An AI agent that creates a dynamic 'battle card' for upcoming sales calls. Give it the prospect's name and company, and it scours the web for recent news, financial reports, blog posts, and social media activity to build a comprehensive brief with talking points and recommended angles.
Good B2B salespeople research before every meeting, but it takes time: reading the prospect's website, recent news, annual reports, job ads, press releases and whatever the contact has said publicly. When calendars fill up, preparation gets squeezed, and reps walk in with generic pitches. Managers know better-prepared calls go better, yet cannot give reps more hours in the day.
An AI sales call prep assistant does the reading. Given a contact name and company, it gathers recent public information from permitted sources, pulls in what your CRM already knows about the account, and writes a one-page brief: what the company does, recent changes, likely priorities, relevant case material from your own library and suggested questions. The rep still owns the conversation and should check anything surprising, but they start with context rather than a blank page.
The rep enters a contact and company, or the assistant triggers automatically from a new meeting in the calendar or CRM.
The agent queries news APIs, the company's own website, public filings and licensed data providers, plus the CRM record and past emails for the account.
Retrieved material is ranked for relevance and recency, and an LLM summarises it into a structured brief with links to every source.
The brief matches likely pain points with your products and suggests discovery questions, drawing on your approved messaging and case material.
The rep reviews the brief in a web view or email before the call and can mark errors, which improves future source selection.
A sales rep inputs a prospect's name. The AI returns a one-page brief highlighting the prospect's company recently announced an expansion (opportunity), and the prospect commented on LinkedIn about supply chain issues (pain point), suggesting a tailored pitch.
LLMs (GPT), Web Scraping APIs (BrowseAI), News APIs, RAG, Web UI
Researching businesses from public sources is common practice, but personal information about individual contacts is still covered by the Privacy Act 1988 once you collect and store it in your CRM. Keep briefs focused on professional, business-relevant information and avoid compiling personal profiles. Scraping LinkedIn or other social platforms generally breaches their terms of service, so use official APIs or licensed data providers rather than automated scraping. If research leads to outreach, the Spam Act 2003 applies to commercial emails and messages, and the Do Not Call Register Act 2006 may apply to phone calls to certain numbers.
This solution requires careful evaluation of cost-benefit ratio. Consider implementation only after thorough analysis and risk assessment.
Automated scraping of LinkedIn breaches its user agreement and can get accounts restricted. Safer options include LinkedIn's own sales tools, licensed B2B data providers and information prospects have shared directly with you. The assistant can also summarise notes a rep pastes in manually from a profile they viewed themselves.
Accuracy depends on source quality. Company news, websites and filings produce reliable summaries, while ambiguous names or thin online presence cause errors. Because every point in the brief links to its source, reps can verify quickly. Treat the brief as preparation notes, not a set of confirmed facts to quote back to the prospect.
The two-to-four-week band covers connecting the CRM and calendar, choosing data sources, building the brief template and testing with a few reps. Costs depend mainly on data provider subscriptions and model usage per brief. Starting with one sales team and a single brief format keeps the pilot manageable.
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