How Does Valley's AI Research and Personalization Actually Create Human-Quality Messages?
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Make LinkedIn your Greatest Revenue Channel ↓
Saniya Sood
How Deep Does Valley's Research Go for Each Prospect?
Valley's research engine goes far beyond basic LinkedIn scraping, capturing 60-100 data points per prospect from diverse sources across the internet. This isn't just profile information - Valley conducts the equivalent of 15-20 pages of research for every single prospect, examining their digital footprint comprehensively.
The research spans multiple platforms and sources: LinkedIn profiles and activity, entire company websites (every page indexed), social media presence across TikTok, Instagram, YouTube, and Substack, podcast appearances and speaking engagements, press releases and news mentions, hiring trends and job postings, funding history and investor information, and even Spotify playlists or other unique digital signatures. This creates a 360-degree view of each prospect that no human SDR could realistically compile at scale.
Valley's AI then intelligently determines which research details are most relevant for each specific prospect. For instance, if targeting a CMO who recently spoke at a conference about attribution challenges, Valley might reference that specific quote while positioning your solution in that context. This creates genuinely personalized messages that demonstrate real understanding, not just mail merge personalization.
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What Makes Valley's 7-LLM Architecture Superior to Single AI Models?
Valley employs seven specialized LLMs working in concert across 25 processing steps for each message, creating a sophisticated orchestration that single-model solutions can't match. Each model handles a specific function: one analyzes your company context, another researches prospects, a third handles personalization, a fourth ensures message quality, a fifth manages tone matching, a sixth handles qualification scoring, and a seventh optimizes for LinkedIn's specific constraints.
This multi-model approach isn't just technical complexity for its own sake. As Valley explains: "Valley uses seven different language models on each message to capture subtle nuances of how you speak and write, while incorporating the most relevant research." The result is messages that don't feel AI-generated because different specialists handle different aspects of the communication.
The 25-step processing ensures that every message goes through multiple quality checks and refinements. Valley determines which research points to emphasize, how to frame your value proposition for this specific prospect, what tone and style to use based on their seniority and industry, and how to create a compelling hook that will resonate. This orchestrated approach produces messages that consistently outperform both human SDRs and simpler AI tools.
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How Does Valley Learn and Match Your Unique Writing Style?
Valley's writing style system goes beyond simple templates or tone settings. The platform learns your communication patterns through multiple inputs: examples of successful messages you've sent in the past, specific dos and don'ts you define, industry-specific terminology and phrases you prefer, and even punctuation and capitalization preferences.
The system is remarkably flexible. One user noted: "The all lowercase letters in the demo messages were my CEO's writing style, which makes it feel like a text. However, users can customize their writing style in Valley." Whether you prefer formal business communication or casual, conversational tones, Valley adapts to match your authentic voice.
Valley offers preset writing styles (Valley style, Director style, Executive style) updated twice monthly based on aggregated success data. However, most users customize these starting points. You can create multiple writing styles for different contexts - one for C-suite outreach, another for practitioners, another for specific industries. The AI then applies the appropriate style based on the prospect and campaign context.
Can Valley Handle Industry-Specific and Technical Personalization?
Valley excels at incorporating industry-specific context and technical details that make messages credible to sophisticated buyers. The platform can recognize and appropriately use industry jargon, reference relevant compliance requirements (HIPAA for healthcare, SOC-2 for security), incorporate technical specifications when targeting engineers, and adapt complexity based on the prospect's technical sophistication.
For example, when one user asked about targeting companies using specific platforms like Workiva or Oracle EPM, Valley demonstrated it could identify these technologies through job postings, website mentions, and other public signals. The AI then incorporates this technical context naturally: "I noticed your team is expanding Oracle EPM capabilities based on recent job postings..."
Valley's research agents can be configured to prioritize different information types based on your industry. Technical sellers might prioritize technology stack and integration details, while business sellers might focus on funding, growth metrics, and strategic initiatives. This configurability ensures relevant personalization regardless of your market.
How Does Valley Ensure Messages Don't Sound AI-Generated?
Valley employs multiple techniques to ensure messages feel genuinely human-written. First, the multi-model architecture prevents the repetitive patterns that plague single-model systems. Each message is unique because it's built from scratch based on specific prospect research, not modified from templates.
Second, Valley incorporates natural variation in message structure, length, and style. The AI doesn't follow rigid formulas - sometimes it leads with a question, sometimes with an observation, sometimes with a connection point. This variety mirrors how humans actually write when they're being thoughtful and personal.
Third, Valley's tone matching ensures appropriate informality or formality based on context. The system recognizes that a message to a startup founder should feel different from one to an enterprise executive, even when selling the same product. Users consistently report: "Valley's messaging is what impressed me most. The messaging is not nearly as personal and it doesn't sound like AI wrote it, right? It sounds like a real human."
What Research Agents Can Valley Deploy for Different Prospecting Scenarios?
Valley offers multiple specialized research agents that can be combined for optimal personalization. Users can select up to five agents per campaign, with each agent focusing on different intelligence gathering:
Company Deep Dive examines entire websites and web presence, Prospect Deep Dive focuses on individual achievements and background, Recent Posts/Comments captures LinkedIn activity and engagement, Hiring Trends identifies growth patterns and team expansion, Fundraising History tracks investment rounds and growth trajectory, Press Releases finds recent announcements and initiatives, and Customer Success identifies social proof and case studies.
The strategic selection of research agents dramatically impacts message quality. For example, when targeting technical buyers, you might prioritize hiring trends (to identify technical stack from job postings) and company deep dive (for architecture details). When targeting executives, you might focus on fundraising history and press releases to reference strategic initiatives.
Users can also provide specific research instructions: "Find the most recent funding round. If unavailable, check for sales hiring. If neither exists, reference recent news." This hierarchical approach ensures every message has relevant personalization, even for prospects with limited public information.
How Does Valley Handle Personalization for Prospects with Limited Online Presence?
Even when prospects have minimal LinkedIn activity or limited digital footprints, Valley's comprehensive research approach finds personalization angles. The platform looks beyond the individual to examine company-level signals, industry trends, competitive landscape, and peer company activities. This ensures meaningful personalization even for low-profile prospects.
Valley's multi-source approach is key here. While a prospect might have a sparse LinkedIn profile, Valley might find them quoted in a trade publication, mentioned in a company blog post, listed in conference speaker rosters, or referenced in team announcements. The AI synthesizes these scattered data points into coherent personalization.
For truly minimal-information prospects, Valley falls back on intelligent company and industry-level personalization. Rather than sending generic messages, it might reference recent company initiatives, industry challenges, or competitive dynamics that would be relevant to someone in that role. This maintains message quality even in information-scarce scenarios.
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