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The Intelligence Layer

Six AI Engines for GTM Discovery

From first hypothesis to sales-ready playbook. Arnen’s GTM discovery tools compress 18–24 months of go-to-market research into 4–6 weeks — so you can price, position, and sell with confidence.

Arnen Engine StatusBOOTING
01
WATERFALL02
VOICE03
RANGE04
SHIELD05
LENS06
ARENA
All Engines Online
Book DemoExplore Engines
Platform Capabilities

The Discovery Engines

Each engine solves a distinct GTM discovery challenge. Together, they build the complete go-to-market picture.

THE WATERFALL

Analogical Reasoning Engine

Find your price when no benchmark exists

5-layer waterfall cascades through 10,000+ funded companies to find structural analogs — similar solutions, adjacent markets, same buyers, ROI profiles, and economic fundamentals.

01
92%
02
78%
03
64%
04
51%
05
38%
Synthesized ACV$85k–$140k
Confidence
87%
10,000+companies analyzed
Key Outputs
  • 01Synthesized ACV range with confidence
  • 023-5 analog mappings per layer
  • 03Layer-by-layer audit trail
  • 04Sensitivity analysis
Deep Dive→
THE VOICE

Narrative Generator

20+ ways to say what you do

Category names, hero stories, analogy frames, and analyst definitions. Fire/Drop/Refine the ones that stick.

CATEGORY

"The GTM Discovery Platform"

ANALOGY

"It's like Datadog for go-to-market"

VALUE

"10x faster GTM strategy"

ANALYST

"Competitive Intelligence SaaS"

FIRE / DROP / REFINE
20+variants per cycle
Key Outputs
  • 015 category name candidates
  • 023 hero story arcs
  • 034 analogy frames
  • 044 analyst definitions
Deep Dive→
THE RANGE

Pricing Model

Defensible numbers, not guesses

Monte Carlo simulation meets Bayesian updating. Your price range learns from every deal outcome.

$20k$300k
RECOMMENDED
$85k–$140k
10,000 simulations
15%ACV prediction accuracy
Key Outputs
  • 01Initial Anchor WTP
  • 02Expected Discount Curve
  • 03ACV floor and ceiling
  • 04Unit economics model
Deep Dive→
THE SHIELD

Objection Forecaster

30 objections mapped before your first call

Multi-source intelligence predicts technical, procurement, and budget objections — with counter-arguments ready.

Technical
10
Budget
10
Procurement
10
Competitive
4
Total Mapped34
80%+objection coverage
Key Outputs
  • 0110 technical objections
  • 0210 procurement objections
  • 0310 budget objections
  • 04Severity scoring
Deep Dive→
THE LENS

Analyst Intelligence

See your Gartner classification early

Predicts how analysts will categorize you. Provides briefing materials and positioning guidance before publication.

LEADERSNICHECHALL.VISION.
YOUCOMPLETENESS →ABILITY →
3analyst firms covered
Key Outputs
  • 01Classification prediction
  • 02Coverage mapping
  • 03Briefing materials
  • 04Category fit assessment
Deep Dive→
THE ARENA

Call Simulator

Practice against AI buyers who fight back

5 buyer personas push back like real procurement. Performance scoring and coaching after every simulated call.

SKEPTICAL ARCHITECTWhat's your integration timeline?
Under 2 weeks for standard deploys...
RISK CISOOur CISO requires SOC2 before eval.
Real-time coaching active
5buyer personas
Key Outputs
  • 01Performance scoring
  • 02Coaching feedback
  • 03Emotional tracking
  • 04Voice mode
Deep Dive→
Deep Dive

Inside Each Engine

Six purpose-built GTM discovery tools, each solving a specific challenge that category-creating founders face. Here’s how they work.

01
THE WATERFALLAnalogical Reasoning Engine

Find your price when no benchmark exists

Your product is novel. That doesn't mean your market is. The Analogical Reasoning Engine cascades through five layers of increasingly lateral market intelligence to find structural analogs — companies that share your buyer profile, value mechanics, or economic model even if they're in completely different categories.

Starting with direct competitors (rare for novel products) and expanding through adjacent markets, same-buyer matches, ROI profiles, and economic fundamentals, the Waterfall synthesizes a defensible ACV range with confidence intervals — not a number pulled from thin air.

Key Outputs
  • 01Synthesized ACV range with confidence score
  • 023–5 analogous market mappings per layer
  • 03Layer-by-layer audit trail for board discussions
  • 04Sensitivity analysis showing which inputs matter most
10,000+funded companies analyzed per discovery
ENGINE 01: THE WATERFALLOPERATIONAL_
01 Similar Solutions92%
Direct product analogs
02 Adjacent Markets78%
Same problem, different approach
03 Same Buyer Persona64%
Same decision-maker
04 Similar ROI Profiles51%
Matching payback mechanics
05 Cost Avoidance38%
Pure economic fallback
Synthesized ACV$85k–$140k
02
THE VOICENarrative Generator

Twenty ways to explain what you do

Category creators have a messaging problem. You understand the technology deeply, but translating that into a story that resonates with buyers, analysts, and investors requires language you haven't discovered yet.

The Narrative Generator produces 20+ positioning variants across five distinct types — category names, hero stories, analogy frames, value propositions, and analyst definitions. Use the Fire/Drop/Refine workflow to triage at scale and converge on the narratives that stick.

Key Outputs
  • 015 category name candidates
  • 023 hero story arcs (Problem → Solution → Transformation)
  • 034 analogy frames ('It's like X for Y')
  • 044 value propositions with quantified impact
  • 054 analyst definitions (Gartner/Forrester-ready)
20+positioning variants per discovery cycle
ENGINE 02: THE VOICEOPERATIONAL_
CATEGORY NAMEThe GTM Discovery Platform
fire
HERO STORYFrom zero market data to board-ready strategy
fire
ANALOGY FRAMEIt's like Datadog for go-to-market
refine
VALUE PROP10x faster GTM strategy at 1/10th the cost
fire
ANALYST DEFAI-powered competitive intelligence SaaS
drop
03
THE RANGEPricing Model

Defensible numbers, not guesswork

Most early-stage pricing is a founder's gut feeling. The Pricing Model replaces instinct with simulation — running 10,000 Monte Carlo scenarios against analogous market data to produce ACV ranges with real confidence intervals.

As deals close, Bayesian updating refines your priors. Every win and loss makes the next prediction more accurate. The model also calculates unit economics for different sales motions — so you know what pricing supports direct sales vs. PLG vs. channel.

Key Outputs
  • 01Initial Anchor Willingness-to-Pay (IAWTP)
  • 02Expected Discount Curve by deal stage
  • 03ACV floor and ceiling with confidence bands
  • 04Unit economics model (margin, CAC, LTV:CAC)
  • 05Procurement Negotiation Trajectory
15%— customers close within this range of predicted ACV
ENGINE 03: THE RANGEOPERATIONAL_
$20k$300k
IAWTP$112k
ACV Floor$68k
Discount Curve12-18%
Payback14 mo
Gross Margin82%
LTV:CAC4.2x
04
THE SHIELDObjection Forecaster

Know the 30 objections before your first call

Every sales call is a minefield of objections you didn't anticipate. The Objection Forecaster uses multi-source intelligence — predicted objections from market analysis, extracted patterns from call transcripts, and crowdsourced insights from the Arnen network — to map the entire objection landscape before you pick up the phone.

Each objection comes with severity scoring, frequency prediction, and a counter-argument framework. Technical, procurement, budget, and competitive objections are all covered — so your sales team walks in prepared, not surprised.

Key Outputs
  • 0110 technical objections with counter-arguments
  • 0210 procurement objections with compliance responses
  • 0310 budget objections with ROI frameworks
  • 04Competitive objection analysis
  • 05Severity and frequency scoring per objection
80%+of predicted objections occur in actual calls
ENGINE 04: THE SHIELDOPERATIONAL_
HIGHTechnical

“What if the integration breaks our existing workflow?”

HIGHProcurement

“We need SOC2 compliance before procurement can sign off”

MEDBudget

“How does this compare to building in-house?”

MEDCompetitive

“Your competitor offers this at half the price”

05
THE LENSAnalyst Intelligence

See your Gartner classification before they publish

Being miscategorized by an industry analyst can gate your enterprise adoption for years. The Analyst Intelligence Engine predicts how Gartner, Forrester, and IDC will classify your product — before the Magic Quadrant or Wave is published.

More than prediction: it generates briefing materials, terminology mapping, and positioning adjustments that help you proactively shape how analysts perceive your category. This is the difference between landing in the right quadrant and being lumped into the wrong one.

Key Outputs
  • 01Classification prediction per analyst firm
  • 02Existing coverage mapping (relevant MQs, Waves)
  • 03Analyst briefing materials and talking points
  • 04Terminology and positioning guidance
  • 05Category fit assessment (too narrow / appropriate / too broad)
3major analyst firms covered (Gartner, Forrester, IDC)
ENGINE 05: THE LENSOPERATIONAL_
LEADERSNICHE
YOUR PRODUCTCOMPLETENESS →
GartnerPREDICTED
ForresterPREDICTED
IDCPREDICTED
06
THE ARENACall Simulator

Practice against AI buyers who fight back

Five distinct buyer personas — Skeptical Architect, Budget VP, Risk CISO, Innovation CTO, and Process Procurement — simulate the full range of enterprise sales conversations. Each persona maintains consistent character traits, raises objections at realistic intervals, and tracks emotional state throughout.

After each simulated call, you receive multi-dimensional performance scoring and actionable coaching. It's the difference between preparing with slides and preparing with resistance. Voice mode available for realistic practice.

Key Outputs
  • 01Multi-dimensional performance scoring
  • 02Actionable coaching feedback per call
  • 03Emotional state tracking throughout conversation
  • 04Objection handling assessment
  • 05Voice mode for realistic practice
5buyer personas with real-time coaching
ENGINE 06: THE ARENAOPERATIONAL_
PERSONA: Skeptical Architect · DIFFICULTY: Hard
Walk me through your architecture. How do you handle multi-tenant data isolation?
We use row-level security with per-tenant encryption keys. Each org's data is logically and cryptographically isolated...
That's standard. What about at the compute layer? Can one tenant's workload impact another's performance?
COACHING: Buyer is testing depth. Pivot to specific SLA guarantees.
Discovery Pipeline

How the Engines Connect

From a 30-minute intake to a complete GTM playbook. Each engine feeds into the next, building intelligence layer by layer.

IN
Intake30 min
01
Waterfall2-4 hrs
02
NarrativeAuto
03
PricingAuto
04
ObjectionsAuto
05
AnalystAuto
06
Simulator1-2 hrs
OUT
Playbook4-6 wks
Total Discovery Cycle4–6 weeksvs. 18–24 months traditional
READY TO DISCOVER?

Stop Guessing.
Start Discovering.

First hypothesis delivered in 48 hours. Full GTM playbook in 4–6 weeks. No credit card required.

$140kavg first deal
15%ACV accuracy
4-6 wksto playbook
Book DemoRead the Framework

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