Brainforge Knowledge

qualification-criteria

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Brainforge Qualification Criteria

Purpose: Define what makes a good lead for Brainforge Source: ICP Operating System (Notion) Last Updated: 2025-01-16

Ideal Customer Profile (ICP) - Executive Summary

Who We Exist For: We exist to be the AI-powered growth partner for data-driven companies as they scale: from building the first data foundation through scale-up execution and into mature market expansion. We serve operator-level leaders who already believe in data and AI, but need a partner who can move fast today without creating drag tomorrow.

What They’re Looking For: Our buyers are not looking for tools, dashboards, or headcount. They are looking for clarity, leverage, and outcomes—delivered at the speed their business actually operates.

Organizing Principle: We organize our ICP around company maturity, not industry or title. This allows us to partner long-term while staying precise about what matters now.

Growth Stages (ICP Segmentation)

Startups: Build it right without slowing down

Who they are:

  • Post Series A, or 30M ARR/GMV companies
  • Without a mature data team

What’s broken:

  • Scrappy data, low trust
  • Decisions made in spreadsheets

Why they hire us:

  • They need a clean, scalable foundation that works immediately
  • Won’t need to be rebuilt later

Our role:

  • Lay the minimum viable data foundation
  • Enables speed, trust, and future growth

Deal Size: 30K/month

Scale Ups: Turn data into growth leverage

Who they are:

  • 100M ARR/GMV companies
  • Scaling teams, channels, and products

What’s broken:

  • Growth slowing
  • Conflicting numbers
  • Analytics teams bottlenecked
  • AI tools underused

Why they hire us:

  • They need one view of the business
  • Clear answers on where to invest, fix, or stop

Our role:

  • Create a single source of truth
  • Surface growth constraints
  • Operationalize analytics across teams

Deal Size: 40K/month

Unicorn/Enterprise Growth: Scale intelligence while fighting bureaucracy

Who they are:

  • $500M+ ARR/GMV organizations
  • Modern stacks

What’s broken:

  • Complexity outpacing insight
  • Manual workflows killing speed
  • Decision-making not scaling

Why they hire us:

  • They need AI and automation embedded into operations
  • Not another layer of tools

Our role:

  • Design and deploy AI systems that scale decision-making
  • Preserve velocity while scaling

Deal Size: 100K+/month

Seasonal Champions: Activated by funding cycles, board deadlines, or operational stress

Who they are:

  • Companies at any stage
  • Activated by external pressure (funding, board, operational stress)

What’s broken:

  • Urgent need for data/AI solutions
  • Time-sensitive deadlines
  • Need fast execution

Why they hire us:

  • Speed to proof over endless roadmap discussions
  • Need immediate results

Our role:

  • Rapid deployment
  • Quick wins
  • Fast time to value

Deal Size: Variable (pilot-focused)

Company Characteristics

Revenue/Size Ranges:

  • Startups: 30M ARR/GMV
  • Scale Ups: 100M ARR/GMV
  • Enterprise: $500M+ ARR/GMV

Stage:

  • Post Series A minimum
  • Growth stage companies
  • Companies with operational pressure

Other Characteristics:

  • Data-driven companies
  • Operator-level leaders who believe in data and AI
  • Companies needing speed without creating future drag
  • Modern tech stacks (for enterprise)

Decision Maker Roles

Primary Decision Makers:

  • Operator-level leaders (founders, executives)
  • Leaders who already believe in data and AI
  • Decision makers who need speed and leverage

Key Characteristics:

  • Not looking for tools, dashboards, or headcount
  • Looking for clarity, leverage, and outcomes
  • Need partners who move at business speed

Pain Points (What Makes Them a Good Fit)

Pain Point 1: Scrappy Data, Low Trust (Startups)

  • Description: Decisions made in spreadsheets, no reliable data foundation
  • Signs they have it: Manual data processes, conflicting numbers, low trust in data
  • How Brainforge solves it: Lay minimum viable data foundation that enables speed and trust

Pain Point 2: Growth Constraints, Conflicting Numbers (Scale Ups)

  • Description: Growth slowing, analytics teams bottlenecked, AI tools underused
  • Signs they have it: Multiple data sources, conflicting metrics, analytics bottlenecks
  • How Brainforge solves it: Create single source of truth, surface growth constraints, operationalize analytics

Pain Point 3: Complexity Outpacing Insight (Enterprise)

  • Description: Manual workflows killing speed, decision-making not scaling
  • Signs they have it: Complex systems, slow decision-making, manual processes
  • How Brainforge solves it: Design and deploy AI systems that scale decision-making while preserving velocity

Pain Point 4: Urgent Need (Seasonal Champions)

  • Description: Funding cycles, board deadlines, operational stress creating urgency
  • Signs they have it: Time-sensitive deadlines, external pressure, need fast results
  • How Brainforge solves it: Speed to proof, rapid deployment, quick wins

Qualification Questions

BANT Framework (or Your Framework)

Budget:

  • Question: “What’s your budget range for this type of work?”
  • Signals:
    • Mid-Market: 30K/month range
    • Enterprise: 100K+/month range
    • Initial pilots or workshops acceptable
  • Red flags:
    • No formal data/AI budget
    • Seeking cheap execution
    • Budget too low for engagement type

Authority:

  • Question: “Who makes the final decision on this type of engagement?”
  • Signals:
    • Operator-level leaders (founders, executives)
    • Leaders who already believe in data and AI
    • Executive ownership (not delegated downward)
  • Red flags:
    • Delegating “AI” downward with no executive ownership
    • No decision maker access
    • Staff augmentation request (not strategic)

Need:

  • Question: “What’s broken with your current data/AI approach?”
  • Signals:
    • Clear operational pain (scrappy data, conflicting numbers, bottlenecks)
    • Growth constraints or complexity issues
    • Urgent need (funding cycles, board deadlines, operational stress)
    • Already believe in data and AI (not debating whether it matters)
  • Red flags:
    • Still debating whether data infrastructure matters
    • Research-only with no business outcomes
    • Optimizing vanity metrics without outcome ownership
    • No operational pressure

Timeline:

  • Question: “When do you need to see results?”
  • Signals:
    • Active project, ready to start
    • Urgent need (funding cycles, board deadlines)
    • Operational stress creating urgency
    • Speed to proof over endless roadmap discussions
  • Red flags:
    • No timeline or urgency
    • Slow procurement requirements (traditional enterprise)
    • Rigid scopes that prevent speed

Brainforge-Specific Qualification

Question 1: [Your question]

  • Good answer: [What you want to hear]
  • Red flag: [What to watch out for]

Question 2: [Your question]

  • Good answer: [What you want to hear]
  • Red flag: [What to watch out for]

Question 3: [Your question]

  • Good answer: [What you want to hear]
  • Red flag: [What to watch out for]

Red Flags (Disqualification Criteria)

We Explicitly Do NOT Serve:

Company Stage Red Flags:

  • Pre-revenue or early-stage teams without operational pressure
  • Companies with <25 employees
  • No formal data/AI budget

Buyer Intent Red Flags:

  • Buyers seeking staff augmentation disguised as strategy
  • Organizations still debating whether data infrastructure matters
  • Buyers seeking cheap execution rather than business leverage
  • Research-only initiatives with no tie to business outcomes
  • Research-only initiatives with no operational or financial accountability

Buyer Type Red Flags:

  • Traditional enterprise buyers who require slow procurement and rigid scopes
  • Organizations still deciding whether to adopt a data warehouse
  • Teams optimizing vanity metrics without ownership of outcomes
  • Founders or managers delegating “AI” downward with no executive ownership

Common Disqualification Reasons:

  1. Too early stage (pre-revenue, <25 employees) - Most common
  2. Wrong buyer intent (staff aug, cheap execution) - Very common
  3. No operational pressure or urgency - Common
  4. Still debating whether data infrastructure matters - Common

Qualification Scorecard

Score Each Lead (1-10):

Company Fit (ICP):

  • 10: Perfect match
  • 7-9: Good match
  • 4-6: Fair match
  • 1-3: Poor match

Decision Maker:

  • 10: C-level decision maker
  • 7-9: VP/Director with authority
  • 4-6: Manager/influencer
  • 1-3: Individual contributor only

Pain Point:

  • 10: Clear, urgent pain point
  • 7-9: Identified pain point
  • 4-6: Potential pain point
  • 1-3: No clear pain point

Budget:

  • 10: Budget confirmed
  • 7-9: Budget likely
  • 4-6: Budget unclear
  • 1-3: No budget

Timeline:

  • 10: Active project, ready to start
  • 7-9: Project planned in 3-6 months
  • 4-6: Project planned in 6-12 months
  • 1-3: No timeline

Total Score:

  • 40-50: Highly qualified - pursue aggressively
  • 30-39: Qualified - pursue with standard cadence
  • 20-29: Marginally qualified - low priority
  • 10-19: Not qualified - disqualify

Decision Tree

Is it a good company fit? (ICP)
├─ NO → Disqualify
└─ YES
    ├─ Is there a decision maker?
    │   ├─ NO → Disqualify
    │   └─ YES
    │       ├─ Do they have a pain point?
    │       │   ├─ NO → Disqualify
    │       │   └─ YES
    │       │       ├─ Do they have budget?
    │       │       │   ├─ NO → Disqualify
    │       │       │   └─ YES
    │       │       │       ├─ Is timing good?
    │       │       │       │   ├─ NO → Nurture for later
    │       │       │       │   └─ YES → QUALIFIED

Examples

Qualified Lead Example

Company: [Example company] Role: [Example role] Why Qualified:

  • [Reason 1]
  • [Reason 2]
  • [Reason 3]

Score: [X]/50

Disqualified Lead Example

Company: [Example company] Role: [Example role] Why Disqualified:

  • [Reason 1]
  • [Reason 2]

Red Flags:

  • [Flag 1]
  • [Flag 2]

Fill in your actual qualification criteria, then we’ll use it to train agents!

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