DCF Model Foutdetectie
WACC weights gebruiken book value. Terminal growth boven GDP. ARR expansion dubbel geteld.
Geen take-home taken meer. Geen gissen op basis van CV's. Kies expert-built scenario's uit onze bibliotheek en toets elke kandidaat voor elke rol — in minuten, niet dagen.
Voorbeelden
Drie scenario's uit de library. Klik om de valstrik te onthullen.
WACC weights gebruiken book value. Terminal growth boven GDP. ARR expansion dubbel geteld.
IDOR in download endpoint. API secret logging. Broken JWT validatie. MIME type spoofing.
Het blokkerpunt is een security review backlog — niet interesse. De AI heeft de verkeerde diagnose gesteld.
Hoe het werkt
Selecteer 2-4 scenarios uit de library die matchen met je vacature. Of laat ons het matchen.
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Zie wie echt kan werken en wie AI-antwoorden kopieert. Met anti-cheat proof.
De volledige bibliotheek
Bedrijfsjurist · Fusies en overnames · Procesvoering · Compliance · Regulering
Candidate reviews a commercial contract before a 3pm signing. The AI summary contains 3 material errors — a mislabelled liability clause, a buried auto-renewal, and a wrong governing law jurisdiction. Time pressure is real.
A deal spans UK, Delaware, and Singapore. The AI provides a confident conflict-of-laws analysis with a fundamental jurisdiction error in a commercially critical area — IP ownership under employment law.
A client wants something technically arguable but ethically compromised — an NDA drafted to suppress legitimate whistleblowing. The AI helpfully enables the request without flagging the professional responsibility dimension.
An AI-generated DD summary for a mid-market acquisition looks 80% solid. Hidden within are 3 material red flags the AI treats as isolated items — together they reveal a chain-of-title IP problem.
Arbitration in 72 hours. The AI builds a case strategy but has missed a recent appellate decision that undermines the primary argument and misread one piece of evidence in the client's favour.
A fintech startup needs a GDPR + PSD2 compliance review before launch. The AI produces a checklist that misses two material obligations specific to payment data processing and cross-border data transfers.
A SaaS company is raising Series B. The AI produces an IP ownership summary showing clean title — but misses that the founding team's prior employer may have a claim on the core algorithm under an IP assignment agreement.
HR wants to exit a senior employee. The AI drafts a termination letter and settlement proposal — but mischaracterises the notice period entitlement and omits a protected characteristic risk that creates significant litigation exposure.
A data breach occurred 6 hours ago. GDPR's 72-hour notification window is running. The AI produces a response plan that misidentifies which supervisory authority to notify and underestimates the number of affected data subjects.
A supplier is invoking force majeure to exit a delivery obligation. The AI confirms the clause likely applies — but misreads the scope, which is narrower than standard and explicitly excludes supply chain disruptions.
Enterprise AE · VP Sales · Revenue Operations · KAM · Partnerships · SDR-lead
A €280K enterprise deal has gone cold. Champion is silent. Competitor entered. AI recommends a generic check-in email and a discount. The real blocker is a security review backlog — not interest level.
A major account is 18 months into a 3-year competitor contract. AI produces a competitive intelligence brief with factual errors in the competitor's pricing model and recommends leading with ROI when the prospect's primary concern is risk.
14 deals, 3 high-intensity pushes available. AI ranks by deal size alone — ignoring champion strength, days-since-activity, and competitive exposure data that is present in the context.
Six stakeholders: CFO, CISO, CTO, Legal, Operations, champion. AI sends the same ROI-focused message to all six and identifies the wrong person as economic buyer.
11-point procurement counter-proposal including uncapped liability and 90-day payment terms. AI recommends accepting 6 points that should be defended — including unlimited liability with no pushback.
Build a 5-touch outbound sequence targeting VP Operations at logistics companies. AI generates a polished but generic sequence with no personalisation signals and leads with product features rather than operational pain.
A 2-year customer is at 70% of contract value. Identify the expansion opportunity and build the business case. AI focuses on seat expansion — missing a larger platform opportunity that a usage analysis reveals.
A €2.1M quarterly forecast. AI analyses the pipeline and confirms the number is achievable. But 3 deals in Commit stage have warning signs — stale activity, no economic buyer access, competitor present — that AI ignores.
6 months of win/loss data. AI attributes most losses to price. The candidate must use AI to dig deeper and uncover that the real pattern is late-stage champion loss — not price sensitivity.
Should the company expand into the Benelux market? AI provides a market analysis that overstates TAM and ignores an existing distribution partnership that would conflict with direct sales motion.
CMO · Growth · Brand · Performance · Productmarketing · Demand Gen
€400K/quarter campaign underperforming. AI uses last-touch attribution and recommends cutting LinkedIn — which is the top-of-funnel driver for 68% of closed deals. The methodology, not the channel, is wrong.
Competitor announced a product with 3 overlapping features at 20% lower price. Launch is in 48 hours. AI recommends a price match and pausing the launch — both strategically wrong.
3 years of customer data. AI segments by company size — the visible variable — missing behavioural signals (expansion triggers, feature adoption patterns) that are far more predictive of LTV.
A post mischaracterising the product has 40K shares in 4 hours. AI drafts a legally safe but brand-dead response that will accelerate the backfire effect by directly engaging the false claim.
New product, new market, €800K, 6 months. AI builds a complete GTM plan but sizes the market incorrectly using top-down TAM and builds the buyer journey on a flawed assumption about who initiates the purchase decision.
The blog has 400 posts. Organic traffic is declining. AI recommends a content calendar with 12 new posts per month — missing that 40 existing posts are cannibalising each other on the same keywords.
€120K influencer campaign results are in. AI declares it a success based on reach and engagement. The candidate must uncover that conversion rate was 0.3% and CAC was 4x the company benchmark.
Pricing page converts at 1.8% vs industry 3.2%. AI recommends adding more feature comparison rows. The real friction is cognitive overload and a missing risk-reversal element — not information density.
Build a 6-email nurture sequence for free trial users who haven't activated in 7 days. AI writes a generic product feature sequence. The candidate must redirect it toward activation blockers and social proof.
Google Ads spend is €80K/month. ROAS is declining. AI recommends increasing budget on top-performing keywords — but the keywords that look top-performing are branded terms that would convert organically anyway.
CFO · FP&A · M&A-analist · Investeringen · Risico · Private equity · Portfolio
An AI-built DCF looks clean and professional. Three material errors: book value WACC weights, terminal growth rate above GDP, and double-counted ARR expansion in the revenue model.
10 acquisition targets, 25 minutes, AI summaries for each. Two weaker targets are made to look stronger than they are. The best target has a hidden risk the AI labels as immaterial.
Revenue growing 28%, margins expanding, AI confirms the positive narrative. Hidden: DSO up 45 days, operating cash flow below net income for 3 years, depreciation policy change driving margin — not operations.
Four competing uses for €50M: M&A, organic expansion, buyback, technology investment. AI optimises for financial return only, ignoring strategic positioning and optionality that transforms the correct answer.
Rates up 200bps unexpectedly. AI stress tests with normal-period correlations that break down in a crisis, misidentifies the largest position as biggest risk (it is actually the most liquid), misses second-order margin call cascade.
Q3 results: revenue 8% below plan. AI attributes the miss to market headwinds. The candidate must use AI to uncover that 70% of the miss is concentrated in two product lines and one region — an operational issue, not macro.
Cash conversion cycle is 72 days vs industry 38 days. AI recommends extending payables — missing that the company's largest supplier contract has an early payment discount that makes extending payables net-negative.
A scale-up is targeting an IPO in 18 months. AI produces a readiness checklist rating them 7/10. Three gaps are understated: revenue recognition practices, related-party transaction disclosure, and CFO succession risk.
35% of revenue is USD, costs are EUR. AI recommends a 12-month forward hedge on 100% of exposure — missing that over-hedging natural hedges from USD-denominated costs creates unnecessary basis risk.
A borrower is requesting a €5M credit facility increase. AI analyses the financials and recommends approval. The candidate must find the covenant breach risk buried in the Q3 notes and the hidden cross-default clause.
Staff engineer · Solutions architect · SRE · Platform · Security · CTO · DevOps
API latency up 10x. AI diagnoses database CPU — wrong. The real cause is N+1 queries introduced in the last deploy. The memory spike pattern the AI interpreted is a symptom, not the root cause.
A polished AI-proposed architecture for a high-scale feature contains 3 design flaws: a synchronous chain creating cascade failure risk, an eventual consistency guarantee the queue doesn't actually provide, and a direct DB cross-read violating domain boundaries.
A PR handles auth, file uploads, and external API calls. AI gives it a clean bill of health — missing an IDOR in the download endpoint, API secret logging at INFO level, broken JWT alg validation, and MIME type spoofing in uploads.
Real-time notifications for 2M users. AI recommends a specific vendor with per-notification pricing that looks cheap at 10K users but becomes the company's second-largest infrastructure cost at 2M. SLA also doesn't meet stated requirements.
400K line Rails app, 60% untested, 18-month migration to SOA without stopping feature development. AI recommends a big-bang rewrite starting with the highest-risk payment module — the worst possible sequencing decision.
Postgres query times degraded 8x in 48 hours. AI recommends adding read replicas and increasing instance size. The real cause is table bloat from a missing VACUUM schedule on a high-churn table that was recently modified.
AWS bill has grown 3x in 6 months with flat usage. AI identifies unused EC2 instances as the primary driver. The real culprit is data transfer costs and NAT Gateway misuse — together 4x more expensive than the idle instances.
A new API version is ready to ship. AI reviews it and approves. The candidate must find 2 breaking changes that the AI classified as non-breaking and a missing pagination design that will cause client timeouts at scale.
An AI-generated DR plan claims RTO of 4 hours and RPO of 1 hour. The candidate must identify that the restore procedure hasn't been tested, the backup validation step is missing, and the RTO assumes network capacity that won't exist during a regional outage.
A backlog of 47 technical debt items. AI prioritises by estimated fix time (shortest first). The candidate must reframe around business risk impact — finding that the two items with longest fix times carry the highest production risk.
Gratis voor HR Managers
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