why it matters, and how it moves through systems.

Day 1-2: What is Data?

The Basics: Data is just information. When you make a purchase online, that’s data. When someone visits a website, that’s data. When a sensor measures temperature, that’s data.

Types of Data:

Structured Data — Organized in tables (like a spreadsheet). Example: Customer names, emails, purchase dates
Semi-structured Data — Has some organization but not rigid. Example: JSON files, XML
Unstructured Data — No fixed format. Example: Emails, images, videos, social media posts

Why Companies Care About Data:

Understand what customers want
Make better business decisions
Find problems before they get expensive
Automate repetitive work
Predict future trends

The Problem: Most companies have data scattered everywhere:

Sales data in Salesforce
Marketing data in HubSpot
Financial data in QuickBooks
Website data in Google Analytics
Support tickets in Zendesk

BrainForge’s Role: We help companies bring all this data together in one place so they can actually use it.

Day 3-4: Data Warehouses vs Data Lakes

What is a Data Warehouse? Think of it as a giant, organized filing cabinet in the cloud where all a company’s data gets stored in one place. The data is cleaned, structured, and ready to use for analysis and reporting.

Popular Data Warehouses:

Snowflake — Works year-round, not just in winter ❄️
Google BigQuery — Google’s warehouse
Amazon Redshift — Amazon’s warehouse
Azure Synapse — Microsoft’s warehouse

Key Features:

Structured and organized
Fast queries (you can ask questions and get answers quickly)
Built for analytics and reporting
Relatively expensive storage, but optimized for speed

What is a Data Lake? A data lake is like dumping everything into… well, a lake. You store raw, unprocessed data in its original format. It’s cheaper but messier.

Key Features:

Store everything (logs, files, raw data)
Cheaper storage
Data isn’t cleaned yet
Good for “we might need this someday”

What is a Data Lakehouse? A hybrid approach that combines the best of both:

Cheap storage like a lake
Organized structure like a warehouse
Not actually located at a lake (disappointingly)

BrainForge’s Approach: We typically recommend data warehouses (especially Snowflake) for most clients because they need clean, ready-to-use data — not a dumping ground.

Day 5-7: How Data Moves — Pipelines, ETL, and ELT

What is a Data Pipeline? A data pipeline is the system that moves data from where it’s created (like Shopify or Salesforce) to where it’s stored and analyzed (like Snowflake).

Think of it like plumbing — data flows through pipes, gets cleaned along the way, and ends up somewhere useful.

What is ETL? Extract, Transform, Load

Extract — Pull data from source systems (Shopify, Google Ads, etc.)
Transform — Clean it up, fix errors, reshape it
Load — Store it in the data warehouse

Example:

Extract customer orders from Shopify
Transform by removing test orders and standardizing zip codes
Load into Snowflake for reporting

What is ELT? Extract, Load, Transform

The order is different — you load the raw data first, then transform it inside the warehouse.

Extract — Pull data from sources
Load — Dump it into the warehouse (still messy)
Transform — Clean it up using tools inside the warehouse

Why ELT? Modern cloud warehouses (like Snowflake) are powerful enough to handle transformations themselves. It’s often faster and more flexible.

BrainForge’s Approach: We use ELT for most modern cloud projects. We extract data using tools like Fivetran or Polytomic, load it into Snowflake, then transform it using dbt.

Common Tools:

Extraction/Loading: Fivetran, Polytomic, Airbyte, Stitch
Transformation: dbt (more on this in Week 2)

Week 2: The Modern Data Stack

Goal: Understand the tools and technologies that power modern data operations.

Day 8-10: Snowflake & Cloud Data Warehouses

What is Snowflake? Snowflake is a cloud-based data warehouse — the most popular one we use at BrainForge. It’s where clients store all their cleaned, organized data.

Why Snowflake?

Separation of storage and compute — You can scale them independently (store tons of data cheaply, only pay for compute when running queries)
Fast — Uses Massively Parallel Processing (MPP) to run queries quickly
Easy to use — SQL-based, no need to manage servers
Secure — Built-in encryption, access controls, compliance features

Key Concepts:

Database — The top-level container for your data
Schema — A way to organize tables within a database
Tables — Where your actual data lives (rows and columns)
Warehouses — The compute resources that run queries (confusingly named, but it’s the “engine”)

When to Use Snowflake:

You have data from multiple sources
You need fast analytics
You’re growing and need to scale
You want to avoid managing servers

BrainForge’s Snowflake Work:

Set up Snowflake environments from scratch
Design database schemas
Optimize query performance
Build data models and pipelines

Day 11-13: dbt (Data Build Tool)

What is dbt? dbt is the tool we use to transform raw data into clean, usable data models inside the warehouse. Think of it as the construction crew that takes raw materials and builds something useful.

Why dbt?

Write transformations in SQL (the language of data)
Version control your data logic (like Git for code)
Test your data automatically
Document everything
Modular and reusable

How dbt Works:

Models — SQL files that define transformations
Tests — Check that data is correct (e.g., no nulls in required fields)
Documentation — Auto-generate docs that explain what each table does
Lineage — See how data flows from source to final output

Example:

-- models/staging/stg_customers.sql
select
    customer_id,
    lower(email) as email,  -- standardize emails
    created_at
from raw.shopify.customers
where deleted_at is null  -- remove deleted customers

dbt Layers:

Staging — Clean raw data, standardize column names
Intermediate — Join and combine data
Marts — Final business-ready tables (for dashboards and reports)

BrainForge’s dbt Work:

Build dbt projects from scratch
Organize models into logical layers
Write tests to ensure data quality
Create documentation
Train client teams to maintain dbt

Day 14-16: BI Tools & Dashboards

What is BI (Business Intelligence)? BI tools turn data into visualizations — charts, graphs, dashboards — so non-technical people can understand what’s happening in the business.

Popular BI Tools:

Omni — Modern, SQL-based (our preferred tool)
Looker — Google’s BI tool, powerful but complex
Tableau — Drag-and-drop, very visual
Power BI — Microsoft’s tool
Metabase — Open-source, simple

What Makes a Good Dashboard?

Answers a specific question — Don’t just show data, solve a problem
Easy to understand — Non-technical users should “get it” immediately
Up-to-date — Data refreshes automatically
Actionable — Users know what to do next

Common Dashboard Types:

Executive Dashboard — High-level metrics (revenue, profit, growth)
Sales Dashboard — Pipeline, deals, quotas
Marketing Dashboard — Campaign performance, leads, conversions
Product Dashboard — User engagement, feature adoption, retention

BrainForge’s BI Work:

Design mockups with stakeholders
Build dashboards in Omni, Looker, or Tableau
Connect to Snowflake or other warehouses
Train teams to use and maintain dashboards

Day 17-19: Product Analytics

What is Product Analytics? Product analytics is about understanding how users interact with your product — what features they use, where they drop off, what drives retention.

Popular Tools:

Mixpanel — Event-based analytics
Amplitude — Similar to Mixpanel
PostHog — Open-source alternative
GA4 (Google Analytics 4) — Google’s free tool

Key Concepts:

Events — User actions (clicked button, viewed page, completed purchase)
Funnels — Step-by-step journeys (Sign up → Activate → Subscribe)
Cohorts — Groups of users (users who signed up in January)
Retention — Do users come back?
Activation — Did users experience the “aha moment”?

Example Questions Product Analytics Answers:

What percentage of users complete onboarding?
Which features drive retention?
Where do users drop off in the checkout flow?
What’s the conversion rate from free to paid?

BrainForge’s Product Analytics Work:

Set up event tracking (what to measure)
Implement tracking in apps
Build funnels and cohort analyses
Create activation and retention dashboards
Train product teams to self-serve

Day 20-21: Data Quality & Testing

Why Data Quality Matters: Bad data = bad decisions. If the data in your dashboard is wrong, people make the wrong calls.

Common Data Quality Issues:

Missing values — Nulls where there shouldn’t be
Duplicates — Same record appears twice
Inconsistent formats — “New York” vs “NY” vs “new york”
Stale data — Data hasn’t updated in days
Wrong types — Text in a number field

How We Ensure Data Quality:

dbt Tests — Automated checks
- Not null (field must have a value)
- Unique (no duplicates)
- Relationships (foreign keys exist)
- Custom checks (revenue > 0)
Data Monitoring — Alerts when things break
- Pipelines fail
- Data volume drops
- Anomalies detected
Documentation — So people know what data means

BrainForge’s Quality Work:

Write dbt tests
Set up monitoring and alerts
Create data dictionaries
Audit existing data quality

Week 3: AI & Automation Explained

Goal: Understand what AI actually is, how it works, and how we use it.

Day 22-23: What is AI? (For Real)

The Hype vs Reality: AI is not magic. It’s not sentient. It’s pattern recognition at scale.

Types of AI:

Machine Learning (ML) — Computers learn patterns from data
- Example: Spam filters, recommendation engines
Large Language Models (LLMs) — AI trained on text to understand and generate language
- Examples: ChatGPT, Claude, GPT-4
Generative AI — AI that creates new content (text, images, code)
- Examples: ChatGPT writes text, DALL-E creates images

What LLMs Can Do:

Summarize text
Answer questions
Write content
Extract information
Classify and categorize
Translate languages

What LLMs Can’t Do:

Know things that aren’t in their training data (unless you tell them)
Do math reliably (without tools)
Access real-time information (without integrations)
Reason like humans (they’re pattern matchers)

BrainForge’s AI Philosophy: “Raise the Floor, Not the Ceiling” We don’t chase bleeding-edge research. We use AI that works today to solve real problems. We build practical, reliable solutions — not science experiments.

Day 24-25: RAG, Copilots, and AI Agents

What is RAG (Retrieval Augmented Generation)? RAG = AI that searches your documents first, then answers based on what it finds.

How RAG Works:

User asks a question
System searches company docs, wikis, policies
Relevant content is retrieved
LLM uses that content to answer the question

Example:

Without RAG: “I don’t know your company’s vacation policy”
With RAG: Searches HR docs → “You get 15 days PTO per year, accrued monthly”

BrainForge’s RAG Work:

Build internal knowledge hubs
Connect to Notion, Google Drive, Confluence
Make company knowledge searchable via AI chat

What is a Copilot? A copilot is AI that assists a human — the human is still in control, but AI speeds up the work.

Examples:

Analyst Copilot — Summarizes meetings, creates action items
Lead Researcher Copilot — Auto-researches accounts for sales teams
Customer Support Copilot — Suggests responses to support tickets

Key Principle: Human in the loop. AI suggests, human decides.

What is an AI Agent? An agent is AI that takes actions on its own (with guardrails).

Examples:

AI Intake Optimizer — Triages incoming requests and routes them
Lead Distribution Agent — Assigns leads to sales reps automatically
Ticket Automation Agent — Creates tickets from meetings

Key Principle: Guardrails and oversight. AI acts, but humans monitor.

Day 26-27: AI in Practice — Evals, Guardrails, Safety

The Problem with AI: LLMs can hallucinate (make things up), be biased, or give unsafe responses.

How We Make AI Reliable:

Evals (Evaluations) — Testing AI output quality
- Does it answer correctly?
- Is the tone appropriate?
- Does it follow instructions?
Guardrails — Rules that prevent bad behavior
- Block unsafe content
- Enforce company policies
- Prevent leaking sensitive data
Human Review — Spot-checking AI outputs

BrainForge’s AI Safety Work:

Build evaluation frameworks
Set up guardrails and policy layers
Create audit trails
Train clients on safe AI usage

Week 4: BrainForge in Practice

Goal: Understand how we engage with clients and deliver value.

Day 28: Who We Are & What We Do

Who is BrainForge? We’re a forward-deployed AI & data services team for mid-market growing companies ($10M+ revenue). We embed with client teams to solve hard, unsexy problems with data and AI.

What Makes Us Different:

Outcome-focused — We care about results, not just deliverables
Raise the floor, not the ceiling — We build working solutions, not science experiments
Human + AI — We pair consultants with AI tools for leverage
Speed & pragmatism — We deliver value in weeks, not quarters

Our Core Services:

Category	What We Do
Data Platform	Set up warehouses, pipelines, orchestration, and data foundations
Data Modeling	Build data models, marts, and semantic layers
Reporting & Insights	Build dashboards, reporting, and decision-support analytics
Product Analytics	Event tracking, funnels, retention analysis
AI	Copilots, agents, knowledge systems, and workflow automation
Training & Enablement	AI literacy, data literacy, tool training

Day 29: Client Stories & Case Studies

1. ABC Home & Commercial — AI CSR Agent

Problem: Call center overwhelmed, 3-6 month CSR training time
Solution: Built “Andi” — AI agent that handles customer inquiries
Result: Faster onboarding, reduced call center load, actual ROI

2. StackBlitz — Modern Analytics Stack

Problem: No centralized data, teams working in silos
Solution: Built Snowflake + dbt + Omni stack
Result: GTM team has clean, AI-ready analytics

3. Vita Coco — Real-Time Stock Monitoring

Problem: Couldn’t track inventory across 900+ Target stores
Solution: Real-time monitoring system
Result: Detect stockouts immediately, optimize supply chain

4. Amazon Dashboard — Segmented Sales Reporting

Problem: Sales data scattered, no unified view
Solution: Built segmented dashboard
Result: Adopted across marketing, finance, and ops

5. Ticket Automation — Meeting → Tickets

Problem: Manual ticket creation from meetings
Solution: AI agent listens to meetings and creates tickets
Result: Hours saved weekly

6. Lead Enrichment — Account Based Marketing

Problem: Completely manual ABM workflow
Solution: Automated enrichment and workflow
Result: Scaled ABM without adding headcount

Key Themes:

We solve real problems
We deliver measurable results
We make AI practical, not theoretical

Day 30: How We Work & Engage

Typical Engagement Flow:

Discovery (2-4 weeks)
- Understand the problem
- Audit current state
- Map data sources and workflows
- Identify quick wins
Build (4-8 weeks)
- Set up infrastructure
- Build pipelines and models
- Create dashboards or AI systems
- Test and iterate
Enablement (2-4 weeks)
- Train client teams
- Document everything
- Hand off ownership
- Set up maintenance plan

Our Delivery Principles:

Fast feedback loops — Show value in weeks, not months
Modular and iterative — Build in pieces, ship continuously
Knowledge transfer — Clients should be self-sufficient when we leave
Outcome-driven — We measure success by business impact, not hours billed

Commercial Models:

Diagnostic Sprint — 2-4 week assessment
Accelerator Package — Fixed-scope, fixed-timeline project
Retainer — Ongoing embedded support
Outcome-based — Tied to specific KPIs

Our Typical Client:

$10 M -$ 500M revenue
Digital transformation or operations lead
Has data but can’t use it effectively
Needs speed and pragmatism, not a 6-month roadmap

Glossary: Common Terms & Buzzwords

Term	What It Means
AI (Artificial Intelligence)	Computers that can learn and make decisions
LLM (Large Language Model)	AI trained on text (ChatGPT, Claude)
RAG (Retrieval Augmented Generation)	AI that searches docs before answering
Data Warehouse	Centralized storage for structured data (Snowflake, BigQuery)
Data Lake	Storage for raw, unprocessed data
Data Lakehouse	Hybrid of warehouse and lake
ETL	Extract, Transform, Load
ELT	Extract, Load, Transform
Data Pipeline	System that moves data from source to destination
dbt	Data build tool — transforms data in the warehouse
BI (Business Intelligence)	Tools for dashboards and reporting (Omni, Looker, Tableau)
Product Analytics	Understanding user behavior (Mixpanel, Amplitude)
GA4	Google Analytics 4 — website/app analytics
Snowflake	Popular cloud data warehouse
MPP (Massively Parallel Processing)	Running queries across many servers at once
Schema	Structure of a database (tables, columns, types)
SQL	Language for querying databases
API	Way for systems to talk to each other
Copilot	AI assistant (human in control)
Agent	AI that takes actions (with guardrails)
Guardrails	Rules to keep AI safe and reliable
Evals	Testing AI output quality
Stand-up	Quick daily team meeting (10-15 mins)
Edge Server	Server close to users for speed (not super relevant to us)
Client-Server	Architecture where clients request data from servers

Resources & Next Steps

Internal Resources

Notion Pages:

Services Overview
Brainforge Data Playbooks
Case Studies
Tool Comparisons

Files (brainforge-files/sales/):

brainforge_capabilities_deck.pdf — What we do and how we engage
deep_dive_on_ai_automation_services.pdf — Technical AI services
brainforge_general_services.pdf — Full service catalog
Case study PDFs in case_studies/ folder
Tool comparison PDFs in comparisons/ folder

External Resources

Data Fundamentals:

AI/LLM Fundamentals:

Product Analytics:

Who to Talk To

Data questions: Engineering team
AI questions: AI team
Client engagement: Uttam, Robert
Sales questions: Sales team
Specific project questions: Project leads

Next Steps After 30 Days

Suggested Activities:

Shadow a client call — See how we engage with prospects
Read 3 case studies — Understand our proven outcomes
Watch a dashboard demo — See our BI work in action
Sit in on a standup — Experience how engineering operates
Review an SOW — Understand how we scope projects

Deepen Your Knowledge:

Pick one service line and go deep
Learn the basics of SQL (not required, but helpful)
Get hands-on with Notion, Snowflake, or dbt (if curious)
Join a project kickoff or demo

Feedback & Maintenance

This curriculum is a living document. As BrainForge evolves, so should this guide.

How to Contribute:

Found something confusing? Flag it
Missing a topic? Add it
Better explanation? Update it
New case study? Include it

Maintained by: GTM & Operations

Last Updated: January 7, 2026

Welcome to BrainForge. Let’s make AI and data actually useful. 🚀

Brainforge Knowledge

Explorer

brainforge-30-day-curriculum

BrainForge 30-Day Learning Curriculum

🎯 Purpose of This Document

📚 Table of Contents

Week 1: Data Fundamentals

Day 1-2: What is Data?

Day 3-4: Data Warehouses vs Data Lakes

Day 5-7: How Data Moves — Pipelines, ETL, and ELT

Week 2: The Modern Data Stack

Day 8-10: Snowflake & Cloud Data Warehouses

Day 11-13: dbt (Data Build Tool)

Day 14-16: BI Tools & Dashboards

Day 17-19: Product Analytics

Day 20-21: Data Quality & Testing

Week 3: AI & Automation Explained

Day 22-23: What is AI? (For Real)

Day 24-25: RAG, Copilots, and AI Agents

Day 26-27: AI in Practice — Evals, Guardrails, Safety

Week 4: BrainForge in Practice

Day 28: Who We Are & What We Do

Day 29: Client Stories & Case Studies

Day 30: How We Work & Engage

Glossary: Common Terms & Buzzwords

Resources & Next Steps

Internal Resources

External Resources

Who to Talk To

Next Steps After 30 Days

Feedback & Maintenance

Graph View

Table of Contents