K01
Product knowledge
Capabilities, use cases, delivery model, limitations, FAQs, and value propositions.
VERA USES IT TO
Decide whether the account’s problems genuinely map to the product
SALESKEYS KNOWLEDGE HUB
Train Workers with your knowledge—not generic prompts
Import product, ICP, market, exclusion, template, and SOP knowledge so Vera, Clara, and Victor make decisions from real company context—not generic prompts.
Import knowledge your team already uses
Templates
Email templates, message templates, call scripts, workflow templates
SOPs
Sales, support, and onboarding SOPs, plus internal processes
Product knowledge
Product docs, FAQs, internal references, external materials
Competitor intelligence
Competitor profiles, battle cards, market analysis, positioning docs
Knowledge packs
Package and reuse knowledge across teams and Workers
External sources
Import from Notion, Google Drive, Confluence, files, or URLs
Knowledge hub
Buyer research knowledge pack
32 items · Vera
92%
Outreach templates
26 items · Clara
87%
Objection handling
18 items · Victor
82%
Product FAQ
45 items · All
76%
Competitor battlecards
12 items · Shared
68%
VERA KNOWLEDGE READING
Vera does more than retrieve documents—knowledge constrains every research decision
Whether an account deserves research depends on your product capabilities, ideal-customer criteria, target-market context, and explicit exclusion boundaries. Vera reads and combines all four in every research task.
K02
ICP knowledge
Industry, size, region, business model, technology, buying roles, and priority attributes.
VERA USES IT TO
Score fit and prioritize accounts and decision-makers for research
K03
Market knowledge
Regional rules, industry trends, competitive landscape, terminology, channels, and local context.
VERA USES IT TO
Interpret signals, localize context, and avoid market-blind conclusions
K04
Exclusion knowledge
Restricted industries, unsupported regions, competitors, customers, low-value types, and compliance limits.
VERA USES IT TO
Block targets that should never enter downstream execution
Vera’s knowledge-reading and decision sequence
Knowledge is not simply concatenated. Vera calls the relevant pack at each research stage and retains citations in the decision evidence.
Read product boundariesMatch ICP criteriaAdd market contextApply exclusionsGenerate research conclusion
Exclusion knowledge has final veto
Even with positive signals, an account that matches an explicit exclusion is not handed to Clara, and the reason is retained.
Vera decision trace
Example account · Northstar Automation
- Product relevance: predictive maintenance and industrial sensor need
- ICP match: manufacturing, about 420 employees, target territory
- Market signal: expanding production and hiring reliability engineers
- Exclusion check: no competitor, existing-customer, or restricted-region match
STRUCTURED OUTPUT
Research conclusion, fit score, cited evidence, exclusion-check result, and a recommended angle ready for Clara.
How the knowledge hub works
Import knowledge
Upload files or connect Notion, Google Drive, or URLs.
Organize by category
Tag and structure knowledge into topics and packs.
Connect to Workers
Bind knowledge packs to Vera, Clara, Victor, or specific workflows.
Reuse across teams
Share knowledge packs between workspaces and teams.
Measure usage
Track how often knowledge is used and how well it performs.
How teams use the knowledge hub
Align many Workers on the same facts and talk tracks
Distribute packs and templates by team or initiative
Keep Workers aligned as product and FAQs change
Share competitor battlecards across teams and workspaces
See which knowledge drives execution quality