Evidence-First Methodology

Why generic intent data fails — and how VexASI captures, adjudicates, and delivers verified market signals.

Why Generic Intent Data Fails

Generic intent data platforms aggregate browsing behavior, form downloads, and content engagement signals from third-party data brokers. The problem: these signals measure content consumption, not buying intent. A marketing whitepaper download tells you someone read something. It does not tell you who is planning a procurement, what technology they are evaluating, or when they intend to act.

For GTM teams selling into AEC, Drug Discovery, Advanced Manufacturing, and Logistics — sectors where buying cycles are long, stakeholders are distributed, and decisions are technically complex — generic intent data produces high false-positive rates, low relevance, and poor sales alignment.

Three Core Failures of Generic Intent Data

  • No source traceability. Scores are black-box metrics with no auditable origin. You cannot verify what triggered a signal.
  • No sector specificity. Signals are collected the same way regardless of whether you sell to architects, factory operators, or logistics planners.
  • No evidence preservation. When a signal fires, the context that made it relevant is usually lost or abstracted.

How VexASI Captures Public Evidence

VexASI targets public sources that are known to contain procurement-relevant signals within the AEC, Drug Discovery, Advanced Manufacturing, and Logistics sectors. The focus is on sources with high signal-to-noise ratio for these verticals.

Hiring Signals

Public job postings reveal procurement timelines. A firm hiring a "Revit BIM Manager" or "WMS Implementation Lead" is likely in or approaching a technology adoption cycle.

Technology Signals

Vendor implementation announcements, conference tool talks, and technical blog posts reveal active technology evaluation and adoption.

Project Signals

Public project awards, facility construction announcements, and capacity expansion filings indicate capital spending cycles.

Compliance & Regulatory

Filings, certifications, and compliance updates create procurement pressure points that intent data cannot capture.

How Zeta Extracts Candidate Signals

Zeta is the extraction layer. When a public source contains a candidate signal, Zeta runs a local AI extraction pass to pull structured data from the source document.

  1. Source Identification

    The discovery layer flags a public document as a potential signal source based on sector relevance, source type, and recency thresholds.

  2. Local Extraction

    Zeta extracts the original quote, source URL, company name, signal classification, and any other relevant structured fields. The extraction runs locally — source data is not sent to third-party LLM APIs.

  3. Signal Classification

    The extracted signal is classified by type (hiring, technology, project, compliance), sector relevance, and initial confidence assessment.

  4. Packet Assembly

    The extraction output becomes an evidence packet — a structured object containing the original quote, URL, classification, and metadata. This packet is submitted for Alpha review.

How Alpha Adjudicates

Alpha is the human QA layer. No signal becomes intelligence without Alpha review. The operator evaluates each evidence packet against a structured set of criteria:

  1. Source Validity

    Is the source public, verifiable, and reliable? Proprietary or paywalled sources without accessible quotes are deferred.

  2. Signal Specificity

    Does the evidence point to a specific company, technology, or action? Generic statements without named tools or concrete commitments are rejected.

  3. Sector Relevance

    Is the signal relevant to AEC, Drug Discovery, Advanced Manufacturing, or Logistics? Out-of-scope signals are excluded.

  4. Temporal Relevance

    Is the signal recent enough to be actionable? Stale signals are deprioritized or excluded based on defined thresholds.

  5. False Positive Filter

    Is this signal likely a false positive? Self-referential corporate marketing, generic buzzword usage, and recycled content are flagged and rejected.

Signals that pass all gates are promoted to "approved" status. Signals that fail are rejected with documented reasoning. The rejection reason is preserved for quality tracking.

How False Positives Are Blocked

The VexASI system applies multiple layers of false-positive blocking:

Generic Buzzword Filter

Statements containing generic terms like "digital transformation," "innovation," or "next-gen platform" without named tools or concrete commitments are rejected at extraction.

Self-Reference Detection

Corporate press releases announcing company milestones without third-party vendor references are flagged for review and typically deferred.

Stale Signal Exclusion

Signals older than the defined recency threshold are automatically deprioritized or excluded to ensure delivery relevance.

Alpha Human Gate

Every candidate passes through human review. The Alpha operator has final adjudication authority — no automated signal is delivered without verification.

How Approved Claims Become Briefs

Approved signals are compiled into intelligence briefs — structured documents designed for GTM handoff. Each brief contains:

Briefs are delivered on a weekly cadence during the pilot phase, with clear success criteria defined upfront. The goal is to prove signal quality against your target accounts before scaling.

Request Intelligence Brief