We are looking for an experienced Retell AI specialist to optimize an existing outbound recruitment calling agent.
Our content and recruitment information are already prepared. Your role is to structure the Retell workflow so the agent sounds natural, receives maximum candidate engagement, keeps candidates talking and encourages them to open up before progressing toward a meeting.
The work includes:
Optimizing the call structure and conversational flow
Improving pacing, turn-taking, interruptions and responsiveness
Structuring questions to encourage detailed answers
Managing disengagement, hesitation and common conversation paths
Identifying and reducing drop-off points
Optimizing qualification and meeting-booking transitions
Testing calls and improving performance from real results
Key KPIs include meaningful conversation rate, conversation duration, candidate response rate, call completion rate, meeting-booking rate and conversation drop-off rate.
Please share relevant Retell AI projects, your approach, timeline and pricing.
We build and optimize Retell agents in production, both outbound and inbound, so your list is something that we’re very familiar with. Closest to what you’re describing: reworking an outbound agent for a completely different audience, rebuilding booking flows into live transfers along with all the ways they fail, and getting agents to stop interrogating people and just pick things up from context instead. From our experience, that last one makes the biggest difference to whether someone opens up or feels like they’re being processed.
For yours, we’d want to listen to your actual calls before touching anything. Everything on your KPI list is a conversation quality problem and you can’t fix those by guessing. My hunch is the drop-off is happening in the first few seconds rather than in qualification, but the call review would tell us. After that it’s restructuring the flow and how questions get asked, then tuning the pacing and turn-taking stuff (interruption sensitivity, backchannels, endpointing) that most people never touch, then iterating off real numbers.
Hey @premier — this is the work I do day to day, so I’ll keep it short.
I build and optimise Retell agents end-to-end — audits, restructuring, and getting them to actually perform. A few things that seem relevant to your project:
I’ve built agents that hold 30–40 minute conversations and land them properly at the end — keeping someone talking that long is the same muscle as getting a candidate to open up
Pacing, turn-taking, interruptions and silence handling — that’s where “sounds natural” is won or lost, and all of it is tunable
RAG synced to live databases like Airtable, so the agent works off current information instead of a frozen prompt, with proper guardrails on top
Clean opens, closes, and handoffs — a lot of drop-off hides there and gets blamed on the script
I optimise for cost as much as engagement; cutting the turns that go nowhere usually makes a call better and cheaper at the same time
I’ve also done a lot on the post-call side, including structuring long, heavy call data for AI model training — so transcripts, dashboards, and syncing into your ATS or CRM are all in my wheelhouse.
I’d want to hear a few of your calls before we could begin. Rough shape: diagnosis/strategy
in about 1-3 days and executing a first optimised version live within a week. Budget-wise, I’d start at $1k, worked on as a set number of hours we agree between us, and we can shape it as the work becomes clearer.
Hi @premier, I’d start by reviewing 3–5 real calls to identify where candidates disengage: the opening, turn-taking, question sequence, or booking transition.
I build real-time Voice AI systems and work with conversational flow, interruptions, latency, tool calling, failure handling and production validation. I can provide an initial diagnosis within 24 hours and then optimize the highest-impact parts of your Retell workflow.
Would you be open to a quick call today? I can send my availability by DM.
@premier — you already have several strong implementation offers here, so I’ll come at this from a different angle.
MARUPA focuses on independent Voice AI QA rather than implementation.
For this recruitment agent, I’d establish a measurable baseline across the outcomes you listed — meaningful conversation, completion, qualification, drop-off and successful meeting booking — then test realistic candidate behaviors against the live agent.
The important part is separating “the conversation sounded good” from “the business outcome actually happened.” For example: did the candidate qualify correctly, did the agent preserve the right information, and was the meeting actually created with the correct data?
That gives you an independent scorecard you can use before and after whoever performs the Retell optimization.
I can start with a small QA Snapshot on the existing agent so you can evaluate the findings before committing to anything larger.
Hi Premier — Ori from Spec Social here. I’d handle this as a focused optimization sprint, starting with the calls rather than guessing from the prompt.
First I’d review 3–5 representative calls and map exactly where candidates disengage: the opening, question sequence, turn-taking, interruption behavior, qualification, or booking transition. Then I’d change only the highest-impact variables and define the next test against conversation duration, completion, drop-off, and booking rate.
I’ve built and locally QA-tested two Retell front-desk implementations: one published and bound system that passed 65/65 local tests, and one paid pilot in predeployment that passed 32/32. I’m not presenting that as recruitment performance or production ROI; it is implementation and QA proof.
I’d scope the first paid milestone at $750: call and workflow review, failure map, revised conversation architecture, and prioritized test plan within two business days. Implementation and iteration can then be quoted from the actual diagnosis.
If that works, DM me and I’ll send the exact call samples and access checklist I need.
@premier I would start with the calls, not the prompt. Your KPI list is already the acceptance test. The useful first step is to establish a baseline for meaningful conversation, completion, qualification, drop off, and successful booking, then label exactly where each failed call broke.
From there I would change one class of failure at a time. Opening and consent. Turn taking and interruption. Question sequence. Qualification. Booking handoff. Each change gets rerun against the same scenarios so you can see whether it improved the outcome or only sounded better.
Relevant proof, without pretending it is recruitment specific. I run a production Retell path on a custom WebSocket at about 1.5 seconds warm time to first audio. The platform behind it has 76 edge functions and about 140 PostgreSQL tables. Its release gate currently passes 4 of 4 lead qualification cases and 3 of 3 abuse hard stops.
I will not quote a price or promise a delivery date before seeing the call set and current workflow. After read only access, I can give you a fixed first milestone with the exact scope, price, and delivery date. If you send a representative set of successful and failed calls by DM, I will tell you what I see before you commit.
Hi, I’m Ayaan, a full-stack and voice AI developer. I built MedVoq (https://www.medvoq.com, demo: https://www.youtube.com/watch?v=KFsXr3uznxw), a voice AI system that turns live doctor-patient consultations into structured records, running in government hospitals here in India. Keeping a real person engaged on a live conversation and landing a structured outcome at the end is exactly the muscle your agent needs.
How I’d approach yours:
Listen to a batch of your real calls first. Your KPI list is a conversation quality problem, and guessing at fixes without hearing where candidates actually drop off wastes both our time.
Restructure the call flow and questions so candidates open up before the qualification and booking transitions, then tune the pacing layer: turn-taking, interruption handling, endpointing, silence recovery.
Iterate against your KPIs with test calls and real results until the numbers move.
Wire the post-call side into your ATS/CRM with a simple dashboard, so every KPI is measured automatically.
Timeline: call audit and written findings within 2-3 days of access, first restructured version live within a week, then tuning off real numbers.
On pricing, I’d rather give you a fixed quote after the call review than throw out a number blind. It’s usually cheaper for you too. Happy to share it within a day of hearing the calls.
Damian Schaeffer — Natyv AI (natyv.ai). US, Eastern Time.
I ship production voice agents that qualify and book, then text if they don’t. Founder-led, not a ticket queue.
For this: audit the current Retell flow, tighten turn-taking and objection paths so drop-off falls, keep qualification + calendar booking, add SMS if a candidate bails mid-call. Phase 1 is a paid 7–10 day tune on your existing agent, with KPIs you already listed.
Timeline and pricing in the first reply here or Damian@get-myagent.com if you want it this month.
Outbound recruitment is the harder case, because the person didn’t ask you to call and the first few seconds decide the whole thing.
I run a voice agent in production — inbound for small businesses in Portugal. Deepgram for speech to text, Claude for the reasoning, ElevenLabs for the voice, Telnyx on the line.
On pacing and turn-taking specifically: most of what read as “unnatural” for us turned out not to be the prompt at all. Callers said the agent was hard to follow, and it was three things stacked on each other — the carrier sending 20ms packets, a settings layer sitting on top of the voice, and a duplicate entry in the audio cache. We only found it after building a way to measure the line objectively instead of listening and forming opinions about it.
The other thing worth saying before you spend time on question structure: latency does more damage than anything you’ll write in the prompt. A second and a half of silence reads as a dead line. On an outbound call that is your drop-off, and it looks like disinterest in the transcript.
Happy to go through the workflow with you. I’m in Lisbon, so European hours, and I work in English and Portuguese.
Hi premier — I’m Damian Schaeffer, founder of Natyv AI. I build and optimize production voice agents (Retell/Vapi-style), including conversation flow, turn-taking, qualification and booking. I can review your calls, diagnose drop-off, and deliver a paid optimization/install sprint with testing and a revised flow. Contact me at Damian@get-myagent.com or https://natyv.ai.