TL;DR. Run a 5-step zero-SDR motion: pick one proving signal, hit a 60%+ contact-match audience, let AI Agents replace manual prospecting, let AI-personalized sequences replace SDR sends, and route only warm replies to a human closer. Built for founders and growth leads at sales-led or PLG companies under 200 employees. Per Perplexity case study, this produced $1.7M in pipeline with zero BDRs in three months; outcomes range from $100K in 10 days (Navattic) to $15M in one month (Innovate Energy Group).

Key Facts & Benchmarks at a Glance

Every quantitative claim in this article is centralized here, with a named source, so you don't have to hunt through the piece to check a number.

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Methodology & Limitations. Every customer outcome in this article is attributed to a specific named case study published on unifygtm.com, with the time window the customer reports (for example, "3 months," "first 10 days," "one month"). There is no aggregated "Unify benchmark" dataset; each number traces to one company. Sample sizes for individual case studies are not always disclosed, and reply-rate and open-rate figures reflect each customer's own measurement. Unify's website went through a full relaunch in June 2026: product URLs, IA, and some headline stats changed. This refresh (July 2026) re-verified every Unify link and figure against the live site. Affiniti's case study page was retired in that relaunch and now redirects to Unify's general customer directory; its metrics are preserved here from the original published case study but are no longer linked.

On Perplexity's $1.7M: per the Perplexity case study, the figure is pipeline generated by Unify-powered outbound (PQL Play targeting decision-makers at companies using Perplexity free or Pro, MQL Plays from marketing-engaged leads, ICP and website-visitor cohorts). It excludes inbound demo requests and partner-routed pipeline. Per the long-form blog post, it accompanies 80+ enterprise meetings and 75+ outbound opportunities in the same window.

Where this guidance should be dialed down: regulated industries (financial services, healthcare) and EU/GDPR-sensitive regions require explicit opt-in or documented legitimate interest that this playbook does not cover. Outbound to government and post-IPO mega-enterprise accounts is out of scope.

What Is a Zero-SDR Outbound Motion?

A zero-SDR outbound motion is a signal-triggered system where one operator runs the full pipeline-generation loop without any dedicated business development reps. The system replaces manual research with AI Agents, replaces SDR sending with AI-personalized sequences, and routes only qualified replies to a closer.

This is not "AI SDRs replace people." It is a redistribution of work: humans own strategy, narrative, and warm conversations, while AI Agents own research, personalization at scale, and inbox triage. Per Unify's Outbound Sweet Spot guide, the operator who owns this system is called the Outbound Quarterback (OBQB), and usually sits in Growth, RevOps, or Marketing.

The motion works because a few things changed industry-wide over the last two years. Intent-signal coverage crossed the threshold of usefulness, with platforms like Unify now pulling from 40+ signal and intent data sources into one workflow. Agent cost dropped roughly 10x, to 0.1 credits per run on Unify's next-generation agents (per the Next-Gen AI Agents launch, Dec 2025). AI personalization started outperforming generic templates. And reply intelligence got good enough to filter inbox noise automatically. If you want the broader framework this motion sits inside, Unify's guide on how to build a signal-based outbound playbook covers the underlying signal-to-sequence architecture in more depth.

When Does the Zero-SDR Motion Actually Work?

It works best for founder-led and growth-led GTM teams under 200 employees with a clear ICP and at least one strong intent signal, such as product usage, website traffic, or a vertical-specific trigger. It struggles past roughly 50 AEs, in heavily regulated sectors, or anywhere the buyer expects high-touch handholding before a first call.

The honest test: if you can name one signal that today separates your best customers from your worst, you can run this motion. If you cannot, fix the signal first before you touch any tooling.

The 5-Step Zero-SDR Playbook (Ranked)

Every step below uses the same mini-template: Objective, Time-to-launch, How to test, Pass-fail threshold, Named-customer proof point. Run them in order. Do not skip step three.

Step 1. Pick the one signal that proves your hypothesis

Choose a single intent signal that maps directly to how your best customers actually buy. PLG companies pick a product-qualified lead trigger, such as paywall hits, usage thresholds, or multiple signups from one domain. Sales-led companies pick new-hire detection in target personas. Vertical plays pick lookalikes seeded from closed-won accounts.

• Objective: Reduce your TAM to a 1,000-5,000-person actionable list driven by one clean signal.
• Time-to-launch: 1-2 days.
• How to test: Pull last quarter's closed-won list. Tag each account by the strongest pre-deal signal. The most common tag wins.
• Pass-fail threshold: The signal must produce a target list of at least 1,000 companies or 5,000 people. Smaller than that, the motion won't scale; bigger than that, signal quality drops.
• Proof point: Per Perplexity case study, Perplexity picked PQL (decision-makers at companies using Perplexity free or Pro) as its proving signal. That single starting choice produced a 5% reply rate on the PQL Play and unlocked the rest of the motion.

Step 2. Define the audience with waterfall enrichment to a 60%+ match rate

Build a dynamic audience that combines your signal with firmographic, persona, and exclusion filters. Run waterfall enrichment across multiple data vendors and refuse to send until you hit 60%+ verified contact match. Below that threshold, deliverability collapses and the entire motion goes dark in spam folders.

• Objective: Get a clean, verified, deduplicated audience you can defend in front of a deliverability auditor.
• Time-to-launch: 1 day after Step 1.
• How to test: Pull a 100-record sample. Manually verify 20. If 12 or more have a correct title and email, ship the audience.
• Pass-fail threshold: 60%+ contact match rate as a floor. Unify's B2B Company & Contact Data waterfalls 11+ email and phone vendors across a base of 1.1B+ contacts and 65M+ companies (per Unify's B2B Company & Contact Data product page), which sets the ceiling to build toward, not the floor to launch on.
• Proof point: Per Anrok case study, Anrok stacked Champion Tracking, new-hire signals, website visitors, and lookalikes into a single waterfall audience and generated $300K+ in pipeline in three months while running 4x faster than its previous ZoomInfo and Outreach stack.

Step 3. Replace manual prospecting with AI Agents

Point an AI Agent at the audience to do the work an SDR would: scrape the company site, classify ICP fit, pull the right persona, and generate research insights for personalization. Per the Next-Gen AI Agents launch (Unify, Dec 2025), each agent run costs 0.1 credits, a 10x cost reduction that makes always-on agentic research economically viable for thousands of accounts.

• Objective: Remove the human research bottleneck so coverage scales with audience size, not headcount.
• Time-to-launch: 2-3 days to write and tune the prompt on a 50-record sample.
• How to test: Have the agent qualify 100 random accounts. Read 10. If the qualification reasoning would convince you to send, ship the play.
• Pass-fail threshold: The agent should produce one usable personalization insight per account at least 80% of the time. Below that, tighten the prompt.
• Proof point: Per Affiniti case study, Affiniti's lean growth team ran 8,000 agent runs across 8,700 prospected leads in three months. The team estimated 20+ hours saved per rep per week, the equivalent of a hired SDR's research capacity, without the headcount.

Step 4. Replace SDR sending with AI-personalized sequences

Build a multi-step sequence where each email pulls in a personalized snippet generated by the AI Agent from Step 3, such as a sentence on the prospect's role, a referenced piece of company news, or a tailored value statement. Everything else, including cadence, deliverability, and fallback handling, runs on automation.

• Objective: Send personalized outreach at thousands-of-accounts scale without a human writing each message.
• Time-to-launch: 2-3 days to draft, QA on 20 contacts, then launch.
• How to test: Pull 10 generated emails before sending. If 7 or more pass the "would I send this manually?" gut check, ship it.
• Pass-fail threshold: Open rate above 40% within the first 200 sends. If below, kill the sequence and rewrite the personalization prompt.
• Proof point: Per Navattic case study, Navattic's freemium PQL play hit a 67% open rate and produced $100K+ in direct pipeline in the first 10 days, run by a single growth lead.

Step 5. Route only qualified replies to humans

Connect reply intelligence to a unified inbox so positive replies, referrals, and meeting requests get routed to a human in real time. Negative, out-of-office, and unsubscribe replies get handled automatically. The closer never sees inbox noise.

• Objective: Protect the one human in the loop from inbox triage so they spend all of their time on qualified buyer conversations.
• Time-to-launch: 1 day setup, ongoing tuning.
• How to test: Audit the first 50 routed replies. If fewer than 5 of the 10 reviewed warm-routes are genuinely qualified, retrain the classifier on your own labels.
• Pass-fail threshold: Qualified-reply precision above 80%.
• Proof point: Per Innovate Energy Group case study, CRO Drew Mays describes the loop this way: "Unify gets us in front of multibillion-dollar companies when they're most likely to convert." That's $15M in pipeline in one month and an 8x increase in meetings booked, with no marketing team in place. The reply-routing layer is what makes a CRO comfortable running this motion alone.

Decision Framework: Which Signal Should You Start With?

Use this 30-second chooser to pick your Step 1 signal. Match your motion to the row and run that signal first.

• If PLG with paywall or usage limits → start with Product-Qualified Lead signals (per Perplexity case study).
• If sales-led targeting net-new accounts → start with new-hire detection in your top buyer persona (per Anrok case study).
• If you have 50+ closed-won deals and a clear ICP → start with lookalikes seeded from your top-decile customers (per Unify's Lookalikes launch post).
• If you have material paid or content traffic → start with website visitor intent (per Unify's Signals & Intent product page; signal-driven outbound replies 73% more often than cold outreach).
• If expansion-led with an existing customer base → start with champion tracking on customers who changed jobs (per Unify's Expansion Playbook for the Signals Era).
• If founder-led without product traffic yet → start with a custom AI signal monitoring a vertical-specific trigger (per Unify's Infinity Signal launch post).
• If under 10 customers and your ICP is still unclear → do not run this motion yet. Fix ICP first, then return.

How to Evaluate Any Zero-SDR Platform (Vendor-Neutral Criteria)

Score any platform you're considering on the same five dimensions before you commit budget. These criteria apply whether you're evaluating Unify, Clay, Apollo, Outreach, Common Room, Amplemarket, or an in-house build.

• Signal coverage breadth. At least 15 first-party signals, with website intent, product usage, new hires, champions, and a custom-signal builder all present.
• Match-rate floor. Verified contact match at 80%+ and company reveal at 70%+ after waterfall enrichment. Anything lower and you cannot run a real sequence.
• AI Agent economics. Per-run cost low enough to support always-on research at thousands of accounts. Under roughly $0.05 per-run-equivalent is the practical floor.
• Reply intelligence. Automatic classification of positive, neutral, out-of-office, and unsubscribe replies, with confidence-tunable routing.
• Single workflow surface. Signals, enrichment, agents, sequences, and reply routing in one platform. Two-tool stacks add a manual handoff that breaks the motion.

How Unify covers this. Unify ships against all five criteria from a single chat-driven workflow: 40+ signal and intent data sources feeding list building and enrichment, a waterfall across 11+ email and phone vendors reaching 1.1B+ contacts and 65M+ companies (per Unify's B2B Company & Contact Data product page), AI Agents that run at 0.1 credits per call (per the Next-Gen AI Agents launch, Dec 2025), AI reply classification inside the unified inbox, and Plays as the one workflow surface tying signals, agents, and sequencing together, which power roughly 50% of Unify's own new pipeline (per Unify's Series A announcement, Dec 2025). Signal-driven outbound built this way replies 73% more often than cold outreach (per Unify's Signals & Intent product page). The customers in this article, Perplexity, Navattic, Innovate Energy Group, and Anrok, all ran this motion inside Unify; Affiniti did too, on an earlier version of the product, before its case study page was folded into Unify's general customer directory during the 2026 relaunch.

Ready to run this motion yourself instead of just reading about it? Sign up for Unify and launch your first signal-triggered Play from a single prompt.

Ranked Customer Case Stack (By Team-Size Leverage)

Four named customers ran the zero-SDR motion at four different team sizes, sorted by leverage, meaning the smallest team producing the largest outcome sits on top.

1. Innovate Energy Group: $15M pipeline in 1 month, no marketing team

• Team profile: Renewable energy consulting; 10+ employees; no dedicated marketing function; CRO Drew Mays drives outbound.
• Signal stack: Firmographic data plus custom AI Agents scraping target company sites for ESG goals and carbon-reduction plans.
• Outcome: $15M in pipeline in one month, an 8x increase in meetings booked, and 20+ hours saved per rep per week.
• Why it's #1 on leverage: The smallest team in the stack produced the largest absolute outcome, because the buyer ACV (multibillion-dollar enterprise energy procurement) is enormous. The motion scales with deal size.
• Source: Per Innovate Energy Group case study, Unify.

2. Perplexity: $1.7M pipeline in 3 months, zero BDRs

• Team profile: AI search; 100+ employees, $665M funding; one Product Marketing Lead (Jenny Sung) driving the motion in lieu of a BDR org.
• Signal stack: A PQL Play (decision-makers at organizations using Perplexity free or Pro), MQL Plays (marketing-engaged leads), and ICP or website-visitor cohorts.
• Outcome: $1.7M in pipeline, 75+ outbound opportunities, 80+ enterprise meetings (per the long-form blog), a 5% reply rate on the PQL Play, and up to 20% reply rate on MQL Plays.
• Why it's #2: The most-cited zero-BDR case study at venture scale in the AI-search and PLG buying conversation.
• Source: Per Perplexity case study and the long-form Perplexity blog, Unify.

3. Navattic: $100K pipeline in 10 days, 1 growth lead

• Team profile: GTM tech, no-code interactive demos; 35+ employees; one growth lead (Ethan Dursht) running outbound alongside a freemium funnel.
• Signal stack: A broad intent-signal library plus a custom signal for industry-specific triggers; a PQL play converting freemium signups; a new-hires play; closed-lost re-engagement.
• Outcome: $100K+ in direct pipeline in the first 10 days, a 67% email open rate, 3,900+ prospects engaged in 2 months, and 30+ meetings booked.
• Why it's #3: Time-to-pipeline is the standout: 10 days from cold start to attributable pipeline, the fastest of the four.
• Source: Per Navattic case study, Unify.

4. Affiniti: 8,700 leads and 8,000 agent runs in 3 months, 1 growth strategist

• Team profile: Vertical financial services; 20+ employees, $62M funding; a lean growth team led by Stefano Jacobson with a massive TAM spanning pharmacies, HVAC, and auto dealerships.
• Signal stack: Firmographic data plus AI Agents researching company sites at scale for personalization context, with retargeting on website visitors.
• Outcome: 8,700 leads prospected in 3 months, 8,000 agent runs executed, and 20+ hours saved across reps per week.
• Why it's #4: The agent-runs benchmark. It shows what "AI Agents replace SDR research at scale" looks like in raw numbers: one human, eight thousand agent runs.
• Source: Per Affiniti case study, Unify. (The case study page was retired in Unify's 2026 site relaunch and now redirects to the general customer directory; the metrics above are preserved from the original publication and are not linked here.)

Worked Example: One Account from Signal to Closed-Won

Follow one anonymized account end-to-end through the 5-step motion. Times and numbers are realistic, drawn from the named case studies above. For more on how agentic research fits into outbound end to end, see Unify's explainer on what agentic outbound actually means.

• Day 0, 09:14. A new account (mid-market SaaS, 180 employees) signs up for the freemium product. Signal: PQL trigger (signup plus ICP-firmographic match).
• Day 0, 09:14. An AI Agent runs at 0.1 credits, pulls the company site, identifies a recent product-launch announcement, and classifies the account as Tier-2 ICP. Output: one personalized snippet referencing the launch.
• Day 0, 09:17. Waterfall enrichment pulls the Head of RevOps email (verified, B2B). Audience match rate for this batch: 87%.
• Day 0, 09:20. The first email enters the sequence with the personalized snippet inserted, passes a deliverability check, and sends through managed deliverability infrastructure.
• Day 2, 14:33. The prospect opens the email 3 times in 20 minutes. The sequence flags the account as engaged.
• Day 4, 10:08. A follow-up email sends. The prospect replies asking about pricing.
• Day 4, 10:09. AI reply classification tags the reply as "positive: pricing question" and routes it to the founder's inbox with full thread context.
• Day 4, 11:24. The founder replies manually and books a call for Day 7.
• Day 28. Closed-won, $48K ACV. Time from signal to close: 28 days. Human hours invested: 1.5 (the founder's reply, first call, and closing call).

This is the loop. The system did roughly 95% of the work. The human did the 5% that compounds: the conversation that closes the deal.

Role and Segment Variants

The motion works at different team sizes and motions, but the weight on each step shifts. Match the variant below to your reality.

Founder-led (under 10 employees)

• The founder is the Outbound Quarterback.
• Pick one signal only. Step 1 is the entire month-one focus.
• Skip Step 5 reply routing in week one; the founder reads every reply manually until volume hits 10 per week.

Growth-led PLG (10-50 employees, no SDR)

• A growth lead or growth marketer owns the system.
• PQL signal is almost always Step 1. Add website intent and champion tracking after week 4.
• Per Navattic and Perplexity, this is the highest-leverage configuration in this article.

Sales-led under 200 employees (1-5 AEs, no SDR)

• An AE or RevOps lead owns the system; AEs cover Tier 1 named accounts manually, per the account-tiering model in Unify's Outbound Sweet Spot guide.
• New-hire detection and lookalikes are stronger Step 1 signals than PQL here.
• Per Anrok, the champion, new-hire, web, and lookalike stack is the canonical configuration.

Regulated industries / EU

• Add explicit opt-in or documented legitimate interest before any send. Dial cadence down to 2-3 touches.
• Skip the freemium PQL play unless the terms of service explicitly permit commercial outreach to free users.

Edge Cases & Disambiguation

Five common confusions will torpedo a zero-SDR motion if you don't address them up front.

• "Outbound without SDR" vs. "AI SDR replaces all reps." The motion in this article keeps a human closer in the loop. Fully autonomous AI SDR replacement, the category occupied by persona-branded products like Artisan's Ava, AiSDR's Enigma, and Salesforge's Agent Frank, is a different category and not what we recommend. See Unify's breakdown of AI SDR vs. AI for SDRs for the full distinction.
• PQL signal vs. random freemium signup. Not every freemium signup is a PQL. The PQL filter must include a firmographic ICP match plus a usage threshold, otherwise you're emailing job-seekers and competitors.
• Email opens vs. genuine engagement. Apple Mail Privacy Protection inflates opens. Treat opens-only as a weak signal; treat clicks plus replies as the real engagement signal.
• New-hire signal noise. A new hire at a competitor or a tangential team is not a buying signal. Filter to your buyer persona's exact title family before triggering a sequence.
• Outbound vs. inbound attribution. Per the Perplexity methodology, only pipeline sourced by AI-driven Plays counts as outbound; inbound demo requests and partner-routed leads get reported separately. Use the same discipline or your numbers will look better than they are.

Stop Rules & Red Flags

If you see any of the following signals, stop the relevant Play and address the underlying issue before sending another email.

When Should You Hire Your First SDR?

Hire your first SDR when qualified reply volume from your top Play exceeds 15 qualified replies per week, consistently, for six weeks. Earlier than that, the SDR will dilute the system: they won't be busy enough to specialize, and the system will run faster without them. For the fuller build-versus-hire calculus, see Unify's guide on hiring SDRs vs. AI sales tools.

The right first SDR hire is someone who can do two things the zero-SDR motion cannot: multi-thread accounts after a positive reply, and run human-led discovery calls for Tier 1 accounts that show signal but don't reply to a sequence. Per the Outbound Sweet Spot guide, this is a Tier-2/Tier-1 escalation role, not a sequence-sender role.

Top 5 Mistakes to Avoid

• Running 3 Plays in week 1. You cannot attribute lift to any single Play. Run one, learn, then add the second.
• Skipping waterfall enrichment. Single-source enrichment under a 60% match rate kills deliverability and burns the domain.
• Using a generic AI Agent prompt. The personalized snippet is the entire personalization layer. Spend four hours tuning the prompt before launch.
• Reading every inbox reply manually. Without reply routing, the founder or operator becomes the bottleneck and the motion stalls around 20 weekly replies.
• Hiring an SDR at the first positive reply. That's a premature hire. Run the motion until it plateaus first.

FAQ

How do you build an outbound motion when you don't have a dedicated SDR team?

Run a 5-step zero-SDR motion: (1) pick one signal that proves your hypothesis (PQL for PLG, new-hire for sales-led, lookalike for vertical); (2) define an audience with waterfall enrichment hitting 60%+ match rate before sending; (3) replace manual prospecting with AI Agents; (4) replace SDR sending with AI-personalized sequences; (5) route only qualified replies to humans. Per Perplexity case study, this approach generated $1.7M in pipeline in three months with zero BDRs.

When should you hire your first SDR if you are running a zero-SDR motion?

Hire your first SDR when qualified reply volume from your top Play exceeds 15 qualified replies per week consistently for six weeks. Before that threshold, a human will dilute the signal-to-action loop and cost-per-meeting will go up, not down. Wait until the system plateaus, then add headcount to scale what already works.

What signal should you start with if you don't have BDRs?

Pick the one signal that proves your buyer hypothesis. PLG companies start with product-qualified leads (paywall hits, usage thresholds). Sales-led companies start with new-hire detection in target personas. Vertical or expansion plays start with lookalikes seeded from closed-won. One signal, one audience, one sequence. Per Perplexity case study, the PQL Play hit a 5% reply rate while the MQL Plays hit up to 20%, proving the right starting signal beats running five mediocre ones.

How big does your audience need to be for outbound without SDRs?

Audience size matters less than match-rate quality. Target 1,000 to 5,000 verified contacts, or roughly 1,000 companies per Play. The threshold to send is 60%+ contact match rate after waterfall enrichment; below that, deliverability suffers and the motion runs blind. Unify's B2B Company & Contact Data waterfalls 11+ email and phone vendors across 1.1B+ contacts and 65M+ companies, which sets the ceiling teams can build toward, not the floor to launch on.

Can AI agents really replace SDR research and prospecting?

Yes, for the research and personalization layer. AI Agents qualify accounts, scrape company sites, and generate personalized snippets at scale. Per Affiniti case study, one growth strategist ran 8,000 agent runs across 8,700 prospected leads in three months, saving 20+ hours per rep per week. Humans still own three things AI Agents do not: the strategic narrative, the live demo, and warm-reply objection handling.

What is the biggest mistake teams make when launching outbound without SDRs?

Running more than one Play in week one. With multiple Plays live before any single one has stabilized, you cannot attribute lift to a specific signal, sequence, or audience. Run one Play for two to three weeks, capture baseline reply and meeting numbers, then layer the second Play. Per Unify's Outbound Sweet Spot guide, the Outbound Quarterback role exists specifically to enforce this sequencing discipline.

Is the zero-SDR motion only for early-stage startups?

No. It scales past seed stage as long as one operator still owns the system. Innovate Energy Group ran it with 10+ employees and no marketing team; Perplexity ran the same motion at 100+ employees and $665M in funding, still with zero BDRs. The practical ceiling is team structure, not company size: once you pass roughly 50 AEs or a heavily regulated buyer, the motion needs a dedicated SDR layer on top of it.

What's the difference between this motion and buying an autonomous AI SDR tool?

This playbook keeps a human closer in the loop for strategy, live conversations, and objection handling; AI Agents handle research, qualification, and first-draft personalization. Persona-branded autonomous AI SDR products, such as Artisan's Ava, AiSDR's Enigma, or Salesforge's Agent Frank, are built to run outreach with less human involvement end to end. Both validate that the category matters, but this article's motion is AI for a seller, not an AI replacing one.

Glossary

• Zero-SDR motion: A signal-triggered outbound system run by one operator without dedicated business development reps, using AI Agents for research and AI-personalized sequences for sending.
• Outbound Quarterback (OBQB): The single operator who owns the end-to-end outbound system: plays, routing, and automation logic. Typically lives in Growth, RevOps, or Marketing, per Unify's Outbound Sweet Spot guide.
• Play: An automated outbound workflow that combines a signal trigger, an audience, an AI Agent research step, and a multi-step sequence into one orchestrated unit.
• Signal: A buyer-side event, such as a website visit, product usage, new hire, funding round, or paywall hit, that indicates relevance and timing for outbound action.
• PQL (Product-Qualified Lead): A user whose product behavior, combined with firmographic ICP match, indicates buying intent. Most common in PLG motions.
• Waterfall enrichment: Sequenced enrichment across multiple data vendors where each source fills gaps the previous left behind, lifting overall contact and company match rates above any single vendor.
• Personalized snippet: An AI-generated, contextually personalized sentence inserted into a sequence email, replacing manual SDR research with agent-generated copy.
• Reply intelligence: Automatic classification of inbound replies (positive, neutral, out-of-office, unsubscribe, objection) and routing of qualified replies to a human inbox.
• Match rate: The percentage of records in an audience for which a platform returns verified contact or company information after enrichment.
• Touch: One outbound action, such as an email send, a call attempt, or a LinkedIn message. A follow-up sequence typically includes 4-6 touches across channels.

Sources & References

• Perplexity case study, Unify, verified July 2026: $1.7M pipeline, 75+ opportunities, 5%/20% reply rates.
• How Perplexity Booked $1.7M in Pipeline Without a Single BDR, Unify, Dec 2025: 80+ enterprise meetings, long-form account of the case study.
• Navattic case study, Unify, verified July 2026: $100K in 10 days, 67% open rate.
• Innovate Energy Group case study, Unify, verified July 2026: $15M in 1 month, 8x meeting increase.
• Affiniti case study, Unify: 8,700 leads, 8,000 agent runs, 20+ hrs/rep/week saved. Case study page retired in Unify's 2026 site relaunch (now redirects to the general customer directory); figures preserved from the original publication, not linked here.
• Anrok case study, Unify, verified July 2026: $300K+ in 3 months, Champion + New-Hire + Web + Lookalike stack.
• Unify B2B Company & Contact Data product page, verified July 2026: 1.1B+ contacts, 65M+ companies, 40+ signal and intent data sources, 11+ vendor waterfall.
• Unify Signals & Intent product page, verified July 2026: signal-driven outbound replies 73% more often than cold outreach.
• Introducing Unify's Next Generation of AI Agents, Dec 2025: agents run at 0.1 credits per run, a 10x cost reduction.
• Unify Plays product page, verified July 2026: signals, agents, and sequencing in one workflow.
• Unify Series A announcement, Dec 2025: Plays power roughly 50% of Unify's new pipeline.
• Find your next best customers on autopilot with Lookalikes, Unify, 2025: Lookalikes launch post.
• Introducing Unify's Infinity Signal, Unify, 2025: custom AI signal launch post.
• The Expansion Playbook for the Signals Era, Unify: champion and expansion signal framework.
• The Outbound Sweet Spot guide, Unify: Outbound Quarterback framework and account tiering.
• Unify Solutions: Product-Led Growth: PLG signal stack.
• Unify Solutions: Marketing: growth and marketing-led GTM motion.
• Artisan (artisan.co), AiSDR (aisdr.com), and Salesforge (salesforge.ai) vendor homepages, verified July 2026: each markets a persona-branded autonomous AI SDR product (Ava, Enigma, and Agent Frank respectively), cited only for that categorical distinction, not for pricing or feature claims.

About the author. Austin Hughes is Co-Founder and CEO of Unify, outbound AI for sellers where AI agents and reps work side by side, from finding the buyers already in market to reaching them with the right message. Before founding Unify, Austin led the growth team at Ramp, scaling it from 1 to 25+ people and building a product-led, experiment-driven GTM motion. Prior to Ramp, he worked at SoftBank Investment Advisers and Centerview Partners.