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AI customer support chatbot for online stores
AI customer support chatbot for online stores that answers from your own policies and live orders
AI customer support chatbot for online stores earns its place only when it answers from your real return policy, your real shipping times and the customer’s real order, because a chatbot that improvises costs more in refunds and trust than it saves in tickets. We build the chatbot on your site and your help desk, ground every reply in sources it can cite, set limits on what it may promise, and pass anything sensitive to your team with the whole conversation attached, for a fixed price instead of a fee on every conversation.
- Fixed price from $1,790, no fee per chat
- Every answer is grounded in your sources
- Difficult chats go to a person with context
- Platforms supportedShopify, WooCommerce, BigCommerce, Wix and custom storefronts, with order lookup wherever the platform exposes an API.
- Typical storeStores handling between a hundred and ten thousand chats and tickets a month with written policies and a help centre or product pages to learn from.
- Knowledge sourcesYour policy pages, help articles, product catalogue and past tickets, kept in sync so the bot never answers from an outdated page.
- Stores coveredOne store per plan, with extra storefronts and languages available as priced options.
- HandoffGorgias, Zendesk, Help Scout, Freshdesk or a shared inbox, with the conversation and a short summary attached.
- Live inTwo to three weeks for a standard build and up to six weeks for an advanced build with actions and several languages.
The costly disconnect
Why free and generic support chatbots disappoint the stores that install them
Most store owners meet the same sequence, which is a free chatbot installed in an afternoon, a few strange answers in the first week and a quiet decision to switch it off because nobody can say what it told customers.
Answers nobody checked
A generic chatbot trained on the open web can state a return window or a shipping charge you do not offer, and a tribunal has already held a business to what its chatbot said. The fix is not more effort but a better rule, and that is exactly what the customer support chatbot build for online stores puts in place.
No view of the order
Without a connection to orders, the bot cannot say where a parcel is, so the most common question still ends in a ticket. Many owners only notice this problem when a customer complains, so catching it earlier is one of the quiet benefits of customer support chatbot for online stores.
Pricing that grows with success
Per-resolution pricing, such as the figures help desks publish for their AI agents, means the more the bot helps, the larger the monthly bill becomes. Left alone, this problem repeats with every order, which is why customer support chatbot for online stores is built to handle it by rule and not by memory.
Free plans with hard limits
Free chatbot plans usually cap conversations, hide handoff and branding controls behind a paid tier and give no reporting on wrong answers. It is easy to overlook until a busy week exposes it, and by then the cost to online stores is already counted in refunds, hours or lost sales in the customer support chatbot work.
Handoff without context
A chat that jumps to email asks the customer to start again, which is exactly what they came to avoid. The fix is not more effort but a better rule, and that is exactly what the customer support chatbot build for online stores puts in place.
Stale knowledge
A bot trained once on last year’s policy keeps quoting it after you change terms, because nothing refreshes its sources. Many owners only notice this problem when a customer complains, so catching it earlier is one of the quiet benefits of customer support chatbot for online stores.
- $0.90is the price Gorgias lists for each automated interaction on annual billing, which shows how per-resolution pricing scales with volume.
- 2024is the year a tribunal held an airline liable for what its website chatbot told a customer, which is why answers must be grounded.
- 1 in 5tickets is an order status question in published helpdesk data, so a bot without order access leaves the largest category untouched.
Sources: Gorgias pricing page; Moffatt v Air Canada, 2024 BCCRT 149; Gorgias and Yuma published ticket category data.
What changes in your support once a grounded AI chatbot is live
Customers get instant, cited answers, and your team sees only the conversations that need a person.
Your chatbot today
- The chatbot answers from general knowledge.
- Order questions always become tickets.
- Nobody reviews what the bot told customers.
- Handoff loses the conversation.
- Costs rise with every resolved chat.
Your chatbot after the build
- Every answer comes from your policies, help articles and catalogue, with the source shown.
- Order questions are answered from the live order and tracking.
- Each conversation is logged and wrong answers are flagged for review.
- Handoff arrives with the full chat and a summary.
- Your cost is a fixed build fee plus the model usage, which we estimate in writing.
What your tickets show
- Fewer wrong promises.
- Fewer tickets about orders.
- A record of what the bot said.
- Customers do not repeat themselves.
- A predictable monthly cost.
What the bot answers alone
- Answering policy, shipping and product questions with a cited source.
- Looking up order status and tracking for verified customers.
- Capturing the email and reason before handing over.
- Logging every conversation and flagging low-confidence answers.
- Summarising the chat when a person takes over.
What a person decides
- Refunds and goodwill above the limit you set.
- Upset or high-value customers.
- Changes to the policies the bot quotes.
- Damage, safety or legal complaints of any kind.
- Any answer the bot marks as uncertain.
Which online stores should build a support chatbot instead of installing a free one
If your policies are written down and customers keep asking about them, a grounded chatbot pays back quickly, and the more specific your rules, the more it beats a generic one.
Stores with detailed policies
Made-to-order, perishable and customised goods have rules that generic bots get wrong and a grounded bot quotes exactly. Owners in this position often say the biggest change is the time returned each week, and customer support chatbot is built to deliver that first for online stores.
Stores with after-hours demand
Customers in other time zones get answers at once instead of waiting for the morning. This is the kind of situation where a fixed-price build is easier to justify than a monthly subscription for customer support chatbot for online stores.
Stores with a small team
One or two people can support far more customers when routine questions are handled first. We look at this situation in the audit and give online stores an honest view of whether the build is worth doing now or later in the customer support chatbot work.
Stores watching cost per ticket
A fixed build fee with predictable usage costs avoids the surprise of per-resolution billing at volume. If this describes your business, the customer support chatbot work for online stores is likely to pay back quickly, and we will tell you plainly if it will not.
Stores selling in several languages
Answers can be written and tested in each language you sell in. Owners in this position often say the biggest change is the time returned each week, and customer support chatbot is built to deliver that first for online stores.
Stores that tried a chatbot and stopped
A rebuilt bot with sources, limits and reporting is the usual fix for a first attempt that went wrong. This is the kind of situation where a fixed-price build is easier to justify than a monthly subscription for customer support chatbot for online stores.
The conversation events that decide how the chatbot answers
The chatbot treats every message as an event with a clear next step rather than a free-form chat.
question_receivedCustomer asks a question
The message is classified as policy, product, order or other before any answer is chosen. Each moment like this has its own rule, so customer support chatbot for online stores behaves the same way every time and never has to guess what to do next.
source_matchedA source is found
The bot retrieves the matching policy or article and answers using its wording, with a link. The event is logged with the rule that handled it, which lets you check later exactly what customer support chatbot did for online stores and why it did it.
order_lookupOrder identity confirmed
After the customer confirms their email or order number, the bot reads the live order and tracking. Because this trigger is handled separately from the others, a problem with it never delays the rest of the customer support chatbot work for online stores.
low_confidenceNo good source exists
When nothing in the sources supports an answer, the bot says so and offers a person instead of improvising. You decide how much of this runs without approval, and for online stores we usually begin with a review step until the first results look right in the customer support chatbot work.
sentiment_flagCustomer sounds upset
Frustration, legal words or repeated contact route the chat to a person at once. Anything this trigger cannot decide safely is passed to you with the record attached, so customer support chatbot for online stores never acts on a doubtful reading.
chat_closedConversation ends
The outcome and category are logged, and a short rating request can follow a resolved chat. We test this moment on your own records before launch, so the customer support chatbot build for online stores has met it before it meets a real customer.
One support chat followed from the question to a cited answer and a clean handoff
This shows how the bot stays within what it can support and how a difficult case reaches a person intact.
- 01
The question arrives
A visitor asks whether a personalised item can be returned, and the bot classifies it as a policy question about made-to-order goods. If the step cannot finish, the record says why and waits for you, so customer support chatbot for online stores never moves on with a half-completed task.
- 02
The source is found
It retrieves the personalised items section of your returns policy, which says such items are final sale unless faulty. You can see the outcome of this step before the next one starts, which keeps the customer support chatbot build for online stores under your control throughout.
- 03
The answer is cited
The reply states the rule in your wording, links the policy and offers help if the item arrived damaged, without inventing any exception. The step uses only the facts you supplied, so nothing in the customer support chatbot result for online stores depends on guesses or on outside sources you did not approve.
- 04
The customer says it is faulty
The bot asks for the order number, confirms ownership by email and reads the order to check the delivery date. Timing for this step is paced to the limits of your tools, so customer support chatbot for online stores keeps running calmly even when volume is high.
- 05
A limit applies
A faulty item may qualify for a replacement, which is outside the bot’s authority, so it writes a summary and passes the case to a person. A check at the end of the step compares the result with the input, which catches the small mistakes that otherwise surface days later in the customer support chatbot build for online stores.
- 06
The log is updated
The conversation is categorised as faulty personalised item, and the pattern appears in the weekly report if it repeats. Because the steps are separate, a change to one of them, such as a new rule for online stores, does not disturb the others in the customer support chatbot flow.
Pricing
Price your AI customer support chatbot
Choose a plan, tell us about your help desk and languages, and add any extras, and the total and live date update as you go.
What an AI customer support chatbot costs and what raises the price
Our fee is fixed and paid once, while the language model usage, your help desk and any tracking service are billed by their own providers.
Volume and size of the sources
A large catalogue or many policies take longer to index and test. A smaller first build can leave this factor for later, and online stores can add it at a priced step once the first results are in in the customer support chatbot work.
Actions the bot can take
Starting returns, issuing labels and applying capped refunds add rules and tests beyond answering. The tools around customer support chatbot bill separately, and we estimate their monthly cost for this factor so your running cost is known as well as the build for online stores.
Languages
Each language multiplies the wording checks and the test questions. Owners who run customer support chatbot for online stores often find this factor is the one that most changes the quote, which is why we ask about it first.
Help desk complexity
Several queues, tags and approval rules add configuration. We check this factor against your real records during the audit, so the number in the quote comes from your data and not from a guess in the customer support chatbot build for online stores.
Review and training needs
A larger team that reviews answers needs a fuller review queue and handbook. This is one of the factors we price in writing before work starts, so the customer support chatbot quote for online stores reflects your real situation and not an average.
Reporting depth
Topic maps and cost-per-ticket dashboards take extra build time. If this factor is small for you, it adds little to the price, and the written quote shows exactly how much it did add for online stores in the customer support chatbot work.
Language model usage, help desk plans and tracking services are billed by their providers.
Next step
Ready to price AI Customer Support Chatbot for Online Stores?
An AI customer support chatbot for online stores answers from your own policies, help articles and live order data on your website chat, hands difficult cases to your team with the history attached, and is priced as a fixed build instead of a fee on every conversation.
- One fixed price from $1,790, paid once
- Every answer cites your own policy
- A month of corrections after launch
Three more jobs the same chatbot can take on
Once the sources and the handoff exist, the chatbot can do more than answer questions.
Product advice from the catalogue
Shoppers ask which size, material or option suits them and receive answers drawn from product pages and size charts. The extra job is measured against a simple before-and-after comparison, so online stores can see whether it was worth adding to the customer support chatbot plan.
Lead capture for custom orders
Visitors asking for quotes leave their requirements and email, and your team receives a structured request. Because the groundwork for customer support chatbot on online stores already exists, this extra job needs very little new work and is quoted in writing before it starts.
A map of what customers ask
Weekly reports group questions by topic so unclear pages and policy gaps become visible. The same records and rules that power customer support chatbot for online stores are reused here, which keeps the cost low and the results consistent with the rest of the build.
Which data the chatbot reads and where each fact comes from
The bot is only as accurate as the sources behind it, so we list every source and who owns it.
| Store object | Fields we use | Direction | Source of truth |
|---|---|---|---|
| Policies | Returns, shipping, warranty, privacy text | Read and indexed | Your published policy pages |
| Help articles | Guides, size charts, care instructions | Read and indexed | Your help centre |
| Products | Titles, options, materials, stock status | Read before answering | Store catalogue |
| Orders | Status, items, fulfilment, tracking | Read for verified customers | Store orders |
| Conversations | Messages, category, outcome, rating | Written by the bot | Chat and help desk logs |
| Flags | Low-confidence answers and corrections | Written for review | Review queue |
Sources are re-indexed on a schedule and whenever you change a policy page, so the bot follows your latest wording.
Edge cases in support chatbots that we test before launch
A chatbot is judged by its worst answers, so we test the awkward questions deliberately and keep the results.
Questions outside the sources
The bot declines to answer and offers a person, and the missing topic is added to a list for your team. If a rule cannot settle the case safely, it goes to you with the record attached, and customer support chatbot for online stores waits instead of guessing.
Prompt tricks from visitors
Instructions typed by visitors cannot change the bot’s rules or make it reveal internal notes. Cases like this are the reason the customer support chatbot build for online stores includes a trial run, because a tidy example never shows them but real records do.
Policy changes mid-season
A changed policy page is re-indexed quickly, and old wording is removed so it cannot be quoted. Your answer to this case is added to the rule table, so the same situation is handled automatically the next time it appears for online stores in the customer support chatbot work.
Questions in other languages
The bot answers in the visitor’s language where it has tested wording and hands over where it does not. The weekly report counts how often this case occurs, which tells online stores whether it deserves a better rule or is simply rare in the customer support chatbot work.
Requests for a person
A direct request for a human is always honoured at once without arguing. Nothing about this case is hidden from you: it appears in the log with the date, the record and the rule that dealt with it in the customer support chatbot build for online stores.
Promotions and exclusions
Discount questions are answered from live promotion rules, including exclusions, so no unofficial offer is promised. We list the cases that matter most for customer support chatbot in your handover guide, so anyone on your team can understand how online stores deals with them.
Free chatbots, help desk AI add-ons and a built chatbot compared
A free AI customer service chatbot is fine for testing, a help desk add-on is convenient for large teams, and a built chatbot suits stores that want control over answers and cost.
| Free chatbot | Help desk add-on | Checkout Loom build | |
|---|---|---|---|
| Answers cite your own policies | Partly | ✓ | ✓ |
| Reads live order data | – | ✓ | ✓ |
| Reviews wrong answers | – | Partly | ✓ |
| Fixed cost regardless of volume | ✓ | – | Partly |
| Your rules on refunds and promises | – | Partly | ✓ |
| Cost pattern | Free up to a cap | Per resolution | Our build fee plus usage costs |
Usage costs for the language model are small at typical store volumes, and we estimate them in writing before you commit.
How the support chatbot is built and what each part costs
We choose the parts that fit your store and write down what each one will cost every month next to our fixed build fee.
A language model with retrieval
A current model reads only the passages retrieved from your policies and articles, with instructions that forbid inventing terms. We test the layer on your own data before launch, so customer support chatbot for online stores is proven on real records and not on tidy examples.
Your help desk
Gorgias, Zendesk, Help Scout or Freshdesk receive handoffs, tags and summaries, so your team keeps one queue. If the layer fails or changes its terms, the handover guide explains how to swap it, so online stores is never stranded by one supplier in the customer support chatbot work.
Order and tracking connections
Read access to orders on Shopify, WooCommerce or BigCommerce, and carrier tracking through a tracking service. Its role is written in plain language in the handover guide, which means a new team member at online stores can understand it in a few minutes in the customer support chatbot work.
A chat widget
A lightweight widget matched to your brand, loaded after the page so it does not slow checkout. The monthly price of this layer is written next to our fixed fee before you commit, so the true cost of running customer support chatbot for online stores is clear from day one.
A review queue
A simple view of low-confidence answers where you correct the sources and improve the next reply. You can replace this layer later with another tool, because the rules live in a table you own and are not locked inside any one product in the customer support chatbot build for online stores.
Make, Zapier or n8n
Glue for connections that lack a native integration, with retries and alerts on every step. We choose the lightest option that does the job for online stores, since a simpler layer means fewer things to maintain and fewer ways to fail in the customer support chatbot work.
How we keep the chatbot accurate and available
Accuracy depends on sources and testing more than on the model, so that is where the reliability work goes.
Cited answers only
A reply must rest on a retrieved source, and otherwise it becomes a handoff. During the month of corrections after launch, any gap in this safeguard is fixed in writing and the fix is added to the handover guide in the customer support chatbot build for online stores.
A test set of real questions
Every release is run against your own past questions, and the replies are compared with what your team sent. Reports on how often the safeguard fires show online stores whether the customer support chatbot work is getting steadier or whether a rule needs attention.
Re-indexing on change
Sources refresh on a schedule and whenever you publish a policy change. Because this protection is written as a rule and not as a habit, it keeps working when staff change or the business gets busier in the customer support chatbot build for online stores.
Alerts for failures
Index failures, lookup errors and model outages raise alerts that name the part that failed. When something goes wrong in customer support chatbot for online stores, the failure is named, retried within safe limits and listed for you, instead of vanishing into a log nobody reads.
Response time tracking
First reply and resolution times are tracked so slowdowns show early. This safeguard runs on every batch and not only on a schedule, so a problem in customer support chatbot is caught before it can spread to the next record for online stores.
Written answers from the owner
Anything that goes wrong in the first month is answered by the owner in writing with the cause and the fix. Each alert names the rule, the record and the next step, which means you can fix the cause from the message alone without opening any tool in the customer support chatbot build for online stores.
Privacy, permissions and data handling for a support chatbot
A chatbot sees customer messages and order details, so we limit access and say clearly where the data goes.
Least-privilege access
The bot reads orders and writes only the notes and tickets you approve. The arrangement is reviewed at the end of the month of corrections, and any access that is no longer needed is removed in writing in the customer support chatbot build for online stores.
Verified before order details
Order information is shared only after the customer confirms the email or order number. Customer details are never used to train outside models, and drafts for online stores are built only from the facts and records you supply in the customer support chatbot work.
Keys held by you
Every API key is created in your own accounts, so you can cut our access off with one click. We explain in plain words what each permission allows, so the person approving it for online stores knows exactly what they are agreeing to in the customer support chatbot work.
Minimal data to the model
Only the question, the retrieved passages and the needed order facts go to the model, never payment details. At the end of the engagement you receive a list of every connection that was made, so nothing stays open by accident in the customer support chatbot build for online stores.
Retention you control
Chat logs are kept for a period you choose and deleted on request. For customer support chatbot on online stores, access is limited to what the job needs and can be withdrawn in a minute, so ending the work leaves nothing behind.
Access removed at handover
You get a list of everything we changed, and our logins are closed on the day we finish. Nothing in this arrangement requires you to share a password, because access is granted from your own account in a form you can revoke in the customer support chatbot build for online stores.
What you receive with your AI customer support chatbot
Each plan ends with a working chatbot, a tested set of answers and the notes to keep it accurate.
- A chatbot on your site that answers from your policies, articles and catalogue.
- Live order and tracking lookup for verified customers.
- Refund and promise limits written into the bot’s rules.
- Handoff to your help desk with the conversation and a summary.
- A question set drawn from your own past chats, with every result recorded.
- A review queue for low-confidence and corrected answers.
- A weekly report of the topics customers ask about.
- A written estimate of monthly usage costs for your volume.
- A month of repairs once the bot is live.
Verified client reviews and the evidence behind a grounded chatbot
These are the unedited words of verified clients of the Checkout Loom team, followed by the facts that shape how we build support chatbots.
- $0.90per automated interaction is the published Gorgias price on annual billing, one reason we compare fixed builds with per-resolution fees.
- 2024is when a tribunal held a business responsible for its chatbot’s statements, so every answer we build cites a source.
- 30 daysof fixes follow launch so the bot is stable before we step back.
Pricing is taken from vendor pages and confirmed again at build time; the legal case is Moffatt v Air Canada, 2024 BCCRT 149.
★★★★★
The Checkout Loom team built an automation that simplified my workflow and worked perfectly from day one. Communication was excellent, delivery was fast, and I would happily recommend the service.
RebeccaUnited States
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They set up my automation remarkably fast and executed everything perfectly. Communication and service were top tier, so I could not be happier with the results and I highly recommend the Checkout Loom team.
AbbieGermany
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★★★★★
The automation was set up in a clean and practical way by the Checkout Loom team. It saved me a lot of manual work and made the whole process much easier.
JanoUnited Kingdom
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★★★★★
I highly recommend ordering from the Checkout Loom team. I had slow responses and poor communication with other providers, but this team was great and I am very happy with their service.
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★★★★★
I am a returning customer because my first AI automation workflow worked so well. Once again everything was built exactly as requested, and the whole process was reliable from start to finish.
WilliamUnited States
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★★★★★
The determination and commitment to quality from the Checkout Loom team were clear from day one. They understood my requirements perfectly and completed everything with professionalism and care, and it is rare to find a team this dependable.
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It was an excellent experience from start to finish, and the automation works reliably with everything configured properly. It is clear that the team has strong knowledge of automation, and I will definitely be returning for future work.
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The team carefully investigated the problem, tested different scenarios and delivered a dependable solution. A duplicate contact issue had been causing problems for quite some time, and it is finally resolved.
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How the chatbot build runs from sources to a launched, monitored bot
We build from your own words, because a bot trained on them gives answers your team recognises.
- 01
Collect the sources
We gather your policies, help articles and a batch of past tickets and list gaps where customers ask what you have never written down. You see the result of this step in writing before the next one begins, which keeps the customer support chatbot build for online stores on schedule and under your control.
- 02
Write the rules
We write what the bot may say, may do and must never promise, and agree the rules with you in writing. If this step shows a problem, we say so at once and propose the fix, and the date for customer support chatbot is moved in writing rather than quietly for online stores.
- 03
Connect orders and handoff
We connect order lookup and your help desk with the narrowest access that works. Nothing in this step touches your live customers, so online stores can review it calmly and ask for changes before anything goes public in the customer support chatbot work.
- 04
Test against real questions
We run your past questions through the bot, compare its replies with your team’s and fix the differences. The step has a clear finish line, which means you always know whether the customer support chatbot work for online stores is on time or running behind.
- 05
Launch and watch
We switch the bot on for a share of visitors first, review its conversations and then widen it. Questions that arise here are answered in writing, so there is a record of every decision made about customer support chatbot for online stores.
- 06
Hand over
You receive the playbook, the review queue and thirty days of fixes, and we remove our access. We keep each step small enough to check, because large steps hide mistakes and make the customer support chatbot build harder to correct for online stores.
Estimate what a support chatbot could save compared with per-resolution pricing
Fill in your own chat volume and the estimate compares a per-resolution fee with the running cost of a built chatbot, using figures you can change.
Chats the bot resolves each month–
Yearly cost at the per-resolution fee–
Yearly usage cost of a built chatbot–
The comparison leaves out our one-time fee and your help desk plan, which you pay in both cases, and it is meant for planning only.
AI customer support chatbot questions, answered in full
These are the questions store owners ask about accuracy, cost, free options and examples before they order a support chatbot.
What is an AI customer support chatbot for an online store?
It is a chat assistant on your website that answers customer questions using a language model, but only from your own policies, help articles, catalogue and live orders, and that passes difficult cases to your team with the conversation attached.
Is there an AI customer support chatbot free option that is good enough?
Free plans are useful for testing, but they usually cap conversations, offer little control over what the bot may promise and give no reporting on wrong answers. For a store with written policies and real order questions, a grounded build is usually safer.
What do AI customer service chatbot examples look like in practice?
Typical AI customer service chatbot examples include a bot that quotes your return window and links the policy, one that reads an order and gives the real delivery day, and one that suggests a size from your chart and offers a person when the customer is unsure.
How does AI customer service chatbot pricing usually work?
AI customer service chatbot pricing is commonly a monthly plan plus a fee per automated resolution, such as the $0.90 per interaction that Gorgias lists on annual billing. Our model is a fixed build fee plus the language model usage, which we estimate in writing for your volume.
How do you stop the chatbot from making up answers?
It may answer only from passages retrieved from your sources, it must cite them, and when nothing supports an answer it says so and offers a person. Each release is tested against your real past questions before customers see it.
Can the chatbot look up orders?
Yes, after the customer confirms the email or order number, the bot reads the order and tracking and gives the real delivery day, using read access only unless you approve actions such as returns.
Does the chatbot replace my support team?
No, it handles routine questions so your team can spend time on the conversations that need judgement, and any customer can ask for a person at any time.
Which help desks and platforms work with it?
Gorgias, Zendesk, Help Scout and Freshdesk work for handoff, and orders can be read on Shopify, WooCommerce, BigCommerce and most platforms with an API, with any gap quoted in writing.
How soon can the chatbot go live and what do you need first?
A standard build is usually live in two to three weeks. We need your policy pages, help articles, an export of recent tickets and read access to your store, and we send a written checklist after you order.
Will running costs rise when more customers use it?
Model usage grows with volume but stays small per chat, and we show the estimate for your volume in writing. Because our fee is fixed, you do not pay a charge on each resolution.
Give your customers instant answers you can stand behind
Choose a plan in the pricing calculator and continue to checkout, or request a quote listing your platform, help desk and the questions customers ask most. We reply in writing within one working day with a confirmed start date and a short checklist, so source collection starts straight away.
- One fixed price from $1,790, paid once
- Every answer cites your own policy
- A month of corrections after launch