Why do Startups & SMEs struggle with B2B sales?
Because they spend time reaching out to prospects that do not convert.
Why do those prospects not convert?
Because many of them are not the right ICP, they do not have the pain, budget or urgency.
Why are companies targeting the wrong ICP?
Because identifying qualified prospects is difficult, there are thousands of companies but only a few are truly ready to buy at a given time.
Why is it hard to know which companies are ready?
Because most data sources such as Crunchbase, LinkedIn or Apollo provide only static facts like funding rounds, headcount or tech stack, not signals of urgency or need.
Why are static facts not enough?
Because, for example, knowing a company has raised £5 million shows growth but does not prove they need a specific solution. It demonstrates capacity, not fit.
Why do companies still chase these signals?
Because it is the only data available, so they assume that recent funding automatically equates to buying power and therefore a good lead.
Why is that assumption flawed?
Because the real qualification signals are often hidden in risks and gaps such as senior departures, hiring freezes, negative press or compliance challenges.
Why do these signals matter more?
Because they reveal context, highlighting what the company is struggling with at present and whether it is in a position to buy.
Why does Ventur exist?
To stop Startups & SMEs wasting time on unqualified prospects by applying a risk intelligence lens to company data, highlighting which prospects are genuinely worth pursuing today.
Why do VCs conduct diligence?
Because they need to decide whether a startup is worth investing in.
Why is diligence difficult?
Because startups present polished narratives that often omit or obscure weaknesses.
Why can analysts not easily uncover those risks?
Because relevant information is fragmented across news, regulatory filings, technical signals and social media activity.
Why is fragmented information a problem?
Because it takes hours or days to assemble and important details are frequently missed.
Why does manual research increase the chance of missed signals?
Because analysts are pressured to maintain deal flow, so they rely heavily on pitch decks and high-level databases such as Crunchbase.
Why is surface-level data insufficient?
Because funding history, headcount or technology stack show what a company looks like, not where it might fail.
Why do unseen risks matter so much?
Because a single overlooked issue, such as a regulatory issue, toxic culture, key churn risk or dependence on one major customer, can undermine an investment.
Why can VCs not rely solely on consultants or law firms?
Because they are expensive and slow, and usually only involved in later stages. Early screening is still largely internal and shallow.
Why does Ventur exist?
To help VCs improve screening by surfacing early risk signals, enabling them to focus on the startups that are genuinely safe to pursue further.
Large Language Models (LLMs) like GPT-5 Pro and Gemini 2.5 Pro are incredibly powerful at processing general knowledge. But business intelligence demands more than broad summaries; it requires verifiable data, contextual depth, and actionable risk analysis.
We tested Ventur against state-of-the-art models using a real-world B2B due diligence task: Comprehensive risk and opportunity profile for Only Fans UK entity, Fenix International Limited.
See how Ventur's specialised intelligence engine compares.
GPT-5 Pro vs Ventur
GPT-5 Pro produced a lengthy, compliance-oriented document. While it cited official sources, it leaned heavily on public websites, EU/UK law summaries, and regulator press releases. Numbers were often quoted without verification, and the tone drifted into legal commentary rather than business intelligence.
Comparison Point 1: Financial Performance & Scale
Goal: Identify key performance indicators (KPIs) to understand company health and scale.
| GPT-5 Pro | Ventur Intelligence |
|---|---|
| Result: ❌ Incomplete Data | Result: ✅ Comprehensive Metrics |
| Focuses heavily on regulatory news and the Ofcom fine. Fails to extract core financial and user metrics from available data. The output describes regulatory actions but misses the underlying performance of the business itself. | Delivers precise, verified metrics essential for due diligence: • Gross Site Volume: $7.22 billion • Pre-Tax Profit: $683.58 million • Fan Accounts: 377.46 million (+24% YoY) • Creator Accounts: 4.63 million (+13% YoY) |
| Takeaway: General models often miss critical quantitative data if it's not explicitly stated in easily scraped news headlines. | Takeaway: Ventur's data pipeline specifically targets and extracts hard financial and operational KPIs to build a complete picture. |
Comparison Point 2: Risk Analysis Quality
Goal: Move beyond describing regulation to assessing actual, specific risks.
| GPT-5 Pro | Ventur Intelligence |
|---|---|
| Result: ❌ Descriptive Overview | Result: ✅ Actionable Risk Matrix |
| Correctly identifies the Online Safety Act (OSA) as a key regulatory framework. The analysis describes what the law entails in general terms but does not connect it to specific, ongoing platform failures or user-reported issues. | Identifies specific user-reported harms (from Trustpilot analysis) like underage access allegations and creator misrepresentation. It then directly maps these issues to specific duties under the OSA, creating an actionable risk assessment. |
| Takeaway: Provides a good summary of what the rules are. | Takeaway: Shows why the rules matter by linking them to real-world evidence of risk and user harm. |
📊 Benchmark Snapshot
| Criteria | GPT-5 Pro | Ventur |
|---|---|---|
| Financial Accuracy | ❌ Partial | ✅ Verified |
| Risk Context | ❌ Generic | ✅ Targeted |
| Data Sources | ❌ Public Web | ✅ Modular Pipelines |
| Hallucination Rate | ⚠️ Moderate | ✅ Low |
| Output Style | ❌ Long, Legalistic | ✅ Business-first |
Verdict: GPT-5 Pro is powerful at general analysis but falls short on precision + actionable risk intelligence. Ventur bridges that gap.
Ventur Difference:
Ventur's purpose-built pipeline delivered a structured, actionable report:
- Data Pipeline: Ventur's modular ingestion pulls from official records, financial filings, regulatory notices, and structured databases in isolated silos. GPT-5 Pro relied on web scraping + summarisation.
- Accuracy: Ventur cross-verifies metrics (e.g., Gross Site Volume $7.22bn, pre-tax profit £683.58m) against official filings. GPT-5 Pro showed inconsistencies and lacked financial precision.
- Bias Control: Ventur's agentic analysis layer merges siloed data before generating insights → fewer hallucinations, less duplication. GPT-5 Pro output blended blog commentary with regulation text.
- Actionability: Ventur highlights risks, gaps, opportunities in context (e.g., Ofcom fine impact + OSA categorisation risk). GPT-5 Pro listed regulatory frameworks without business implications.
Large Language Models (LLMs) like GPT-5 Pro and Gemini 2.5 Pro are incredibly powerful at processing general knowledge. But business intelligence demands more than broad summaries; it requires verifiable data, contextual depth, and actionable risk analysis.
We tested Ventur against state-of-the-art models using a real-world B2B due diligence task: Comprehensive risk and opportunity profile for Only Fans UK entity, Fenix International Limited.
See how Ventur's specialised intelligence engine compares.
Gemini-2.5 Pro vs Ventur
Gemini-2.5 Pro produced a polished report with strong legal framing (tax precedent, gig economy reclassification risks). But its financials were vague, and user harm data (e.g., Trustpilot reviews, Ofcom enforcement specifics) were missing. It gave the big picture, but not the granular signals Ventur surfaces.
Comparison Point 1: Specificity of Compliance Events
Goal: Find precise details of regulatory enforcement actions.
| Gemini 2.5 Pro | Ventur Intelligence |
|---|---|
| Result: ❌ Vague & Conceptual | Result: ✅ Precise & Verifiable |
| Identifies the Online Safety Act and HMRC VAT case as key "pillars" of regulatory pressure. However, it fails to mention the specific £1.05 million fine issued by Ofcom to Fenix International Ltd, a critical, recent compliance event. | Pinpoints the exact enforcement action: • Fine Amount: £1.05 million • Regulator: Ofcom • Date: 27 March 2025 • Reason: Failure to provide accurate information regarding age verification processes. |
| Takeaway: Understands the general risk landscape but misses crucial, specific data points that signal actual compliance failures. | Takeaway: Ventur's risk engine flags specific enforcement actions, dates, and amounts, providing concrete evidence of regulatory friction. |
Comparison Point 2: Data Synthesis and Output Utility
Goal: Generate a decision-ready report for an analyst or sales team.
| Gemini 2.5 Pro | Ventur Intelligence |
|---|---|
| Result: ❌ Narrative Essay | Result: ✅ Structured Due Diligence Report |
| Produces a high-level narrative analysis. The output reads like a consultative essay on potential risks ("Pillar 1," "Pillar 2") but lacks the underlying company performance data (revenue, profit, user count) to put those risks into context. | Delivers a structured report combining: 1. Financials: Full P&L and growth metrics. 2. Compliance: Specific fines and regulatory timeline. 3. Risk Assessment: A matrix linking user sentiment to potential regulatory breaches. 4. Recommendations: Actionable steps based on the findings. |
| Takeaway: Requires an analyst to re-process the information and find the missing data separately. | Takeaway: Provides an integrated, decision-ready asset that directly answers key diligence questions. |
📊 Benchmark Snapshot
| Criteria | Gemini 2.5 Pro | Ventur |
|---|---|---|
| User Harm Signals | ❌ Missing | ✅ Captured |
| Risk Assessment | ❌ Broad | ✅ Structured |
| Business Relevance | ❌ Legalistic | ✅ Commercial |
| Data Integration | ❌ Web scraping | ✅ Multi-silo pipelines |
| Accuracy / Hallucination | ⚠️ Moderate | ✅ Low |
Verdict: Gemini 2.5 Pro excels at framing laws and precedents but lacks the precision, structure, and actionability Ventur delivers for decision-makers.
Ventur Difference:
Ventur detects hidden qualification signals that Gemini 2.5 Pro ignores, links regulatory outcomes directly to business impact, provides granular risk mapping, and integrates official filings for superior data fidelity.
- Signals Layer: Ventur detects hidden qualification signals (e.g., user complaints on refunds, account security gaps) that Gemini 2.5 Pro ignores.
- Depth of Context: Ventur links regulatory outcomes directly to business impact (e.g., OSA Category 1 status → compliance cost projections). Gemini listed risks but without probability/impact scoring.
- Granularity: Ventur's risk table maps Likelihood vs Impact, plus expected mitigations. Gemini 2.5 Pro summarised risks as broad themes.
- Data Fidelity: Ventur integrates official filings + regulator decisions. Gemini 2.5 Pro leaned on legal commentary and precedent, without concrete performance metrics.
Our Business Intelligence Infrastructure
Ventur processes thousands of structured and unstructured company data points through a distributed, modular pipeline, designed to surface risks, gaps, and opportunities that general AI misses. Let's use the example from our Only Fans benchmark.
Context Retrieval
- Entity recognition: Company identification (Only Fans → Fenix International Ltd)
- Time range detection: Fiscal years, filings, enforcement windows
- Signal extraction: Hiring freezes, regulatory fines, account complaints, financial filings
- Multi-format ingestion: PDFs, HTML, press releases, regulatory notices, structured databases
Data Isolation & Pipeline Processing
- Each source ingested into isolated silos (regulatory → financial → sentiment → ownership)
- Silos are merged downstream into a cohesive knowledge graph
- Prevents contamination, ensures verifiability at the source
Agentic Analysis
- Risk mapping: Likelihood × Impact scoring
- Opportunity detection: Growth signals, funding, expansion plans
- Bias reduction: Multi-agent consensus avoids single-source hallucination
- Cross-verification: Financial data matched against official filings before inclusion
Output Generation
- Reports structured into: Corporate profile, scale & economics, regulatory perimeter, user sentiment, risk table
- Evidence-linked: Every claim tied back to a source (Companies House, Ofcom, Trustpilot, etc.)
- Action-first style: Reports highlight why it matters, not just what happened
Real-Time Multi-Silo Ingestion
- Official filings: Companies House, HMRC, FCA registers
- Regulatory notices: Ofcom, CMA, ICO, Parliament
- Financial data: Annual reports, liquidity snapshots, dividends
- Sentiment: User reviews, media coverage, social chatter
- Ownership & control: Beneficial ownership registries, corporate structures
Unlike GPT-5 Pro or Gemini-2.5 Pro, which lean on blogs, articles, and generic scraping, Ventur's siloed ingestion ensures authoritative, verifiable inputs.
Accuracy by Design
- Reduced Hallucination: Structured ingestion + agentic analysis avoids filler speculation
- Cross-verified numbers: e.g., Only Fans £683.58m pre-tax profit validated against official filings
- Actionable risk tables: Every risk given probability, impact, mitigation under law
Technology Comparison
| Technology Layer | General LLMs | Ventur |
|---|---|---|
| Data Access | Web scrape/blogs | Verified filings, official records |
| Data Pipeline | Monolithic | Modular, siloed ingestion |
| Hallucination Control | Limited | Multi-agent consensus + cross-verification |
| Bias Reduction | ❌ | ✅ Explicitly designed |
| Output Style | Narrative | Structured, actionable |
Privacy & Security
- Data isolation by default: Each client's data lives in separate silos, no cross-contamination.
- Access control: Only sources explicitly authorised by our client are ingested.
- Transparency: Every datapoint traceable to its origin.
And a Little Bit of Magic
Everything above is part of the infrastructure that powers Ventur. But truth be told… we also sprinkle a little fairy dust. The seamless orchestration, the tiny design details, the hidden optimisations that make it all feel effortless. We can't reveal everything. Let's just say there's a layer of "Ventur Magic" in the mix, that makes it all work seamlessly. 🧚