Splattr is built for any sales team that wins by reaching the right people with the right message, from founders and individual reps to full revenue teams. It's especially powerful for considered, research-heavy sales like consulting, staffing, and IT services, but customers across a wide range of industries use it to prioritize their market and personalize outreach at scale.

How is Splattr different from Apollo, ZoomInfo, Seamless, Clay, and Instantly?

Those tools are mostly contact databases and sending engines. They hand you names, emails, or a way to blast them. Splattr sits a step earlier: it's a targeting and scoring engine that figures out who is most likely to buy right now and why, then tells you how to start the conversation. Instead of a generic ICP list, Splattr researches every prospect through the lens of what you actually sell, layering in buying signals, hiring data, and tech stack to score and rank them.

How does Splattr find and score the right prospects?

You train an agent on what you sell, and Splattr scores prospects across your market by analyzing publicly available data like buying signals, hiring and job-posting activity, and technology footprint, all measured against your specific offer. Each prospect gets a score plus clear reasoning for why they ranked where they did, so you can prioritize with confidence.

Can I customize how prospects are scored?

Yes. Scoring is driven by the agent you train on each offer, so the criteria reflect your product, your differentiators, and your ideal buyer, not generic out-of-the-box filters. Companies selling multiple products can run a separate scoring lens for each offer.

What intelligence do I get on each prospect?

Every prospect comes with a score and the reasoning behind it, specific leverage points for your outreach, detected business signals (financial, strategic, and hiring/job changes, with sources), hiring metrics, and the company's technology footprint. It's the research that used to take 30+ minutes per prospect, ready in seconds.

Does Splattr integrate with my CRM and outreach tools?

Yes. Splattr offers a direct HubSpot integration so your scored prospects flow straight into your CRM, with Salesforce support coming soon. You can also export any list to CSV to use in other outreach tools.

How does pricing work?

Splattr is $12 per seat per month to keep your account active, and everything else runs on credits, so you only pay for the prospecting you actually do. You can go fully pay-as-you-go at $0.0299/credit, or save with a credit bundle (down to about $0.017/credit). All features are included on every plan.

What does each action cost?

Different actions use different amounts of credits: Find & score a prospect — 8 credits Look up a LinkedIn profile — 4 credits Find contacts at a company — 2 credits Research report — 8 credits Company report — 20 credits Email lookup — 6 credits Phone lookup — 15 credits Chatting with Splattr's AI assistant also uses credits, based on how much you use it.

Is there a free trial, and how do I get started?

Yes. You can score your first prospects free, no credit card required. Just tell Splattr what you sell, answer up to nine quick questions to train your agent (about 3 minutes), and you'll have scored, prioritized prospects in under 10 minutes.

How quickly will I see results?

Most users go from signup to their first qualified, scored prospects in under 10 minutes, and start personalizing outreach the same day.

How is my data handled and kept secure?

Splattr scores prospects using publicly available data, and we treat your account data as your own. We don't sell your data or use your content to train models that benefit other customers. Your data is hosted on Amazon Web Services in the United States, encrypted in transit and at rest, with role-based, least-privilege access controls. Payments are handled by our PCI-compliant processor, and we never store your card details. Full details are in our Privacy Policy and Data Processing Addendum.

Can my whole team use Splattr together?

Yes. Teams get a shared full-team view and can share campaigns across users, so everyone works from the same prospecting engine. On the Business plan you also get a shared credit pool, per-user spending controls, and dedicated support.

The Gap Between the LinkedIn Vision and Sales Reality

If you work in sales, your LinkedIn feed probably looks a certain way right now. A wall of posts promising that some stack of AI tools just replaced an entire go-to-market team, usually ending with "comment a word and I will send it to you." I got curious about who is actually behind those posts, so I looked at the context on the last twenty-five of them in my feed. ## Who is actually posting Of those twenty-five: - Four were selling LinkedIn growth strategies. - Nine were selling go-to-market consulting. - Eight were selling data for AI agents. - Four were outsourced business development shops. - Zero were go-to-market practitioners simply sharing what is working for them. Every single one was selling something. Not one was a person in the seat describing their real results. That alone explains a lot about the distance between the vision online and what actually happens on the ground. ## What the real conversations sound like Off LinkedIn, the teams we talk to describe something much less triumphant: - "Our agent abused an enrichment and we got a large bill." - "We sell too many different things, and the agent cannot handle the variability." - "It sounds smart, but it does not actually understand our ICP." - "Everyone was excited to build it. No one wants to maintain it." - "Our reps tried it for a week and then went back to what they were doing before." None of that means the technology is fake or that AI has no place in sales. It clearly does. It means the finished, effortless version being sold in the feed is not the version most teams are living. ## The real lesson The attention around AI in sales is real. The results are quite a bit harder to find, largely because most of the loudest voices are selling the dream rather than living it. When you evaluate any of this, weight the practitioner who shows you their actual workflow over the post engineered to get you to comment. The gap between the two is where most of the disappointment comes from.

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