8 YourGPT Alternatives by Support Workflow

YourGPT markets itself as a no-code AI agent platform for support, sales, and operations, with published credit plans, a Studio for workflow logic, and an agent layer that sits over whatever helpdesk you already run. Teams evaluating alternatives usually are not questioning whether agents work. They are questioning the shape of the contract: who owns the build, how the bill moves, and whether the agent lives inside the inbox the team actually opens.

This guide compares eight platforms that replace YourGPT for specific jobs. They are grouped by workflow rather than ranked, because no single ladder makes sense across all of them:

• Existing-helpdesk overlays: Lorikeet, Decagon, and Intercom Fin swap the agent while keeping your current helpdesk. You keep the queue, macros, and reporting; you change who answers.
• Enterprise service operations: Sierra, Ada, and Yellow AI sell a managed program with vendor governance, implementation ownership, and typically a negotiated contract. You buy more than software.
• Ecommerce: Gorgias replaces both the agent and the helpdesk when your support conversations are Shopify orders, returns, and subscriptions.
• Custom builders: Voiceflow hands your team a canvas, API access, and model control so your own builders design the agent.

Each section explains the mechanism the platform uses, the job it finishes, where it fails, and how to model its pricing against YourGPT's published plans.

The baseline: what YourGPT does

YourGPT is an AI agent platform, not a helpdesk. You train agents on websites, documents, help-center content, and connected systems; you add actions through Studio workflows, functions, and APIs; and the agent hands off to humans with conversation context when it cannot finish. YourGPT's documentation describes the product as a no-code AI agent platform that goes beyond answering questions by taking real action, and lists deployment targets spanning websites, WhatsApp, Instagram, Telegram, Slack, and phone. Its ecommerce capabilities matter for part of the shortlist: the AI Copilot documentation lists store actions for product search, product cards and details, add-to-cart, and view cart on Shopify, WooCommerce, and BigCommerce, and YourGPT's homepage describes order-status and returns handling inside chat.

The pricing shape matters because every alternative bills differently. YourGPT's pricing page lists Essential at $39 per month billed annually ($59 monthly) with 10 million AI credits, Professional at $79 per month billed annually ($129 monthly) with 30 million credits, and Advanced at $349 per month billed annually ($499 monthly) with 100 million credits. Enterprise is custom. Credits are consumed by tokens multiplied by a model rate, so a Tier 1 reasoning model burns a plan faster than a small fast model; the model table documents consumption rates from 0.5x up to 15x.

That creates two strengths the alternatives have to beat. First, you can start without a sales cycle and without moving the inbox. Second, the bill starts as a flat plan, not a per-outcome meter that grows every time the agent succeeds.

It also creates the exit reasons. A credit is not a resolution. A long multi-step conversation on a premium model and a short FAQ answer on a mini model do not cost the same, and neither maps cleanly to a ticket a finance leader recognizes. If you need the vendor paid only when a ticket is resolved, the vendor embedded in your helpdesk, or an engineering team building the agent with you, you are shopping for a different contract, not a better chatbot.

YourGPT also publishes a resolution framework. On 26 August 2026, YourGPT said maintained deployments can reach 90%+ AI resolution on eligible repeated requests, defining resolution strictly: the customer's intended outcome is completed, required actions succeed and are confirmed by the responsible system, AI retains ownership, and the same issue does not return inside a re-contact window. That definition is useful when evaluating any vendor's claims. The YourGPT review covers the product in depth, including the wider industry spread YourGPT published.

Why teams look beyond YourGPT

The reasons are operational, and they rarely start with answer quality.

The team outgrows self-serve. YourGPT Studio, functions, and webhooks reward a team that owns knowledge, permissions, and failure review. At enterprise volume, some buyers want the opposite: a vendor team that writes procedures, runs simulations, and sits in quarterly reviews. Sierra, Decagon, and Ada sell that partnership. It costs more and moves slower. For a regulated or high-volume queue, slower can be the right trade.

Credit math stops mapping to the queue. Finance can model 2,000 tickets a month. It cannot easily model 30 million credits until someone measures how many credits a refund workflow, a long troubleshooting thread, and a translated conversation each consume on the selected model. Per-conversation and per-resolution vendors look more expensive per unit and are easier to explain to a budget owner.

The helpdesk is the real system of record. If agents live in Intercom or Zendesk all day, an external agent layer has to prove its handoff payload, ticket fields, and reporting match the native tool. A blank ticket with a name and a channel is not a handoff; it is a second triage job.

The action lives in one store or one CRM. Shopify order changes, returns, and subscriptions are Gorgias's native objects. Intercom Procedures live inside Intercom data. A general agent platform can connect to those systems, but you build and maintain the connection. Some teams would rather buy the object than wire it.

Governance needs a different owner. SSO, data residency, audit history, redaction, approval thresholds, and model routing change shape at enterprise scale. YourGPT publishes Enterprise terms; Sierra, Decagon, Ada, and Yellow AI start the conversation there. That does not make them safer by default; it makes the security review, DPA, and implementation owner part of the product.

Voice and proactive campaigns become the job. YourGPT covers messaging and web well. If the next job is outbound voice, employee service, or campaigns across dozens of channels, Yellow AI and Sierra frame the purchase differently.

Five tests every alternative has to pass

Score every finalist on the same five tests before the demo, not after:

Knowledge trust. Can the agent refuse when the answer is not in approved docs? Ask a plan-limit question whose answer exists only in an unpublished internal note. If the agent invents a number, it fails.

Actions, not only answers. Refunds, order lookups, subscription changes, bookings, and CRM writes need identity checks, permissions, and an audit trail. Ask the agent to start a refund it is not allowed to complete. The correct move is a stopped workflow and a handoff, not a confident confirmation.

Handoff quality. Open the ticket the agent creates. The human should see the transcript, customer identity, records checked, the step that failed, and the reason for escalation. Test this in the helpdesk your team will open at 9 a.m., not in a demo inbox.

Pricing you can forecast. Credits, conversations, resolutions, outcomes, tickets, seats, and platform fees behave differently at the same volume. Write last month's ticket count on paper, run every vendor's meter against it, and ask what fires when the agent escalates after doing useful work.

System-of-record fit. Replacing only the agent is cheaper than replacing the inbox, macros, SLAs, and reports. If a vendor needs a helpdesk migration to show value, price the migration year. If it sits on your current desk, test the integration at the action level, not with a logo.

Run the same pilot on every finalist: a billing plan-limit question, an integration question, a broken-after-a-change ticket, a refund request, and a vague teammate-invite message. Add one ticket that should never auto-resolve and one where the connected system is unavailable. Hold out a set the implementation team never sees during tuning. The AI agent platform selection guide has a scorecard you can take into those demos.

Existing-helpdesk overlays: swap the agent, keep the inbox

This group fits teams that have already decided the helpdesk stays: Zendesk, Intercom, Freshdesk, Salesforce Service Cloud, or similar, with macros, SLAs, and reports the team trusts. The only thing changing is the agent layer on top. Lorikeet, Decagon, and Fin all take that shape, but they differ in who builds the workflows, how they meter the bill, and how much enterprise process they require.

Lorikeet: pay per resolved ticket, keep everything else

Lorikeet is an AI layer, not a helpdesk. Its Concierge agent works across chat, email, SMS, and voice and connects to ticket platforms including Zendesk, Intercom, HubSpot, Front, Help Scout, and Salesforce, according to its pricing page. You keep the queue, macros, and reporting; Lorikeet takes tickets it can finish and escalates the rest with full context. That is the closest structural match to YourGPT on this list, with a different bill and a forward-deployed team attached.

The mechanism is worth understanding. Lorikeet's FAQ describes a Team of Agents architecture: specialized agents handle data lookup, policy checking, action execution, and response generation under an orchestrator, with workflow training separate from reference-material training. Subject matter experts review and approve workflows before deployment, guardrails define what the agent can and cannot do, and escalations route to your default team or specialized teams with full audit trails explaining why each escalation occurred. You are never charged for escalated tickets; only successful resolutions, and you can mark tickets as bad at no charge.

Lorikeet's pricing page publishes Start at $2,100 per month paid annually for under 5,000 monthly tickets, with chat, email, and SMS resolutions at $0.99 and voice at $1.50. Scale is $5,100 per month paid annually for 5,000 to 20,000 tickets, at $0.90 per chat, email, or SMS resolution and $1.20 for voice. Triage and automated QA carry small per-ticket rates. Signature is custom above 20,000 tickets, with a $75K platform fee listed. There is no per-seat charge.

A worked month makes the gap concrete. At 900 chat resolutions on Start, usage is $891 on top of the $2,100 platform month. That only beats YourGPT if the resolutions are harder, the handoff is cleaner, or the forward-deployed team replaces internal hours you would otherwise hire. Lorikeet's own pricing page uses a Scale customer resolving 5,000 chat tickets at $0.90 each: $4,500 in resolutions on top of the plan. Model your own queue before signing.

Lorikeet is the wrong shortlist below meaningful volume; a $2,100 monthly floor needs ticket volume to amortize; and it assumes you already have a helpdesk you like. You do not migrate; you connect.

Decagon: written operating procedures at enterprise volume

Decagon is an enterprise AI agent platform for chat, voice, and email, with no native helpdesk. Like YourGPT, it sits over the CRM, helpdesk, and call-center stack you already run; unlike YourGPT, it is built for a heavier enterprise rollout with forward-deployed engineering. Buyer diligence should center on how support teams shape agent behavior and how the platform handles failure recovery, rather than on marketing claims from other customer deployments.

The distinguishing mechanism is procedure-driven work. CX teams define workflows and rules in natural language that compile into governed logic, with testing, versioning, and production controls aimed at engineering owners. Ask who can roll back a bad procedure and how fast, and have your support lead; not the sales engineer; edit a procedure in the demo and push it to a test environment.

Decagon's pricing post documents two meters: per-conversation pricing, a fixed rate for every incoming conversation, and per-resolution pricing, a higher rate only for fully resolved conversations with no charge for escalations. Most customers choose per-conversation for predictability, according to Decagon. Rates vary by channel, volume, and commitment; there is no public rate card, and voice costs more than chat.

Model it exactly like YourGPT but with the meters swapped: take last month's incoming conversations and verified resolutions, price both Decagon meters, add the platform commitment from the quote and the helpdesk you keep, then compare with your YourGPT plan at the credit burn measured in your pilot. Per-conversation is easier to budget; per-resolution is easier to defend. Pick the one finance can audit.

Public review volume for Decagon is thinner than the large helpdesks, so compensate with reference calls in your industry and the shared pilot. Vendor resolution claims from other companies do not transfer to your policies.

Intercom Fin: same agent, two deployment shapes

Fin is Intercom's AI agent, and it can be sold in two ways. Inside Intercom, it lives natively in the messenger, inbox, and help center, tied to Intercom's data and Procedures. On another helpdesk; Salesforce, Zendesk, HubSpot, Gorgias, and others listed in Fin's pricing page; Fin runs without Intercom seats, subject to a minimum monthly commitment. See the Fin alternatives guide for the stay-or-leave test from the other direction.

The mechanism is outcome-based. Intercom's pricing page lists Fin at $0.99 per outcome, charged at most once per conversation. An outcome includes a confirmed resolution, no further help requested after Fin's answer, or a completed Procedure, including some handoffs. Qualification outcomes for sales are priced higher at $9.99, per Fin's outcome FAQ. Inside Intercom, add seats: Essential from $29 per seat per month published alongside a promotional $19, Advanced at $85, Expert at $132, per the pricing page. On another helpdesk, there is no seat cost but a 50-outcome monthly minimum applies. At 900 billable outcomes, that is $891 before seats.

The main diligence point is outcome definition, because it drives the invoice. A resolution under Intercom's rules can be a customer who did not ask for more help after the last AI answer, and a completed Procedure that hands off can also bill. Track your own verified resolution rate beside the invoice for one month before scaling volume. If the two diverge, renegotiate the workflow design before blaming the model.

Model it against YourGPT with last month's billable outcomes at $0.99, plus the seats you would keep or add. If you already pay for Intercom and would keep it either way, Fin is the incremental line and often wins on friction. If Intercom would be new, include the migration.

Enterprise service operations: buying a program, not a widget

This group is for buyers who have decided AI is a program with a roadmap, a QA function, and an executive metric. You are not evaluating a widget; you are choosing a vendor operating model, a contract shape, and an implementation owner. Sierra, Ada, and Yellow AI all fit here, but they approach it differently.

Sierra: negotiated outcomes and a board-level CX program

Sierra builds one agent across chat, voice, email, SMS, and WhatsApp, connected to systems of record such as CRM and order management. Sierra's product page describes building once and deploying across channels, with Agent Studio for business users and an Agent SDK for engineering teams, plus Ghostwriter, an agent-building agent that drafts from SOPs, transcripts, and plain-language goals. It is an enterprise platform with a services motion, not a self-serve replacement on a credit card. Read the Sierra AI review for the full fit test.

Sierra pioneered outcome-based pricing for AI agents. Sierra's outcome-based pricing post explains that you pay when the software achieves a specific valuable result; a resolved conversation, a saved cancellation, a completed purchase; and unresolved cases generally do not bill, with blended consumption pricing possible for routing or greeter work. Rates and tiers are not published; outcome classes, exclusions, platform or services fees, and minimum commitments are all negotiated. Ask Sierra to label 500 of your historical conversations as billable, escalated, or out of scope before discussing rates; if labeling takes weeks, that is information about time to value.

Sierra is the wrong shortlist if you need a live agent this week, a public rate card for procurement, or a small website queue. A company answering 300 tickets a month does not need an outcome-definition workshop. Model Sierra's year-one cost against your YourGPT plan plus the internal hours Studio ownership takes; if Sierra cannot beat your verified cost per resolution after that math, the partnership is a preference, not a saving.

Ada: a global CX operating model across languages and channels

Ada sells an agentic customer experience platform to large support organizations. Agents run across web, messaging, social, email, and voice under one operating model, with playbooks, testing, and performance management around them. Ada's platform page pairs the software with its ACX practice and expert services, and there is no honest credit-card start comparable to YourGPT Essential. It is the wrong shortlist for a mid-market team without an implementation owner: if nobody inside your company will own knowledge quality, escalation design, and weekly performance review, Ada will not supply that person.

Ada does not publish a rate card. Its resource page notes that most customers use conversation-based pricing, paying for every conversation the agent handles, with a resolution-based model available for enterprises that prefer it. The extracted snapshot we rely on was blocked by a Cloudflare security check, so confirm the current meter and any platform or services fees on the quote. Volume, channels, and integrations set the price. Demand the per-conversation rate and any platform or services fees, then price last month's conversations and your peak month, compared with your YourGPT plan, measured credit overage, and the internal owner both products require. Ada has to win on program scope and governance; it will not win on entry price, and it should not be asked to.

Ada's materials emphasize program scope and brand governance, so evaluate time to value and commercial transparency directly in the contract: name the first three use cases, go-live criteria for each, and the definition of a billable conversation, and run the shared pilot before the roadmap workshop.

Yellow AI: customer and employee service as one global program

Yellow AI is an enterprise platform for customer and employee experience, covering chat, voice, email, and SMS across a wide channel set. Its pricing page frames the full offer at 35-plus channels and 150-plus integrations on Enterprise. The breadth is the reason to consider it and the reason to be careful: you are buying a program that spans departments, not a support widget with extras.

The mechanism that distinguishes Yellow AI is consolidation: a single platform for customer-facing agents, employee-experience agents (HR, IT), voice via its VoiceX product, and proactive campaigns. If you would otherwise buy three products; a support agent, a voice agent, and an employee-service bot; Yellow AI collapses them into one administrative and governance surface.

Diligence should focus on scope control. Name the first use case, the channels in phase one, and the success metric in writing; ask which charges are usage-based beyond the platform fee, including channel and messaging costs that often sit outside the headline quote. Reference customers should match your region and primary channel, not just your industry. Yellow AI's pricing page publishes a Free tier with one AI agent and 500 chat sessions per month, then $0.99 per resolution, and an Enterprise tier with unlimited agents and sessions at custom pricing. Treat Free as an evaluation sandbox; a production quote will turn on conversations, channels, features, and support level. Price phase one only; if Yellow AI only wins when you credit it with future departments, you are buying a roadmap.

Ecommerce: when the conversation is a store order

Gorgias: the Shopify object is the conversation

Gorgias is an ecommerce helpdesk, not a general agent platform. Its AI Agent sits on Gorgias tickets connected to Shopify and other commerce stacks, and the object it owns is the store order. For a SaaS team with no order object, this section is a deliberate mismatch: read it only if support at your company means where-is-my-order, returns, address changes, and subscription skips. Read the Gorgias review.

The mechanism is native Shopify actions: order status, cancellations, address changes, item edits, refunds, and returns within the rules you configure, with the AI trail landing in a ticket a human can audit. Gorgias positions itself as the helpdesk built for Shopify merchants, and its integration includes rich customer profiles, order management directly from the helpdesk, and automated tasks. The helpdesk and the agent arrive together.

The bill is stacked, and that is the diligence item. Gorgias's billing documentation confirms an AI-resolved conversation can count as both a helpdesk ticket and an automation interaction: you are charged both a ticket fee and an automation fee when AI Agent responds to a ticket and does not hand over the conversation to a human. Gorgias's pricing page sells helpdesk plans by ticket volume, starting from a small Starter tier, with AI Agent on every plan priced per resolved interaction. Gorgias's AI Agent pricing explainer puts most plans at $0.90 per resolved interaction, with Starter at $1, and included automated-interaction allowances by tier. Overage fees apply beyond the plan baseline, and an interactive quote for your volume is not published on the visible page, so confirm the current allowance and overage rate on your plan before budgeting.

Ask for a sample invoice from a peak week, not an average month, then run a return that should not auto-approve and an order that has already fulfilled. The agent must stop, explain, and hand off with the order attached. Model Gorgias's ticket tier plus AI interactions against your YourGPT plan plus the helpdesk you would keep for store support; if you would migrate the desk to get order actions, include the migration and the month your macros are rebuilt.

Custom builders: when your team owns the agent

Voiceflow: canvas, API, and model control

Voiceflow is a builder platform for chat, voice, and SMS agents. Product, design, and engineering teams design conversations on a canvas, connect knowledge and APIs, test with simulations, and deploy to web, phone, and messaging surfaces. Voiceflow's pricing page frames the product for businesses and agencies, with implementation support from self-serve to fully managed. Where YourGPT Studio configures an agent for you, Voiceflow hands your team the workbench; a feature if you have builders, a staffing cost if you do not.

The mechanism is builder control: visual canvas plus API and function calling, model choice across major providers with fallback models on higher plans, knowledge-base grounding, simulations, and analytics for pre-launch testing. Voice deployment adds phone numbers and concurrency as plan add-ons. Voiceflow does not publish list prices on its pricing page; its billing documentation describes the bill as a monthly plan fee plus optional add-ons such as editor seats and phone numbers, plus credit-based usage shared across workspaces, with Free/Pro/Business/Enterprise tiers and current prices inside the product's Plans and Billing tab.

Voiceflow is the cleanest comparison against YourGPT on this page because both meter credits. Run the same pilot workflows on both, record credits per verified resolution on the models you would actually ship, then add Voiceflow editor seats and phone numbers against your YourGPT plan tier. The cheaper credit is not the cheaper agent if one platform needs more builder hours to reach the same refusal and handoff quality. Have the person who will maintain the agent build the refund workflow in the trial, including the API failure case, and time it.

Who should stay on YourGPT

Stay when most of these are true:

• Your pilot on real tickets passes the refusal test, and the handoff packet opens cleanly in the helpdesk your team already uses.
• Measured credit burn on your chosen model leaves headroom in the plan at peak volume, not only in an average month.
• Support, sales qualification, and onboarding share one agent, and that overlap is saving a second purchase.
• You have an internal owner for knowledge, permissions, and failure review, and you prefer that control to a vendor program.
• No regulator, region, or channel on your roadmap needs a contract shape YourGPT Enterprise cannot sign.

Leaving a working agent to chase an enterprise logo is its own cost. The alternatives above earn their higher friction only when the job has changed: harder tickets, a different system of record, a voice or employee-service scope, or a finance team that needs to be paid in resolutions.

How to switch without losing the queue

1. Freeze the definition of done. A verified resolution means the customer did not come back within seven days and a human sampled the transcript. Write it down before any vendor defines it for you.
2. Export what YourGPT already knows. Knowledge sources, Studio workflows, functions, escalation rules, and the conversations the agent failed. That corpus is the training and test set for the next platform.
3. Shadow-run for two weeks. Same pilot tickets, same held-out set, every finalist. Score refusal, action correctness, handoff payload, and credits or units consumed per verified resolution.
4. Move one channel first. Website chat, then email, then voice or social. Keep YourGPT live on the remaining channels until the new handoff is boring.
5. Cut the old plan last. Cancel or downgrade YourGPT only after a full billing cycle on the new platform, including one peak period. Early cuts are how teams create a dark queue and a credit overage in the same month.

Take the AI agent buying checklist into vendor calls, and model the final two with the AI agent ROI calculator using your measured pilot numbers, not homepage rates.

Security and identity: what the agent is allowed to do

A typed email, order number, account ID, or phone number is a lookup key, not identity proof. If the agent uses one of these to fetch a customer record, the conversation is still anonymous: any caller who knows the key can see the same data. Customer-specific actions like refunds, account changes, and order lookups require an authenticated customer session or a server-validated identity flow, and then object-level authorization on every request.

Do not collect passwords, admin tokens, or one-time codes in chat. A secure login or verification screen should handle those. For a guest lookup, the agent can send a secure link to the stored contact address, so the customer opens their own record outside the chat window. The agent's instructions are not backend enforcement: whatever the prompt says, the API that performs the action must check permissions itself.

An API 200 or 202 response is not proof a business action completed. Asynchronous jobs can be pending or failed. Read the operation state or the authoritative record before confirming success to the customer, and on timeout inspect the job before retrying. Use idempotency keys or duplicate prevention so a retry does not double a refund or open two tickets. Customer confirmation and backend success are separate things: the customer saying "thank you" does not make a failed API call a resolution.

This applies to every platform in this article. Test each finalist with the action-level permissions you would run in production, not with a demo environment that skips identity validation.

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