Machine learning lead generation that fills your pipeline with companies ready to move models into production.

Outreach starts in week one. List build, messaging, and account research finish during onboarding so day one of the engagement is dialing day — we don’t ramp by quarter, and AI buying windows close too fast for that. First qualified appointments typically land within the first few weeks, with steady weekly cadence as the campaign matures. Your AEs see meetings on the calendar before most agencies have finished writing their kickoff deck.

Three-point standard, and all three must be met. Verified pain — the prospect has named a specific initiative, a stalled POC, or a model that needs to reach production, not just expressed interest in “doing more with AI.” Decision authority — the CTO, Chief Data Officer, or VP of Data is on the call, or one of two people who can sign. Active timeline — they’re evaluating in the next 90 days, not someday. Anything short of all three doesn’t make your AE’s calendar.

A database sells you names and firmographics; it can’t tell you whether the company has data without a model in production, or whether the budget is real. We qualify by conversation. Our SDRs confirm the project, the stakeholder, and the timeline by phone before a meeting is booked — so what reaches your calendar is a buyer with a problem, not a row in a spreadsheet. Lists are a starting point. Qualified pipeline is the deliverable.

We sequence engagement by stakeholder. Data engineering and the technical buyer enter first — they assess feasibility and data readiness. The Chief Data Officer or CTO sets strategic fit; procurement and finance enter last, at commercial terms. Our SDRs stay involved through the nurture phase so the handoff to your AE doesn’t reset the relationship. A nine-month evaluation can’t survive being thrown over a wall at month three; our model is built so it doesn’t have to.

ML and AI consultancies, MLOps and data-engineering vendors, computer-vision and NLP specialists, and AI platform companies — typically firms selling technical engagements into mid-market and enterprise accounts. The common thread isn’t your size, it’s sales infrastructure: you need solutions architects or AEs who can scope and close a technical deal once qualified meetings hit the calendar. If you’re earlier-stage without that motion in place, we’ll tell you upfront.

Onboarding runs five to ten business days from kickoff. We use that window to build the target account list, finalize your messaging, train SDRs on your offer and technical category, and integrate with your CRM. Most kickoff work compresses into two or three short working sessions — your time stays minimal. Messaging gets iterated weekly during the first month based on real call data — what’s landing with data leaders, what isn’t, where to adjust. The point is to sound like your team, not like another agency reading “AI” off a deck.

Yes — that targeting is the edge. We layer cloud platform (AWS, GCP, Azure), data-warehouse stack (Snowflake, Databricks, BigQuery), ML and data-engineering job postings, recent AI funding, and public signals of a stalled or scaling initiative on top of firmographics. The output is a tight target list of accounts that actually have data and a reason to buy — not a generic “tech companies” pull from a list vendor. Sloppy targeting is the single biggest reason outbound campaigns underperform. We fix it before week one.

Monthly retainer, scoped to your target volume, account complexity, and cycle length. Larger enterprise targets and longer technical evaluations cost more to engage; smaller mid-market plays cost less. We don’t publish a rate card because each firm’s offer and target market look different, and a flat price would either overcharge half our clients or undercharge the other half. Book the assessment and you’ll get a written scope and quote within 30 minutes of the call.