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.

Why AI Sales Tools Built for SaaS Fail Services and Consulting Sellers

Most AI go-to-market systems are genuinely good for product companies. In consulting and staffing, they are often close to useless. The reason is structural, not a matter of tuning. When you sell the same thing every day, agents and automations are straightforward to dial in. One offer, one buyer profile, one motion. You can encode that once and let it run. ## The services problem Now picture the person supporting a services team. In a single day, they might need to help sell: - Data governance to a healthcare company. - ERP project management to an automotive manufacturer. - A cloud migration to a fintech startup. - Payroll services to a construction firm. - Organizational change to a retailer. - Engineering staff to a government contractor. Each of those is a different value proposition, a different buyer, a different set of signals that indicate a good fit. A tool built on the assumption of one product to one buyer has no idea what to do with that. Back to square one, six times a day. I felt this directly when I sold in consulting. My value proposition changed depending on the offering, the industry, and the buyer persona in front of me. A tool that could only hold one of those at a time was not helping; it was one more thing to fight. ## Fit is offer-relative The deeper issue is that in services, "good prospect" is not a fixed property of an account. It depends entirely on which offer you are bringing. A company modernizing its systems might be a strong fit for ERP implementation work and a weak fit for something else you sell. Static, one-size scoring cannot express that. The account has to be evaluated through the lens of the specific thing you are selling right now, and that lens changes from deal to deal. ## What this means in practice If your team sells services and your tooling was built for SaaS, the tools are quietly working against you, forcing a many-offering business into a one-product mold. It is worth an honest audit: does your stack understand that the same account can be a great prospect for one offer and a poor one for the next? If it cannot, that is why it feels like it is fighting you.

Put this into practice