# Restaurant Voice Agent — Production tuning + scalable POS playbook (paid project) **URL:** \ **Category:** Hire A Retell Expert\ **Created:** [May 12, 2026, 4:35pm UTC](https://community.retellai.com/t/restaurant-voice-agent-production-tuning-scalable-pos-playbook-paid-project/2677 "2026-05-12T16:35:20Z")\ **Posts on this page:** 12\ **Page:** 1
**Author:** ![team2](https://yyz2.discourse-cdn.com/flex008/user_avatar/community.retellai.com/team2/32/1856_2.png) [@team2](https://community.retellai.com/u/team2)\ **Post date:** [May 12, 2026, 4:35pm UTC](https://community.retellai.com/t/restaurant-voice-agent-production-tuning-scalable-pos-playbook-paid-project/2677/1 "2026-05-12T16:35:20Z")
Looking for an experienced Retell builder to partner on a project with real upside. This isn’t a small-fix gig — it’s a chance to help shape a multi-restaurant voice AI platform that I’m building under my agency, ARCO. **The project:** Bilingual (English + Levantine Arabic) voice ordering agent for a restaurant in New York. Going live as the showcase build for ARCO’s restaurant tech platform. After this launches, the plan is to onboard 5-10 more restaurants over the next 6 months and the right person on this project gets first crack at those builds too. **The stack is already built:** - Retell AI agent (Claude Haiku, ElevenLabs Sara voice) - Supabase Postgres with custom RPC business logic (46 automated regression tests, all passing) - n8n workflows orchestrating Retell ↔ Supabase ↔ abcPOS ↔ Twilio - Pre-call inbound webhook for personalized customer greetings - Smart drink/lemonade flavor routing, hookah pagination, Arabic dialect aliases, 30+ STT mishearing corrections I’ve done the hard architecture work. The backend is solid. **What I need help with:** The agent works ~80% of the time on real calls. I need a Retell expert to get it to 95%, document what they’re doing, and help me build a playbook so I can replicate this for new restaurants on different POS systems (Toast, Square, Clover, etc.). Specifically: 1. **Retell config audit** — boosted keywords, endpointing, interruption sensitivity, Talk While Waiting tuning, voice settings 2. **Prompt structure review** — likely have prompt rules that belong in function descriptions or backend 3. **LLM drift diagnosis** — which behaviors are tunable vs. inherent (and worth designing around with escalation) 4. **Escalation fallback** — frustrated caller / stuck order → capture number, SMS restaurant for callback 5. **POS integration patterns** — generalize what I learned with abcPOS into a documented playbook for Toast/Square/Clover **The deliverable I’m paying for:** - Working production-grade agent at 95% accuracy - Documented fixes in screen-share or written form (I’ll convert to internal docs) - POS integration playbook that lets me onboard restaurant #2 in 1-3 days instead of 11 - Recommended patterns from your production experience **More context:** - Real stack, real client, going live — not a hypothetical - Backend already built — you’re tuning, not rebuilding - Bilingual + complex menu = portfolio piece worth showing other clients - ARCO is actively scaling — strong work here = repeat business - Open to ongoing work if the build is right Happy to share more details, recordings of test calls, and access to the build in a DM. Looking forward to hearing from you. Jon ---
**Author:** ![sridharsravan](https://yyz2.discourse-cdn.com/flex008/user_avatar/community.retellai.com/sridharsravan/32/155_2.png) [@sridharsravan](https://community.retellai.com/u/sridharsravan)\ **Post date:** [May 21, 2026, 4:33pm UTC](https://community.retellai.com/t/restaurant-voice-agent-production-tuning-scalable-pos-playbook-paid-project/2677/3 "2026-05-21T16:33:03Z")
Hi Jon, This is a strong fit on the reliability/orchestration side. I would not start by rewriting the whole agent. I would first turn the current 80% behavior into a labeled failure set, then tune against that so improvements are measurable. The first paid slice I would propose: - review 10-20 real call recordings/transcripts; - classify failures as STT/menu alias, Retell config, prompt ambiguity, function/backend schema, POS/API state, or escalation; - tune the highest-impact Retell settings and prompt/function boundary; - add or tighten the stuck-order fallback: capture number, summarize attempted order, SMS/task to restaurant; - leave a POS onboarding playbook skeleton for Toast/Square/Clover so restaurant #2 is not another custom rebuild. I am not a native Levantine Arabic speaker, so I would handle the system/reliability work and pair the language judgement with your native review or recordings. The useful part I can own is making the agent testable, reducing drift, and turning the POS path into repeatable interfaces. I put together a compact proof/playbook for this exact shape: > **[Restaurant Voice Agent Tuning Playbook](https://sravan27.github.io/money-27-proof/proof/restaurant_voice_agent_tuning_playbook.html)** > > A practical Retell AI restaurant voice-agent tuning playbook covering config, prompt/function boundaries, fallback, POS patterns, and regression tests. Related Retell/GHL workflow proof: > **[Retell AI To GoHighLevel Workflow Demo](https://sravan27.github.io/money-27-proof/proof/retell_ghl_workflow_demo.html)** > > Retell AI voice-agent to GoHighLevel workflow demo with booking, handoff, duplicate prevention, and review paths. If useful, send 3-5 failure recordings/transcripts and the current Retell/n8n/POS boundary map. I can reply with a fixed first milestone and timeline. Sravan ---
**Author:** ![nathan](https://yyz2.discourse-cdn.com/flex008/user_avatar/community.retellai.com/nathan/32/2293_2.png) [@nathan](https://community.retellai.com/u/nathan)\ **Post date:** [July 15, 2026, 9:29am UTC](https://community.retellai.com/t/restaurant-voice-agent-production-tuning-scalable-pos-playbook-paid-project/2677/5 "2026-07-15T09:29:23Z")
Did you find someone to help with this yet? ---
**Author:** ![alberto.rentas](https://yyz2.discourse-cdn.com/flex008/user_avatar/community.retellai.com/alberto.rentas/32/2568_2.png) [@alberto.rentas](https://community.retellai.com/u/alberto.rentas)\ **Post date:** [August 24, 2026, 1:14pm UTC](https://community.retellai.com/t/restaurant-voice-agent-production-tuning-scalable-pos-playbook-paid-project/2677/7 "2026-08-24T13:14:01Z")
Jon — the gap between 46 passing backend regression tests and ~80% reliability on real calls really stood out. I’m building independent Voice AI QA at MARUPA focused on verifying whether the downstream business outcome actually happened correctly across the full workflow. I’d be happy to independently test 5 critical failure paths and share a one-page QA snapshot at no cost. Not replacing the Retell expert — just independently validating the result. ---
**Author:** ![stone](https://yyz2.discourse-cdn.com/flex008/user_avatar/community.retellai.com/stone/32/2559_2.png) [@stone](https://community.retellai.com/u/stone)\ **Post date:** [August 27, 2026, 4:06am UTC](https://community.retellai.com/t/restaurant-voice-agent-production-tuning-scalable-pos-playbook-paid-project/2677/8 "2026-08-27T04:06:37Z")
@team2 If this is still open, I match the Retell, Supabase, Postgres, Twilio, booking, handoff and evaluation portions. I do not speak Arabic, so your side would need to validate Arabic aliases and phrasing. My production voice path reaches about 1.5 seconds warm time to first audio. The platform behind it has 76 edge functions and about 140 Postgres tables. Its release gates currently pass 4 of 4 unqualified lead cases, 3 of 3 abuse stops and 12 of 12 AI CRO eval suite. I would begin by labeling the failed calls you already have, separate speech recognition, function schema, POS state, agent logic and escalation failures, then turn those failures into repeatable cases. I will not quote a schedule or price until I can see the current workflow and evidence. Message me read-only access if the project remains open. Stone ---
**Author:** ![Damian](https://yyz2.discourse-cdn.com/flex008/user_avatar/community.retellai.com/damian/32/2627_2.png) [@Damian](https://community.retellai.com/u/Damian)\ **Post date:** [September 10, 2026, 9:35pm UTC](https://community.retellai.com/t/restaurant-voice-agent-production-tuning-scalable-pos-playbook-paid-project/2677/9 "2026-09-10T21:35:29Z")
@team2 — Damian Schaeffer, Natyv AI (natyv.ai). US / ET. Founder-led Retell production tuning (not a rebuild). Your gap (solid Supabase/n8n backend + ~80% live calls) is exactly where I’d start: label failed calls into STT/alias vs Retell config vs prompt/function boundary vs POS state vs escalation, then tune the highest-impact slice and leave a repeatable playbook. I’m not a Levantine Arabic native speaker — you’d own dialect judgment; I’d own reliability, endpointing/interrupts, escalation SMS, and POS onboarding patterns. Fixed Phase-1 tune is typically 7–10 days on the existing agent once I see recordings + read-only access. Email [Damian@get-myagent.com](mailto:Damian@get-myagent.com) or DM here if still open. ---
**Author:** ![krittiya.clark](https://yyz2.discourse-cdn.com/flex008/user_avatar/community.retellai.com/krittiya.clark/32/2065_2.png) [@krittiya.clark](https://community.retellai.com/u/krittiya.clark)\ **Post date:** [September 11, 2026, 12:39am UTC](https://community.retellai.com/t/restaurant-voice-agent-production-tuning-scalable-pos-playbook-paid-project/2677/10 "2026-09-11T00:39:35Z")
> [@nathan](#): > > Did you find someone to help with this yet? Did you find someone to help you with this yet? ---
**Author:** ![ksmolichki](https://yyz2.discourse-cdn.com/flex008/user_avatar/community.retellai.com/ksmolichki/32/2716_2.png) [@ksmolichki](https://community.retellai.com/u/ksmolichki)\ **Post date:** [September 13, 2026, 8:59pm UTC](https://community.retellai.com/t/restaurant-voice-agent-production-tuning-scalable-pos-playbook-paid-project/2677/11 "2026-09-13T20:59:30Z")
Jon, the measurable gap is the useful part here: 46 passing backend tests versus roughly 80% reliability on real calls. I would not begin by changing production. I propose a fixed $450 diagnostic: run five representative call paths, classify each failure across STT, Retell configuration, prompt/function boundary, POS state and escalation, then return a reproducible scorecard plus the three highest-impact fixes within one business day. No production changes or Levantine-Arabic judgement in this milestone. I can begin once the milestone is funded and I have read-only access plus five recordings. Is the project still open? Email [kalin.smolichki@gmail.com](mailto:kalin.smolichki@gmail.com) or DM here. ---
**Author:** ![ayaankhb24](https://yyz2.discourse-cdn.com/flex008/user_avatar/community.retellai.com/ayaankhb24/32/2615_2.png) [@ayaankhb24](https://community.retellai.com/u/ayaankhb24)\ **Post date:** [September 14, 2026, 7:27am UTC](https://community.retellai.com/t/restaurant-voice-agent-production-tuning-scalable-pos-playbook-paid-project/2677/12 "2026-09-14T07:27:12Z")
hey jon - i built MedVoq, a production voice AI used in Indian government hospitals, so the jump from an 80% demo to a reliable live call flow is familiar territory. i can audit Retell config, move brittle prompt logic into functions/backend where it belongs, test drift and fallback paths, and turn the fixes into a repeatable POS playbook. your Supabase RPC + n8n + Twilio stack is close to work i’ve shipped. what rate or project budget are you working with for the first restaurant? ayaankhan.cv ---
**Author:** ![dwivedivenkatesh98](https://yyz2.discourse-cdn.com/flex008/user_avatar/community.retellai.com/dwivedivenkatesh98/32/2888_2.png) [@dwivedivenkatesh98](https://community.retellai.com/u/dwivedivenkatesh98)\ **Post date:** [October 14, 2026, 6:05am UTC](https://community.retellai.com/t/restaurant-voice-agent-production-tuning-scalable-pos-playbook-paid-project/2677/13 "2026-10-14T06:05:24Z")
Hi Jon, Saw your post about improving the reliability of your bilingual restaurant ordering agent from around 80% toward 95%, while building a repeatable POS integration playbook. We can help with Retell tuning, failure analysis, escalation handling, and documenting a repeatable approach for future restaurant deployments. I run Pinnakle Media. We build AI voice agents on Retell and Vapi and connect them to the tools you already use, like GoHighLevel, n8n and your calendar. Here’s how I’d approach yours: - Review real call recordings and identify where the agent fails - Tune the Retell configuration and prompt/function boundaries based on those failures - Test the fixes and document reusable patterns for future POS integrations If it helps, we can start with a small paid test, so you see the work before you commit to the full build. Is the project still open? If so, we can discuss the first milestone and scope. Thanks, Venkatesh ---
**Author:** ![keini5868](https://yyz2.discourse-cdn.com/flex008/user_avatar/community.retellai.com/keini5868/32/2946_2.png) [@keini5868](https://community.retellai.com/u/keini5868)\ **Post date:** [October 23, 2026, 10:18pm UTC](https://community.retellai.com/t/restaurant-voice-agent-production-tuning-scalable-pos-playbook-paid-project/2677/14 "2026-10-23T22:18:47Z")
I run a live Retell agent in production (voice, tools, and a custom LLM) at [https://ced-castillo.com](https://ced-castillo.com). Most of my time on it has gone to the same problems you listed: replies that run long, filler, awkward handoffs, and calls that fail when the path is not the happy one. I can review your current flow and give you a concrete list of changes: - Where the conversation should be shorter, and which lines to cut - How to structure the prompt, nodes, and transitions so the agent stops wandering - What to change for voicemail, IVR, and a transfer that does not connect - What is making it unreliable once you leave the test call Send me one recording of a bad call and, if you can, a view of the flow. I will tell you within a day whether it is a prompt problem or a node problem. The review is a fixed $350: I listen to the calls, walk the flow with you, and you get the written changes. A 20-minute call before that is only to see the agent, not to do the audit on the call. If that scope fits, send a time and the recording. ---
**Author:** ![jasonjibuilds](https://yyz2.discourse-cdn.com/flex008/user_avatar/community.retellai.com/jasonjibuilds/32/2925_2.png) [@jasonjibuilds](https://community.retellai.com/u/jasonjibuilds)\ **Post date:** [October 23, 2026, 10:27pm UTC](https://community.retellai.com/t/restaurant-voice-agent-production-tuning-scalable-pos-playbook-paid-project/2677/15 "2026-10-23T22:27:44Z")
Jon, are you still placing work for the restaurant rollout and POS playbook, or has this been delivered? I am Jason Ji, a US-based individual contractor with 12 years of professional development experience and 5+ years in production Node.js/Python work. I can own an agreed backend integration and release-testing workstream alongside your Retell specialist. For a reusable POS adapter, I would make menu/modifier mappings explicit, give orders stable operation IDs, reconcile accepted orders after uncertain responses, and exercise duplicate webhooks, unavailable items, timeouts and escalation. Bilingual acceptance cases would use expected outputs approved by your language/domain reviewer. Those checks and the setup/runbook would become the next venue’s deployment playbook. My proposed rate is USD45/hour with a written milestone cap; the complete scope and fixed price would follow the current brief. Working examples and downloadable source are here: [Jason Ji — Software Delivery](https://call-check-voice-20261020.activesnow6.chatgpt.site/engineering) — original demonstrations using synthetic data. I use AI coding tools and review and test the changes. If a delivery workstream remains open, which POS is next and what budget and launch date have been agreed? Jason Ji | [contactjensennow@gmail.com](mailto:contactjensennow@gmail.com)