Fractional growth, run as revenue
AI SDR Consultant: Autonomous Outbound Agents That Scale Pipeline
AI SDR / Fractional Growth
- Elementor100x$200K to $20M ARR as acquisition lead
- Riverside+337%MRR growth driven as a growth operator
- Across engagements$100M+ad budgets managed across paid social and search
What an AI SDR Consultant Actually Builds
An AI SDR tool is one part of an outbound workflow. I personally design, build and manage the agreed research, targeting, message, sequencing and CRM handoff work. The useful result is a relevant conversation and a handoff the receiving seller can accept. The scope identifies what I implement, which routine actions are authorised and which prospecting, meetings or follow-up your assigned SDRs own.
I do not sell you software. I build the pipeline machine the software runs inside: data, targeting, offer, sequencing, and the human handoff.
I am Yaniv Goldenberg, a Fractional CMO (Fractional Head of Growth). I have managed $100M+ in budgets, and I treat outbound the same way I treat paid media: as a measurable revenue channel, not a spray of activity. An AI SDR consultant who only counts sends is selling you the wrong metric. I count booked meetings, meeting-to-opportunity rate, and pipeline value per domain. Those are the numbers that pay your sales team and keep your CFO calm.
The work starts with your ideal customer profile, not your email copy. I rebuild your target list from firmographic and intent signals, strip the dead accounts, and segment by the specific pain each tier feels. AI writes faster when it writes to a tight segment. A generic prompt produces generic email, and generic email gets ignored. Tight inputs produce sharp outputs. That is the whole game, and it is where most AI SDR deployments quietly fail before the first send.
I implement the agreed sender configuration, authentication and monitoring with the account owners. I check the relevant provider requirements, permitted audiences, suppression rules and sending behavior before an authorised launch. Authentication and a gradual rollout do not guarantee inbox placement or a response. For Gmail-specific requirements, see the Google email sender guidelines. I have worked on sender problems; each actual failure still needs its own source and delivery evidence rather than a universal six-week timetable.
I personally build the agreed account-research, source-grounded drafting and reply-routing workflow. We define permitted sources, recipient and suppression rules, approved product claims, routine actions and human decisions before use. Ambiguous replies, missing facts and failed tools route to the named owner; measure review and correction effort rather than assume an 80/20 staffing split.
I have done this scale work in product growth too. I took Elementor to 100x ARR by treating every channel as a funnel with a fixed conversion math, and the same discipline applies to outbound. For outbound measurement, retain the eligible account population, the date window and the observed counts at each stage: delivery, reply, suitable reply, held meeting, accepted opportunity and closed revenue. Rates need their denominators; multiplying rates alone does not give a revenue total. Compare cost and quality by the agreed segment before deciding what to change.
We agree the first segment, build milestones and review window around source access, system readiness and delivery capacity. I implement and test the agreed workflow, then review actual delivery, replies, held meetings and sales acceptance before deciding whether to expand. A working build is not a promise of replies, meetings or pipeline within a fixed period.
Frequently asked questions
What does an AI SDR consultant do that an AI SDR tool does not?
Software capabilities vary. I build the work around the tool: account selection, permitted sources, message and exclusion rules, integrations, checks and the receiving seller’s acceptance process. We choose what to configure or build from the actual task and stack, then assess the agreed workflow separately from commercial results.
How do you keep AI outbound from killing my domain reputation?
I personally configure and test the agreed authentication and monitoring with the account owners against the actual destination-provider requirements. Gmail distinguishes all-sender and bulk-sender requirements. We define permitted audiences, suppression, message review, volume limits and failure handling; configuration alone does not guarantee inbox placement.
How much personalization can AI realistically handle in cold email?
More than a generic merge field, less than a fully human-researched note, if you feed it tight inputs. I configure the model to research each account and draft a first line referencing a real trigger like a hire, funding round, or product launch. The AI drafts; a human approves the offer logic. Sharp segment inputs produce sharp output. Generic prompts produce ignored email.
What numbers should I track instead of emails sent?
Track the eligible account population, actual delivery, suitable replies, held meetings, sales-accepted opportunities and closed revenue over a stated window. Retain the denominator for each rate and include research, tools, review and repair in the operating cost. A booked meeting is not a held meeting or accepted opportunity, and a product of rates alone is not a revenue total.
How long until an AI SDR system produces real pipeline?
The schedule follows the segment, available data, system access and agreed build. I define milestones for implementation and acceptance, then a separate observation window for delivery, replies, held meetings and opportunities. A completed workflow does not guarantee meetings or pipeline by a fixed date. Expansion follows observed fit, quality, cost and the client’s decision.
Choose the operating model around the workflow
AI SDR products differ in how much they let a team inspect, configure and operate. Before choosing a subscription or a custom build, check targeting inputs, source rights, suppression, message review, permitted sending actions, CRM handoff, operating costs and what the team retains. I can implement the agreed workflow around existing tools or client-controlled infrastructure.
Choose a product or custom build by the workflow it supports, the controls it exposes and the work your team can retain and operate.
The version that works treats AI as the research and drafting layer inside a system you control, not an autonomous spam cannon. Agents handle the heavy lifting: pulling account signals, personalizing the angle, sequencing the follow-up. A human reviews quality and approves the messages that need judgment. You get the leverage of automation without surrendering the relevance and reputation that make outbound work at all.
What I build, and how it fits a fractional engagement
An AI SDR system is one capability inside a growth engagement, not a standalone product I sell you a seat to. As your fractional head of growth I make sure the agents target the right ICP, feed the same pipeline as your other channels, and stay measured against real opportunities. The system is built on infrastructure your team owns and can extend.
Research agents
Agents that pull account signals, enrich contacts, and assemble the context a good rep would gather before reaching out.
Drafting agents
Claude-powered drafting that personalizes each message to the account's real signal, not a mail-merge token.
Human in the loop
Approval gates so a person reviews quality and judgment calls before anything sends. Automation does the volume, humans guard the standard.
Deliverability and orchestration
I personally build the agreed n8n sending and sequence workflow with explicit permissions, suppression, monitoring, pause and failure routes. Verify actual provider conditions and receipt; the build does not guarantee reputation or continuous delivery. See n8n automation.
I run my own business on this exact stack
This is the strongest reason to build with me rather than rent a closed AI SDR tool: I dogfood the stack. My own marketing operation runs on n8n for orchestration, the Claude API and Claude Code for agent work, and Postgres for the data layer, with self-hosted deployment I control. I build and debug AI agents on this stack daily for content, research, reporting, and AI-search monitoring. When I build your AI SDR system, I am handing over patterns I depend on in production, not a demo I assembled for a sales call. The build story behind one of these agents is on the Claude Code SEO and GEO skill page.
How I keep AI SDR systems effective and safe
Signal-driven, not volume-driven. The agents act on the same signal-based logic as good human outbound: fewer, better accounts with a relevant reason to reach out now. See outbound.
Human approval where it counts. Define recipients, source permissions, product claims, routine actions and consequential decisions before use. Missing evidence, ambiguous replies, exclusions and failed tools route to the named owner. Test those routes and measure review effort; human approval does not certify every output.
Owned infrastructure. I build the agreed workflows in the selected stack and document client-controlled configuration, source permissions, credentials, dependencies, licensing, provider charges and maintenance. Portability can be useful without implying zero lock-in or no recurring costs.
Deliverability discipline. AI does not get a pass on reputation. Separate domains, authentication, warmup, and capped volume apply exactly as they would for human-run outbound.
Measured on pipeline. Success is qualified opportunities, not emails sent. The agents are tuned to reply quality and meeting-to-pipeline, and I cut what does not convert.
I personally build and manage the agreed SDR programme and automation; your assigned SDRs conduct prospecting, meetings and sales follow-up. We approve source permissions, product facts and message rules before use. Workflow review checks drafts, exclusions, reply routing and failures in the agreed environment. A working handoff is an implementation result, not a promise of meetings or pipeline.
When an AI SDR system is right, and when it is not
Right when
Outbound already fits your model, you have a defined B2B ICP, and you want to scale the research and drafting load without proportionally scaling headcount, while keeping a human on quality.
Wrong when
Outbound itself is not your channel, or you expect a fully autonomous robot with no human oversight. Hands-off AI outbound at volume is how domains get burned and brands get hurt.
The honest call
If you do not yet have outbound working with humans, I will tell you to prove the motion first. AI scales a system that works; it does not rescue one that does not.
Why trust me to build this
I combine growth-operator experience with a stack I run myself. I led acquisition at Elementor from roughly $200K to over $20M ARR as it passed five million users. I led growth at cnvrg.io, an MLOps platform built for exactly the technical B2B buyer AI SDR systems serve, ahead of its acquisition by Intel announced in November 2020 (TechCrunch). I drove +337% Net MRR growth at Riverside. And I build AI agents on n8n and Claude every day for my own business. See the cnvrg.io case study and AI marketing.
Frequently asked questions
What is an AI SDR, really?
A system of agents that research accounts, draft relevant outreach, and manage sequences automatically, with a human approving the work that needs judgment. It is leverage on the research and drafting load, not a fully autonomous robot.
How is this different from AI SDR SaaS products?
A product may be the right choice when its supported workflow, controls and costs fit the task. A custom build is useful when you need specific sources, review rules, integrations or retained configuration. I compare those needs before recommending the implementation. Tool access and a fluent draft do not by themselves establish approved outreach or qualified pipeline.
Do you actually use this stack yourself?
Yes. My own marketing runs on n8n, the Claude API, Claude Code, and Postgres. I build and debug these agents in production daily, so the patterns I hand over are proven, not demoware. See the Claude Code skill.
Is there a human in the loop?
Yes. We define the approved sources, recipients, exclusions, message rules and routine actions before use. Ambiguous replies, missing evidence, failed tools and consequential decisions go to the named reviewer. We test those routes and measure review effort; human approval does not guarantee every output or commercial result.
Will AI outbound hurt my domain?
It can create reputation and delivery risk. I check the permitted sources and audiences, sender setup, message quality, suppression, actual delivery feedback and the agreed operating limits. Authentication or a careful rollout cannot guarantee inbox placement or prevent every rejection; uncertainty and failed sends stay visible.
Do I need outbound working first?
Ideally yes. AI scales a motion that already works. If outbound is not yet proven with humans, I will tell you to validate it before automating. See outbound.
Who owns the system?
Ownership and access follow the actual agreement and tool licences. I document the client-controlled workflows, source permissions, credentials, dependencies, provider charges, maintenance and handoff checks. A custom build can preserve useful configuration and data without implying zero lock-in or no recurring costs.
How much does this cost?
Diagnostic, infrastructure and embedded Operator pricing depend on the engagement. Scope, time commitment, delivery milestones and contract terms are agreed before work starts.
Do you have capacity to take on a new AI SDR client right now?
I agree the scope, time commitment and availability for each client or project before work starts. The way to find out where things stand is to send me a message or book a 15-minute call. When you contact me directly, I read every message myself and reply within one day.
What is included in an AI SDR build?
The build covers research and drafting agents, a human-in-the-loop workflow, deliverability infrastructure, and team handoff and docs. The research agents pull account signals, enrich contacts, and assemble the context a good rep would gather before reaching out. The drafting is Claude-powered and personalizes each message to the account's real signal. Approval gates mean a person reviews quality and judgment calls before anything sends. Pricing for the build depends on build scope.
Is there a minimum contract length for an AI SDR build, and how do I end it?
There is no minimum term. Engagements are month-to-month, and either side can end the engagement with 30 days notice. If the AI SDR system runs as part of a fractional engagement, ending it involves no severance. Contract terms are agreed before work starts.
Pricing
I personally investigate and build an agreed AI SDR workflow, hand it over or continue its operation within an Operator scope. Advisory is the separate advice-only offer. Sending, recipient selection and other actions require their actual agreed permissions; a diagnostic or build does not itself authorize outreach.
Diagnostic sprint
Fixed $6,000-$8,000typical range; scoped per engagement
An audit of your growth stack on an agreed timeline, plus a 90-day roadmap. Fixed scope; any follow-on retainer is agreed separately.
AI Marketing infra
From Pricing and terms agreed for the engagementtypical range; scoped per engagement
- Research and drafting agents
- Human-in-the-loop workflow
- Deliverability infrastructure
- Team handoff and docs
Operator (embedded)
Pricing and terms agreed for the engagementtypical range; scoped per engagement
Full fractional role with the AI SDR system owned alongside the rest of growth. See fractional CMO.
Build an AI SDR system you own, not one you rent
Book a 15-min call. I will tell you whether an AI SDR fits your motion, what to build on your own stack, and where a human still needs to stay in the loop.
Explore consulting specialties
What an AI SDR consultant builds that a tool license cannot
A software license does not by itself define a useful outbound workflow. I personally build the agreed targeting, source checks, message and review rules, integrations and seller handoff around the chosen tools. The implementation earns its place when the receiving team can use and maintain it; the commercial result still needs observation.
I start with the signal layer, not the send. An AI SDR consultant who has run growth knows that the account list is the campaign. Funding rounds, new hires in the buying role, tech-stack changes, job posts that imply a pain you solve: those are the triggers that make outbound feel timely instead of random. Clay enriches and routes; the agent only fires when a real reason exists.
The second build is deliverability discipline. Autonomous volume kills domains when nobody is watching. I separate sending domains from your primary, warm them properly, cap daily volume per inbox, and watch reply and spam rates like a dashboard, not a quarterly report. An AI SDR consultant who skips this hands you a burned domain and a tool subscription.
I keep the first-touch angle, product claims and consequential reply decisions with the owner defined for the engagement, while building the research, drafting and routine processing that can run within agreed permissions. We measure the actual review and correction workload before claiming a staffing saving.
The last piece is honest measurement. Booked meetings that show up and turn into pipeline, not opens and clicks. I wire the replies back to the CRM, track meeting-held and opportunity rates by segment, and kill the segments that look busy but never close. If a list is not producing, an AI SDR consultant says so and reallocates instead of defending the spend.
If you want an AI SDR consultant who treats autonomous outbound as a system to engineer rather than a button to press, that is how every engagement runs.
Fractional CMO by industry and stage
- Fractional CMO for B2B SaaS
- Fractional CMO for Pre-Seed Startups
- Fractional CMO for Cybersecurity
- Fractional CMO for Fintech
- CMO as a Service in Israel
Account research that a seller can use in the next conversation
The same agreed research workflow can prepare an assigned inbound account, existing-customer discussion or sales meeting as well as an outbound brief. I personally build the permitted CRM/public-source connections, source-linked account context, evidence-gap record and handoff. The output names what is known, what is inferred and what the seller still needs to ask.
- Bind each claim to the correct account or contact, its permitted source and observation date.
- Prepare buying-context notes, discovery questions and reviewable personalization without inventing a trigger.
- Save agreed CRM context or tasks and confirm who receives them; contacting the person remains a separate approved action.
For the incoming identity and assignment itself, use CRM automation. The useful account brief supports a seller decision; it does not certify qualification, deliverability or future pipeline.
Next step
Let's turn this into measurable revenue
Book a 15-min call. I will tell you whether this is your next move, or whether your money is better spent elsewhere.