D.A.S. (Demand Activation Score) is the system we built to turn public data intelligence into actionable insights you can act on.
Over 350 universal vectors across 31 analytical categories, evaluating over 10,000 external business signals. A custom-built analysis for every engagement, shaped around exactly what you need the D.A.S. system for.
Transparency & Protection
D.A.S. tracks over 350 universal vectors across 31 categories, evaluates every data point for both importance and reliability, and uses pressure signal detection to build a unique analysis for each engagement. This structural intelligence engine is what makes our competitive risk intelligence different from data platforms or research firms. To keep it different, the operational details stay protected.
We show enough for you to understand what D.A.S. does and why it works, from organizational buying dynamics to financial stress indicators. We protect enough to make sure it stays working. The next six steps walk you through what happens at each stage.
How It Works
Every engagement follows the same six steps. Here is what happens at each stage and why it matters.
Every engagement starts with understanding your business. We learn what you sell, who you sell it to, and what makes your offering valuable in your market. We then identify the right target and confirm there is enough available data to produce meaningful target company intelligence. If there is not, we tell you before any work begins. Learn more about our process.
We gather public data intelligence from publicly available sources, including financial filings, regulatory databases, and labor market signals, among others. The full list of where we look is part of our protected methodology. For certain engagements, clients can bring in their own data, paid research subscriptions, or internal intelligence at additional cost.
The collected data is organized against 350+ universal vectors across 31 analytical categories. Before anything contributes to a score, every data point is evaluated on two levels.
Not every external business signal carries the same importance. A regulatory deadline means something different than a leadership change, and the weight of each one shifts depending on what you sell and who you are targeting.
Not every source is equally trustworthy. A court filing carries more weight than an anonymous review. Less reliable information is not thrown out, but it is rated lower so the final analysis clearly reflects what we know for certain versus what we are working from directionally.
The buying pressure analysis is built from scratch for each engagement. What matters most when selling cybersecurity software is completely different from what matters when selling logistics services or medical equipment. The model accounts for your industry, your target, and the specific product or service you are selling, whether that is something physical, digital, or a service. No two engagements use the same model.
What the system produces depends on which product is being used. A Pre-Intent Intelligence engagement produces a purchase readiness analysis reflecting the overall strength and urgency of the sales trigger signals found. A R.I.S.K. engagement produces a churn risk assessment with severity levels. A Pre-Diligence engagement produces categorized findings: material concerns, areas that require verification, and areas that came back clear. Every output includes a confidence level that tells you how much of the analysis is backed by verified sources. We include this because you should always know how solid the ground is beneath a finding before you act on it.
The final output is a company intelligence report, not a spreadsheet or a dashboard. The format is written in plain language so anyone on your team can read it and act on it without needing to decode jargon or raw data. The document is built to be useful for anyone on your team, regardless of their role. See how different teams use these deliverables.
What We Analyze
D.A.S. performs multi-domain B2B sales intelligence analysis across 31 categories spanning five publicly shown areas. The 20 categories below are the ones we share publicly. Each one contains multiple vectors with their own rules for scoring, sourcing, and how they relate to each other. Those rules are protected. Additional categories are not shown publicly. See how the process works end to end.
F1
Identifies how a company's financial trajectory creates different types of purchasing urgency depending on whether they are growing, flattening, or contracting.
F2
Detects when internal cost pressures reach the point where a company is forced to find new solutions or accept declining performance.
F3
Evaluates internal financial timing and purchasing authority analysis to determine when a company is able to act on a purchase and who controls that decision.
F4
Performs capital constraints analysis to assess whether a company's financial position is creating room to buy or putting up barriers that prevent it.
O5
Identifies when a company's internal systems are creating enough friction that upgrading or replacing them stops being optional.
O6
Tracks vendor dependency risk across a company's existing vendor relationships and detects when dissatisfaction or exposure is creating an opening for change.
O7
Surfaces operational breakdowns that are large enough to force a company into action before the problem gets worse.
O8
Monitors external requirements that create purchasing timelines a company cannot negotiate or delay.
M9
Surfaces competitive risk intelligence to evaluate when competitive pressure in a company's market has reached the point where standing still is no longer an option.
M10
Tracks broader market-level changes that push companies toward solutions they were not planning to look for.
M11
Detects when a company's own customers are creating pressure that forces internal upgrades or risks revenue loss.
M12
Identifies strategic shifts inside a company that will create new purchasing needs as a downstream consequence.
P13
Monitors leadership changes and the purchasing windows that new decision-makers reliably open in their first months.
P14
Maps how organizational buying dynamics shape purchasing decisions, identifying where real authority sits and how internal approval chains affect deal velocity.
P15
Evaluates whether a company's internal environment will speed up or slow down a purchasing decision.
P16
Detects internal dynamics that influence whether a deal moves forward or stalls regardless of the product's fit.
D17
Monitors sudden events that compress decision timelines and unlock budget that did not previously exist.
D18
Tracks technology and workforce shifts that force companies to make decisions faster than they planned.
D19
Identifies physical and geographic signals that indicate a company's operations are shifting in ways that create new needs.
D20
Identifies buying trigger windows when multiple types of pressure are converging on a company at the same time, creating a window where action becomes likely.
The 20 categories above are publicly disclosed. Additional categories operate beneath the surface, handling deeper analysis that the public categories do not cover. These are classified as protected methodology and are not detailed on this site. This depth is part of what separates D.A.S. from standard B2B sales intelligence platforms.
One Engine, Three Products
D.A.S. powers all three Prophacite products. The engine is identical. The target company intelligence it produces changes based on what you need to know. See how the process works.
The same analytical engine adapts to different questions depending on the product. A purchase readiness analysis through Pre-Intent Intelligence evaluates forward-looking demand signals, while a vendor risk assessment through Pre-Diligence examines what standard due diligence typically misses. For existing client portfolios, churn risk detection through R.I.S.K. surfaces customer churn prediction indicators and customer churn analytics before revenue loss becomes visible. Because the methodology combines technology-augmented due diligence with human verification at every stage, the question of whether ai intelligence reports are accurate is addressed structurally rather than left to assumption.
Common Questions
D.A.S. stands for Demand Activation Score and Demand Activation System. The Score is the measurable output. The System is the B2B intelligence methodology that produces it. Together they represent a structured assessment of a company's position relative to the question being asked.
Intent data platforms track keyword searches and website visits to show what companies are researching right now. D.A.S. evaluates structural conditions across multiple analytical domains to identify the business conditions creating demand, often before companies begin actively searching. Intent data tells you someone is looking. D.A.S. is built to identify the conditions that force them to buy.
Multiple parts of the D.A.S. process are protected. Where we get our data, the categories we do not show publicly, how we judge source reliability, how each analysis is built, and the math behind the scores are all classified. The steps shown on this page describe what happens at each stage. The operational specifics of each stage are protected intellectual property.
Our analysis draws from publicly available sources like financial filings, regulatory records, and labor market signals. These sources form the public data intelligence that powers every engagement. The full list of sources is protected. Some citations are included with deliverables. We do not use purchased contact lists, scraped personal data, or proprietary databases at the standard level. Clients who want to bring in their own data or paid research subscriptions can do so at additional cost.
No. Every engagement gets its own model built from scratch. The same 350+ vectors are evaluated, but how much each one matters changes based on your industry, your target, and what you are selling. What matters when selling cybersecurity software looks nothing like what matters when selling logistics services or medical equipment. No two analyses use the same model.
Measurable buying urgency is measured by evaluating 350+ universal vectors across 31 analytical categories. Each vector is scored for severity and weighted dynamically based on the engagement context. The buying pressure analysis is designed to detect when multiple urgency indicators converge across observable structural conditions. The result is an assessment based on what is actually happening at a company, not on what they have searched for online or said about themselves.
We follow a six-step process: learning about your business, gathering data, organizing and weighting it, building a custom analysis model, producing a score, risk assessment, or categorized findings with a confidence rating, and delivering a finished company intelligence report. The analysis is built from scratch for each engagement and shaped around your industry, your target, and what you sell, whether that is a physical product, software, or a service.
Every engagement starts with a conversation about what you need to know and who you need to know it about. Have questions first? See our FAQ.