Building a new list for every campaign is killing your outbound.

We build signal-to-sequence data pipelines. Infrastructure that watches public buying signals — LinkedIn posts, job postings, hiring activity — evaluates each one with a multi-layer AI qualification system, resolves the right decision maker at the target company, and enrolls that person in your outreach sequences automatically.

The output isn't a list. It's a qualified, enrolled contact with a full reasoning trail attached. You can see exactly why that person was selected, what signal triggered it, and when they entered your sequence.

We run this as a fully managed service. You don't need a RevOps hire, a GTM engineer, or an implementation project. We build and operate the pipeline on your behalf.

No. Agencies build campaigns that end when the contract does. We build infrastructure that compounds.

An agency books you meetings by running a campaign. We build the system that means your reps show up to every conversation with a reason: a specific signal, a specific company fit, a specific person. Not a guess. Month three looks different from month one because the AI qualification layer has been tuned against real data from your pipeline. That doesn't happen with an agency retainer.

No. AI SDR platforms write better outreach messages. That's a copywriting problem.

We use AI to answer a different question before copy is ever written: should this contact be in a sequence at all?

The qualification runs before anyone writes a word. The AI reads the signal, evaluates company fit, reads the person's actual profile language, and writes down why they qualify or don't. Every decision is auditable. If the reasoning is wrong, we fix the prompt. An AI SDR has no idea why it's sending what it's sending. It just sends.

B2B companies — primarily SaaS and cybersecurity — with 50 to 1,000 employees, a proven offer, and a targeting problem. You have a working sales motion. Your pipeline quality is inconsistent. You've tried Apollo. You ran an AI SDR pilot. You're tired of rebuilding from scratch every quarter.

This is not for pre-PMF companies still figuring out what they're selling. The pipeline we build requires a clear ICP. If you don't have one yet, we can't help you find it. We can help you act on it at scale once you do.

Intent data platforms like Bombora and 6sense monitor what companies are reading across publisher networks and infer buying interest from content consumption. The output is a ranked list of companies that appear to be researching topics relevant to your product. No person-level resolution. No outreach execution.

There are two core problems with this approach.

First, the signal is inferred. A company reading articles about SDR hiring tools might be a competitor doing research, one junior employee doing background work, or your own team. Multiple G2 reviewers note the platforms work well for the first one to two months, then signal quality drops as reps realize the scoring isn't translating to pipeline.

Second, the platform stops at the company. You still don't know who to call.

We use declared signals: LinkedIn posts where someone announces they're building an outbound team, job postings that show a company actively hiring SDRs, hiring patterns that indicate a company in a specific growth phase. The intent is explicit, not inferred. And the output isn't a company score. It's an enrolled contact with a full reasoning trail.

A declared signal is one where the organizational intent is explicit and public.

A VP of Sales posting on LinkedIn: “We're building out our first SDR team and looking for tools to support them.” That's a declared signal. The company isn't browsing articles about SDR software. They're publicly announcing they're in the market.

A job posting: “We're hiring 3 BDRs to support our expansion into the mid-market.” That's a declared signal. The company is committing budget to building an outbound team. That's not a content consumption pattern. It's a business decision happening in public.

Declared signals are stronger than inferred signals for two reasons. They're harder to fake because they require actual organizational action, and they're specific enough to evaluate because we can read the actual post or job description and assess company fit from the language itself.

Yes, for publicly available data. Courts — including the Ninth Circuit's hiQ v. LinkedIn ruling — have consistently held that publicly accessible LinkedIn data is fair game for automated collection under the Computer Fraud and Abuse Act. We only collect data that any user could read without logging in.

We don't access private messages, connection data, or any information behind a login wall. The posts and job listings we monitor are public by design. They're announcements the company or individual chose to make to the world.

We're honest about this because we believe outbound should be transparent about how it works. If a question about data sourcing comes up in your sales conversations, you should be able to answer it confidently.

Apollo and ZoomInfo are contact databases. Their job is to give you a list of people who fit a filter — title, industry, company size — so you can build sequences. They start from contacts and work backward toward a reason to reach out.

We start from a signal — a specific moment of declared intent — and work forward toward the right person.

The failure mode for Apollo is coverage and signal depth. Apollo's contact coverage in many B2B verticals is 30% or less. Their intent signals are derivative third-party bidstream data — the same pool Bombora draws from, but without the same quality controls.

The failure mode for ZoomInfo is cost and scope. A ZoomInfo contract for two seats with intent data runs $18,000 to $38,000 per year. It still stops at a list of accounts. The output is a prioritized target, not an enrolled contact.

Our people resolution runs a waterfall: Apollo for org and department intelligence, then LinkedIn enrichment for direct profile data, then phone verification as a dedicated step. We use Apollo as one layer in a multi-layer system. And because every qualified contact has a signal attached, your reps aren't calling from a cold list. They're calling from a reason.

Clay is a data enrichment and workflow orchestration platform. It gives access to 150+ data providers, waterfall enrichment, and AI-powered research. It's excellent, and our architecture draws on similar principles.

The difference is who runs it.

Clay requires a GTM engineer: someone who can build custom workflows, configure enrichment waterfalls, write AI research prompts, and maintain the system as data sources change. Most companies at 50 to 200 employees don't have that person.

We're Clay's underlying logic, fully managed, with a qualification AI layer on top that Clay doesn't have. Clay enriches contacts. We decide which signals are worth acting on before enrichment runs, and we maintain the qualification logic month over month as your ICP understanding evolves.

The buyer who can staff and run Clay doesn't need us. The buyer who needs us doesn't have a GTM engineer. Different buyers entirely.

Amplemarket is the closest direct comparison in terms of market position: signal-triggered outreach for growth-stage companies, multichannel sequences, AI-powered messaging.

The deepest difference isn't features. It's philosophy.

Amplemarket's AI decides what to say. Our AI decides who deserves to be contacted in the first place. Amplemarket doesn't ask whether a signal should trigger outreach. It fires the sequence. Whether the contact was worth contacting is never asked.

A second difference is attribution. Amplemarket tracks sends, opens, replies, and connections at the campaign level. It does not store why a contact was qualified, what signal produced a reply, or which prompt logic is underperforming. If your reply rate drops in month four, you can't ask the system why. We can.

Amplemarket is the right tool for a sales rep who wants to run their own outbound motion and is willing to configure everything themselves. We're the right infrastructure for a company that wants signal-to-enrolled-contact running without an operator on staff.

Outsourced SDR agencies work at human speed. They're capped by the number of hours their reps can work. They don't compound. When the contract ends, the motion ends.

A typical outsourced SDR arrangement runs $5,000 to $10,000 per month for a dedicated rep. At that cost, you're getting manual list building, manual research, and human outreach pacing. The quality is inconsistent because it depends on the individual.

Our pipeline monitors roughly 15,000 signals a month and adds around 400 qualified contacts in the same period. It runs at night. It runs on weekends. It doesn't take PTO. And it gets better each month as the qualification prompts are tuned against real reply data.

We're not replacing a rep's ability to build relationships. We're replacing the bad part of their job. Nobody went into sales to build spreadsheets at midnight. We do the targeting. They do the conversations.

Most tools that claim AI-powered outbound use AI to generate better email copy. We use AI to answer a harder question before copy is ever written: does this signal represent a real buying moment, at a company that fits the ICP, for a person worth contacting?

The qualification runs in three layers.

Post-level qualification.When a LinkedIn post is scraped, an AI layer reads the full text and writes a qualification decision with reasoning. Not a keyword match. A written explanation: “This post discusses the company's intent to build an outbound SDR team and specifically references tool evaluation. Qualified.” That reasoning is stored with the record permanently.

Company-level qualification. Enriched company data is evaluated against your ICP criteria before any person spend is triggered. Headcount, industry, employee growth, funding stage — all checked before we run a people search.

Person-level qualification. Once a decision maker is identified, their profile language is read and evaluated. Are they actually in a buying-relevant role? Does their career history suggest a fit with your offer?

Every contact that enters your sequence has passed all three layers. Every contact that doesn't has a written reason on file.

Yes. That's one of the fundamental design principles.

Every qualified contact has a full reasoning trail: the signal that triggered evaluation, the AI's written reasoning at each layer, the company data that confirmed fit, and the person data that confirmed role. You can query this through our Slack interface in plain English.

This matters for two reasons. First, it lets you catch bad decisions before they compound. If the AI is qualifying contacts you wouldn't have selected, you can see why and fix the prompt. Second, it gives your reps context for every outreach. They're not calling blind. They're calling with a reason.

Yes, but not automatically. This is an important distinction.

The system doesn't magically get smarter. We get smarter by examining the reasoning. When a contact gets a reply, we look at what signal produced it and what the AI said about that contact. When a contact doesn't respond across three touch points, we look at whether the qualification reasoning held up.

That examination informs prompt updates. Month three looks different from month one because we made it different, not because the AI figured it out on its own.

The market is full of tools that promise self-improving AI. What that usually means is they're training on aggregate behavior across all their customers, not on your specific data. We tune prompts on your ICP, your signal types, your reply data. The system gets smarter about your problem, not the average customer's problem.

We ran this pipeline on our own sales motion before building it for clients. Starting from a cold LinkedIn account and zero outbound history: 320 replies in 117 days at a 55% reply rate. The industry benchmark for LinkedIn outbound is 10 to 20%. We ran at 2 to 3× that.

That number reflects a well-tuned pipeline with a clear ICP. Month one won't look like month four.

What to realistically expect in month one: pipeline is live, signals are flowing, first contacts are enrolling. Qualification prompts are being calibrated. Some false positives will come through — contacts we enrolled that, in retrospect, weren't right. You'll flag them. We'll use them to tighten the criteria.

By month three: qualification accuracy is substantially higher. Reply rates are tracking. You have a full signal-to-reply attribution chain and can tell which signals are producing conversations.

It means we can show you, for every contact in your pipeline, the exact signal that triggered their evaluation, the reasoning that qualified them, the date they were enrolled, and whether they replied.

Most outbound tools track what happens from first contact forward. We track from signal backward. The chain looks like:

Every link in that chain is stored and queryable. If a contact booked a meeting, you can trace exactly what public signal started the process six weeks earlier. If reply rates drop in a specific signal category, you can identify which category is underperforming without guessing.

Onboarding typically takes two to four weeks depending on your tech stack and the complexity of your ICP. We need access to your LinkedIn sender profile, your outreach sequences (or help building them), and a clear definition of the company signals and person profiles you're targeting.

In that window, we're building the signal detection rules, configuring the qualification prompts against your ICP, and connecting to your sequences. The first contacts start flowing into your pipeline before onboarding closes.

Clarity on ICP and a point of contact for feedback.

The ICP clarity is critical. “B2B SaaS companies” is not an ICP. “B2B SaaS companies with 50 to 200 employees, in a growth phase indicated by active SDR hiring, selling to enterprise accounts” is an ICP. The more specific, the faster we can calibrate the qualification layer.

The feedback loop is what makes the system improve. When you see a contact in your pipeline and think “this person isn't right,” we want to know. That feedback is the training data for prompt updates. The best clients treat the first 60 days as a collaborative calibration exercise, not a set-and-forget deployment.

Three tiers based on the number of LinkedIn sender profiles in your outbound motion:

• ◆Core: $2,000/month, 1 LinkedIn sender profile
• ◆Growth: $3,500/month, 2 LinkedIn sender profiles
• ◆Scale: $5,000 to $6,000/month, 3 LinkedIn sender profiles

One-time onboarding: $3,000 to $8,000 depending on stack complexity. Three-month minimum on all tiers.

Tool costs are absorbed into the retainer at current scale for most clients. No credits. No overages. No surprise renewal quotes.

Yes, and this is actually common.

We use Apollo as one layer in our people resolution waterfall — specifically for org and department intelligence. You don't need to cancel your Apollo subscription to work with us. We integrate with it.

What changes is how it's used. Instead of a rep manually pulling lists and building sequences from Apollo exports, Apollo becomes one automated step in a pipeline that runs without human intervention. The rep still works the conversations. They just don't build the lists anymore.

Then we should talk before signing anything.

If outbound isn't working, the problem is usually one of three things: bad list quality, wrong signal, or wrong messaging. We fix the first two. We help with the third but we don't ghostwrite your reps.

If the problem is that your offer isn't resonating at all — if you don't have product-market fit and you're hoping outbound will find it — we're not the right call yet. Come back when you have a clear ICP and a proven close rate, even if volume is low. That's when infrastructure helps.

No. And we take this question seriously.

Every contact we enroll was triggered through a specific, declared signal: a public post or job listing that indicated real organizational intent. We're not blasting every VP of Sales in a database. We're contacting people at companies that publicly announced they're in a buying moment relevant to your offer.

That distinction matters for deliverability, for reply rates, and for the kind of business we want to run. We believe if you know you can solve someone's problem and you don't reach out, that's a disservice to them. But reaching out with a reason is different from reaching out with a list.

The AI qualification layer exists precisely to prevent the list-blast dynamic. Every contact passed three layers of scrutiny before a single message was sent. Some contacts are disqualified. That's the point.

It will sometimes make bad calls. That's the honest answer.

No qualification layer is perfect, especially in the first 30 to 60 days when we're calibrating against your specific ICP. Some contacts will enter your pipeline that shouldn't have.

What makes us different from every other tool in this market is what happens next. Bad calls have written reasoning. You can read exactly what the AI said. You can flag the contact. We examine the reasoning, identify where the logic broke down, and update the prompt. The same mistake is unlikely to repeat.

No. Anyone who guarantees specific pipeline numbers in outbound is selling you something.

What we commit to: a pipeline that runs, qualification reasoning that's auditable, prompt tuning based on real reply data, and transparency about what's working and what isn't. If something isn't working, we say so and we fix it. We measure results quantitatively and share the data with you.

Our success correlates directly to yours. Clients who see pipeline stay. Clients who don't, leave. That alignment is the closest thing to a guarantee we can honestly offer. Our incentives are exactly the same as yours.