By turning raw signals and account context into structured review workflows.
Proof of work · AI GTM · Revenue systems
AI-enabled GTM systems that turn scattered market signals into qualified pipeline, sharper outreach and better sales decisions.
These prototypes show the full commercial chain: find the right accounts, qualify faster, follow up properly, manage lead and deal movement, diagnose GTM friction and improve decision quality.
KS
Karina Ströbl
GTM strategist and AI systems designer based in Amsterdam. I help B2B SaaS companies across EMEA replace fragmented revenue workflows with connected, AI-native systems — from signal discovery through to pipeline and decision quality. I've spoken on AI-led GTM at industry events and work hands-on with commercial teams building outbound and CS functions from the ground up.
Before and after
The workflow shift.
Before
- Signals in spreadsheets
- Manual account research
- Generic outreach
- Slow event follow-up
- Messy CRM updates
- Unclear pipeline quality
→
After
- Signal review
- Account prioritisation
- Lead enrichment
- Outreach-ready queue
- Sequences
- Analytics
- Deal intelligence
Solution map
Six connected systems.
Click any solution to jump to the full case study, benefits, contribution and screenshots.
01
AI-enabled GTM Engine
Turns market signals into reviewed accounts, enriched leads, outreach queues and pipeline visibility.
02 AI Revenue Intelligence LabReviews outbound, discovery summaries and deals so AI output becomes commercially useful and safer to use.
03 GTM Workflow DiagnosticsDiagnoses GTM workflows, finds bottlenecks and turns current-state pain into a future-state change plan.
04 Event ScoutManages event discovery, event fit, exhibitor leads, post-event tasks, competitors and outcomes.
05 Lead ManagerTracks lead ownership, stage movement, deal conversion, team performance and pipeline hygiene.
06 GTM Decision IntelligenceMakes the reasoning path behind an account recommendation visible, challengeable and safer to act on.
Benefits
Designed impact.
Prototype value, stated honestly.
By ranking accounts based on fit, urgency, evidence and commercial motion.
By reviewing messages for weak specificity, unsupported claims and template language.
By exposing deal risk, missing information, MEDDPICC gaps and next actions.
By connecting events to lead scoring, tasks, outcomes and pipeline visibility.
Responsible AI GTM
Human review stays in the loop.
The systems are designed around review, explainability and validation, so AI supports commercial judgement instead of bypassing it.
People approve the action before outreach or CRM movement.
Automation needs approval logic, cancel windows and clear controls.
Users can inspect why an account, event or message was recommended.
Prototype data is fake, sample-based or anonymised where needed.
Sales context, source evidence and human judgement stay part of the workflow.
01
Signal discovery to pipeline action
AI-enabled GTM Engine
Turns market signals into reviewed accounts, enriched leads, outreach queues and pipeline visibility.
Faster signal-to-outreach
Sharper account prioritisation
Less rep time on manual research
Business problem
Growth teams often sit on too many scattered signals: events, company news, CRM notes, LinkedIn activity, website research and rep memory. The result is slow account selection, inconsistent follow-up and vague pipeline.
- Prioritises accounts based on signal strength and recency.
- Creates a cleaner path from signal review to outreach.
- Improves rep focus by separating noise from action.
- Connects account discovery, enrichment, sequences and analytics.
- Designed the GTM workflow from market signal to sales action.
- Defined signal categories, review states and account actions.
- Mapped the path from discovery to enrichment, outreach and pipeline.
- Built the prototype structure using AI tools and GTM operating logic.
Detect signal
Review fit
Prioritise account
Enrich contact
Prepare outreach
Track activity
Product evidence
Hero screens
Click any screenshot to inspect it in detail.
Signals Dashboard
Review progress, high-intent accounts and signal quality.
Companies / Today’s Focus
AI-curated account prioritisation and next actions.
Outreach Ready
Approved and enriched prospects ready for review.
Outreach Analytics
Contacted, replied, enrolled and funnel visibility.
View more screens 6 extra screenshots
Home Dashboard
Command-centre view across leads, deals and team performance.
New Signals Review
One-by-one review queue for AI-detected signals.
Signals List
Prioritised signals with intent tiers and actions.
Lookalike Discovery
Generate similar accounts from ICP or previous wins.
Sequence Builder
Multi-step email and LinkedIn workflow.
Event Review
Qualify or dismiss event opportunities.
02
Sales quality and deal judgement
AI Revenue Intelligence Lab
Reviews outbound, discovery summaries and deals so AI output becomes commercially useful and safer to use.
Higher deal qualification accuracy
Safer AI-assisted decisions
Better forecast confidence
Business problem
AI can produce more sales content very quickly, but speed creates risk when messages are generic, claims are unsupported, call summaries are inflated or deals are moved forward with weak evidence.
- Flags generic language and unsupported claims before outreach.
- Turns messy discovery notes into structured sales intelligence.
- Surfaces deal risks, missing information and next-best actions.
- Supports better MEDDPICC discipline and pipeline judgement.
- Designed the evaluation logic for message quality and risk.
- Mapped discovery summaries into CRM-ready commercial structure.
- Created deal intelligence views around stakeholders, gaps and risk.
- Built explainable scoring and rewrite comparison workflows.
Paste content
Score quality
Explain risk
Suggest rewrite
Review deal
Coach next action
Product evidence
Hero screens
Click any screenshot to inspect it in detail.
AI Evaluation Engine
Reviews outbound for AI artifacts and weak claims.
Discovery Summary Review
Turns vague call notes into structured intelligence.
Deal Intelligence / MEDDPICC
Shows qualification completeness and next-best actions.
View more screens 5 extra screenshots
Risk Scorecard
Breaks quality into risk, assumptions, specificity and tone.
Inline Risk Explanation
Explains why a claim creates commercial risk.
Rewrite Comparison
Shows safer rewrite beside flagged source.
Deal Overview
Active deal view with win probability.
Stakeholder Risk View
Maps blockers, missing information and engagement.
03
Process friction to change plan
GTM Workflow Diagnostics
Diagnoses GTM workflows, finds bottlenecks and turns current-state pain into a future-state change plan.
GTM friction made visible
Clearer change priorities
Faster time-to-action plan
Business problem
Many teams know a process is slow or messy, but cannot see where the friction sits. Onboarding, renewals, RFP response and churn-save workflows often hide delays inside handoffs, approval loops and unclear ownership.
- Makes bottlenecks, manual re-entry and approval loops visible.
- Shows ownership gaps and dependency risk.
- Compares current state with proposed future state.
- Creates executive summaries from operational findings.
- Designed the diagnostic intake and workflow-mapping structure.
- Created categories for bottlenecks, dependencies and governance gaps.
- Mapped current-state, future-state and change-plan logic.
- Built executive-summary views for leadership review.
Capture workflow
Map current state
Detect findings
Assess dependencies
Model future state
Create brief
Product evidence
Hero screens
Click any screenshot to inspect it in detail.
Diagnostic Overview
Portfolio-level workflow findings and projected cycle view.
Current-State Map
Manual steps, owners, risks and handoff friction.
Future-State Comparison
Compares current workflow with proposed improvements.
View more screens 4 extra screenshots
Intake Studio
Structured workflow capture and confidence scoring.
Findings View
Groups bottlenecks, re-entry and governance gaps.
Dependencies Narrative
Shows owner concentration and manual handoffs.
Executive Brief
Translates findings into leadership recommendations.
04
Event discovery to measurable pipeline
Event Scout
Manages event discovery, event fit, exhibitor leads, post-event tasks, competitors and outcomes.
Better event ROI visibility
Consistent post-event follow-up
Fewer missed leads
Business problem
Events can become expensive guesswork when teams lack a clear way to qualify events, score exhibitors, assign follow-up and connect activity to pipeline outcomes.
- Qualifies events by country, industry, cost and commercial fit.
- Scores and enriches exhibitor leads before outreach.
- Tracks follow-up tasks and post-event activity.
- Connects events to outcomes such as meetings, demos and pipeline.
- Designed the event discovery and review workflow.
- Mapped exhibitor scoring, enrichment, tasks and CRM export.
- Created views for calendar planning, competitors and outcomes.
- Built the logic for event-led GTM prioritisation.
Discover event
Review fit
Score leads
Enrich contacts
Assign follow-up
Measure outcomes
Product evidence
Hero screens
Click any screenshot to inspect it in detail.
Event Dashboard
Event volume, qualification status and event pipeline.
Event Stream
Event discovery with fit scoring and enrichment actions.
Event Leads
Exhibitor lead list with scoring and CRM/export actions.
View more screens 9 extra screenshots
Event Filter
Select event context before reviewing leads.
Bulk Actions
Push selected leads to CRM or export.
Lead Scoring
Score or re-score leads.
Follow-Up Tasks
Track post-event task completion.
Event Outcomes
Track meetings, demos, trials and conversations.
Competitors
Track competitor presence at events.
Review Queue
Approve, skip or dismiss new events.
Calendar View
See events across the month.
Improvement Lab
Recommendations for event strategy.
05
Lead and deal control layer
Lead Manager
Tracks lead ownership, stage movement, deal conversion, team performance and pipeline hygiene.
Pipeline hygiene enforced
Stage conversion visibility
Fewer deals lost to inaction
Business problem
Once AI finds leads, the work still fails if ownership, stages, deal creation and qualification are messy. This prototype creates the operational layer for managing leads and deals after discovery.
- Keeps lead and deal ownership visible.
- Shows stage movement across lead and deal pipelines.
- Links team performance to leads, meetings, deals and revenue.
- Captures qualification context before a deal is treated as real.
- Designed the lead and deal pipeline structure.
- Mapped stage movement from new lead to client and closed-won.
- Created lead creation, deal creation and qualification flows.
- Connected team performance, deal value and pipeline hygiene.
Create lead
Assign owner
Move stage
Create deal
Capture context
Review performance
Product evidence
Hero screens
Click any screenshot to inspect it in detail.
Lead Manager Dashboard
Pipeline and team performance across leads and revenue.
Leads Pipeline
Kanban lead pipeline with owners and stage movement.
Deals Pipeline
Deal pipeline with value, weighted value and win probability.
View more screens 7 extra screenshots
All Leads
Searchable and filterable lead table.
Team Leaderboard
Team performance across leads, revenue and meetings.
Add Lead
Structured lead creation form.
Add Deal
Deal creation linked to existing or new leads.
Qualification Fields
Why change, why us, why now, risks and blockers.
Create Lead and Deal
Combined lead and deal creation flow.
Lead Table Details
Lead stage, deal stage, score and usage context.
06
Layered reasoning to better GTM decisions
GTM Decision Intelligence
Makes the reasoning path behind an account recommendation visible, challengeable and safer to act on.
Decisions explainable to stakeholders
Lower risk of acting on bad signals
Faster cross-team alignment
Business problem
Single-prompt AI can jump from a thin signal to a confident recommendation. That creates GTM risk: weak-fit accounts, generic messages, wasted rep time and poor prioritisation.
- Shows why an account, motion, persona and next action are recommended.
- Weights evidence and compares competing hypotheses.
- Surfaces reasoning risks and the cost of being wrong.
- Requires manual verification before outreach.
- Designed a multi-stage reasoning chain across signal, research, qualification, strategy, messaging and QA.
- Created decision evolution views for each stage.
- Built evidence weighting, competing hypothesis and risk views.
- Defined manual verification checks before action.
Detect signal
Research context
Qualify account
Choose motion
Draft message
QA decision
Product evidence
Hero screens
Click any screenshot to inspect it in detail.
Decision Recommendation
Tier, persona, motion, recommended action and checks.
Reasoning Chain
Shows how each reasoning stage contributes.
Single Prompt vs Layered Reasoning
Compares generic output with grounded reasoning.
View more screens 8 extra screenshots
Signal Detection
Turns raw signals into commercial opportunities.
Executive Summary
Why now, persona, action and manual checks.
Qualification Stage
Converts fit into account tier and score.
Evaluation Layer
Scores relevance, specificity, risk and confidence.
Research Stage
Adds business context and failure modes.
Decision Report
Exports evidence, hypotheses and risk.
Evidence and Hypotheses
Weights evidence and competing hypotheses.
Reasoning Risks
Shows where the analysis could be wrong.
Series A–B SaaS
20–100 people
NL / EMEA
Outbound or CS scale
Working with early-stage B2B SaaS scale-ups in EMEA who need AI-native GTM systems built.