Clevertar is an Australian Conversational AI company that designs, builds, and operates AI agents that help retailers and service brands sell more and support customers smarter across digital and in-store channels.
Clevertar is based in Adelaide, South Australia, with customers across Australia and beyond.
Clevertar has been developing AI solutions for over 10 years.
We build AI agents that can answer questions, guide customers to the right products or services, and reduce support workload by resolving common enquiries automatically. We also provide analytics and ongoing optimisation so performance improves over time.
Clevertar combines conversational design, AI engineering, and operational delivery. We focus on brand-safe, high-quality customer interactions, measurable performance outcomes, and ongoing improvement, not just “install and forget.”
No. Depending on the use case, we build sales agents, support agents, and hybrid agents across web and other channels. We can also support optional virtual characters and speech AI where it makes sense.
Retail and eCommerce, telecommunications, government and public sector, education, healthcare, and services. If your business has high volumes of repeat questions or complex purchase decisions, we can usually help.
Both. We have an AI platform that can be rapidly deployed and integrated into your existing systems. We also work on custom builds: tailored AI agents for websites, service brands, enterprises, and special requirements.
We deploy AI agents across:
1. AI Sales Agents: guide shoppers, recommend products, handle objections, and increase conversion and
average order value.
2. AI Support Agents: resolve common enquiries, reduce ticket volume, and triage complex cases to humans.
3. Omnichannel and in-store agents: bring the same guidance into physical environments using QR codes,
tablets, kiosks, or store-assisted experiences.
Product discovery, comparisons, sizing/fit guidance, compatibility checks, bundles and upsells, promotions guidance, store and delivery questions, and purchase reassurance.
Order tracking, shipping and delivery queries, returns and exchanges, warranty and troubleshooting, account questions, plan selection (telco), and escalation to human support when needed.
Yes. Many teams start with support deflection and expand into sales guidance once the AI has strong coverage of policies, product knowledge, and user intent patterns.
Yes. AI agents are designed to handle demand surges without adding headcount, as long as your content, policies, and product information are up to date.
Yes. The core difference is the content, the qualification flow, and the integration into your CRM or lead workflows.
Yes. Many organisations deploy internal AI assistants for staff knowledge, SOP guidance, policy lookup, and triage. (Custom Builds are typically best for this.)
Yes. Our platform supports multilingual customer interactions, and Custom Builds can support multilingual deployments as well.
Traditional chatbots rely heavily on rigid rules and decision trees. Clevertar uses modern language AI with structured guardrails, approved knowledge, and performance monitoring so conversations feel natural while staying on brand and accurate.
No. Clevertar can be model-agnostic for Custom Builds, selecting models and infrastructure that fit your requirements for speed, cost, privacy, and governance.
We reduce risk through a combination of:
1. Using approved sources (your site, policies, knowledge base, product catalog)
2. Retrieval-based answering where appropriate- Guardrails and response constraints (including “only answer from these sources” policies)
3. Clear fallback behaviour when the AI is uncertain (ask a clarifying question, offer next steps, or escalate)
It should not guess. The agent should either ask a clarifying question, direct the customer to the most relevant official page, collect details for follow-up, or hand off to a human support path.
Yes. We can tightly scope topics, tone, and allowed sources. With our AI platform, you can also tighten instructions so it only responds about your store and product range.
Yes. We incorporate brand language, style guidance, and tone rules so responses are consistent with your brand.
Yes. For retail, this is one of the primary strengths. The AI can ask a few questions, narrow down options, and explain recommendations in plain language.
Yes. Product availability logic is based on store stock numbers. If your store uses pre-order logic, you can add instructions so the AI explains pre-order availability correctly.
Our platform can also provide order status updates when the customer provides the correct order number and the email address associated with the order.
Typically, not by default. Most deployments start with answering and guiding. If you want action-taking workflows, that is usually handled via Custom Builds with explicit safeguards, authentication, and approvals.
Yes. If a customer provides contact details for follow-up, our platform can surface this for your team so you can respond.
Yes. You can keep human support available and the AI can direct customers to human help when required. Human handover can take place with live chat integrations, CRM integrations, or simply via phone and email to your team.
We offer both. Clevertar builds AI agents for web chat and for telephony, including inbound call answering and outbound calling use cases.
Yes. Common outbound use cases include CSI surveys, appointment reminders, service booking prompts, and follow-up calls. We design the talk track, handle common objections, and route or escalate when needed.
Voice quality is a core focus. The biggest factors are the voice model used, response latency (avoiding awkward pauses), turn-taking (handling interruptions cleanly), and conversation design (how it starts, how it recovers, how it confirms details). We can share voice samples early so you can assess fit.
Yes. Many of our customers specifically require Australian accents and delivery style.
Yes. We can enable interruption handling and natural turn-taking behaviours so conversations feel less scripted and more like a real call.
Yes. We design for fast responses and can implement natural fillers when the system needs to fetch data, for example short acknowledgement phrases. For some experiences we can also add optional ambient cues (like subtle office sounds) if appropriate for your brand.
No. The goal is natural conversation. The AI can route the conversation based on what the caller says, rather than requiring keypad navigation.
Yes. If the caller requests a human, or the AI detects it should escalate, it can transfer the call.
Yes. We can provide a short summary of what the caller is asking and what has already been captured, so your team does not need to start from scratch.
Yes. Inbound “front desk” call answering is a common pattern. The AI can answer FAQs, capture details, and escalate to the right person when required.
Yes, when integrated with your booking or calendar system. The agent can capture required details and either complete the booking or hand off cleanly.
A: It depends on your disclosure preference and how the interaction is designed. Some organisations choose explicit disclosure. Others focus on service outcomes. We can implement whichever approach best aligns with your brand and risk profile.
Yes. We can integrate with CRMs for lead capture, handoff, and workflow triggers, and we can also ingest knowledge base content where relevant.
Yes. We can integrate with common calendar and scheduling systems so the agent can help customers schedule or request appointments.
Yes. We integrate via APIs and can build custom integrations where needed, including looking up customer/order details, availability, booking slots, or other operational data.
Yes. We can combine your website content with approved documents, knowledge base articles, and live system integrations so the agent can answer accurately and contextually.
Yes, with the right integration. The agent can ask for the appropriate identifier (for example email and order number) and then query your system.
No. We integrate with your existing stack. Our AI agents sit on top to handle conversations, answer questions, capture intent, and route outcomes back into your tools.
A typical build follows five phases:
1) Configure and discovery
2) Content transformation
3) Acceptance testing
4) Launch and deployment
5) Optimisation and iteration
Many pilots can be deployed in 1-2 weeks, depending on complexity, integrations, and how ready your content is. If voice is required, we often recommend validating the knowledge and flows in text first, then enabling voice once accuracy and behaviour are proven.
We aim to minimise client lift. Typically we need:
A nominated internal champion for reviews
A shortlist of your top 20 to 40 common customer questions
A review process to confirm tone, escalation rules, and approved sources
We handle the build, testing, and iterations with you.
Usually: Your goals and success criteria, Your key policies (shipping, returns, warranty, support)- Your product/service content (catalog, FAQs, guides, comparisons)- Your brand voice guidance- Access to relevant systems if integrations are required (Custom Builds)- A subject matter expert for review and sign-off
No. We typically transform what you already have (FAQs, policy pages, product pages, knowledge articles) into conversational coverage, then expand based on real customer questions.
We create a benchmark set of real questions your team receives and test the agent’s answers against that set. We then iterate until you’re comfortable with accuracy, tone, and escalation behaviour. Additionally, we run secondary AIs which monitor all conversations for quality purposes.
We use conversation analytics to find gaps, identify new intents, and continuously refine content and behaviour based on what customers actually ask.
Yes. A common approach is to embed the agent directly on FAQ and Contact pages to intercept questions before a customer emails or calls.
Yes. Some retailers position the agent as a conversational search and recommendation layer, especially when product ranges are large or hard to navigate.
Yes. Large catalogs are often where conversational guidance is most valuable, because customers can describe what they need in plain language and the agent can narrow options.
The goal of AI Sales Agents is to reduce friction and answer questions at the moment of intent. Results depend on placement, product complexity, and content readiness. We track assisted conversion and continuously optimise based on real conversations.
You can see activity immediately once the agent is live, but performance typically improves over the first weeks as we close gaps found in real conversations and optimise placement and prompts.
Yes. The agent can recommend complementary products or higher-fit options based on the customer’s use case, budget, and preferences.
Yes. After-hours support is a common starting point. The AI can answer FAQs, capture details for follow-up, and escalate urgent cases based on your rules.
Yes. We can design escalation paths so the AI handles the basics, then hands off for advanced or specialist enquiries.
Yes. For escalations, we can provide structured summaries so your team can respond faster and with full context.
Pricing is typically usage-based, aligned to the number of customer conversations and channels, plus any setup and managed service requirements.
No. A common model is pricing per conversation, which makes forecasting simpler than token-based billing.
A conversation usually refers to a single customer session with back-and-forth messages during that session. We define this clearly in your proposal so usage is predictable.
Yes. We can configure hard caps (the agent pauses or becomes unavailable after a quota) or enable overages (the agent continues and you pay per additional conversation). You choose what fits your finance preference.
Yes. Many clients start with a pilot to validate quality, integrations, and ROI before scaling.
Yes. In addition to our usage-based pricing, for custom requirements,we also provide Custom Builds for broader and deeper needs.
Pricing depends on channels, integrations, governance requirements, conversation volume, and managed service scope. We typically provide a scoped proposal after discovery.
