Best fit
Repetitive tier-one questions at volume - support, lead qualification, or the same internal HR, IT, and ops questions - where the answers live in docs, FAQs, or SOPs the agent can retrieve from.
AI Agents & Chatbots
CoreLine builds AI agents that take on the high-volume, repetitive interactions your team can't scale by hand - support questions, lead qualification, internal HR and IT queries, onboarding, and scheduling. Each agent answers from your own knowledge, follows the escalation and tone rules you set, and hands off to a person the moment it should.
Customer-Facing
Customer support agents
Lead qualification agents
Website and WhatsApp widgets
Internal
HR, IT, and ops knowledge agents
Onboarding assistants
Scheduling agents
Grounded & Governed
Retrieval from your own docs
Escalation and fallback rules
Slack, Teams, and email channels
Engagement fit
When is an AI agent the right call?
Last reviewed
An agent pays off when you handle a high volume of interactions that follow patterns and lean on knowledge you already have written down. We ground the agent in your documentation so it answers from fact, not invention, and we define upfront where it must stop and pass the conversation to a human.
Not the right fit
Low interaction volume, conversations that need real human judgement or empathy, or a domain with no written knowledge to ground the agent in - it would invent answers.
First engagement
We agree the agent's scope, tone, escalation rules, and forbidden topics, connect it to your knowledge sources, and validate its answers against real questions before it faces a customer.
First deliverable
A grounded agent live on one channel, answering from your own content, with escalation to a human on anything it is unsure of and a log of every conversation for review and tuning.
Sound familiar?
Your support team answers the same tier-one questions hundreds of times a week
Leads go cold because no one qualifies them fast enough outside business hours
HR, IT, and ops get interrupted constantly with questions the handbook already answers
You tried a generic chatbot and it made things up or frustrated people into asking for a human anyway
How we build an agent
We build one agent for one job, grounded in your knowledge and bounded by clear rules, then deploy it to a single channel before expanding. Guessing is designed out from the start.
Scope & Ground
We define the agent's job, tone, escalation rules, and forbidden topics with you, then connect it to your knowledge - docs, FAQs, and SOPs - so every answer is retrieved from your own content rather than generated from thin air.
Build & Validate
We build the retrieval-and-generation pipeline, then test it against real questions your team already fields. We tune the fallback messages and the confidence threshold that decides when it answers and when it hands off.
Deploy & Tune
The agent goes live on one channel - website, WhatsApp, Slack, Teams, or email - with every conversation logged. We review those logs, close knowledge gaps, and expand channels once it earns trust.
What a delivered agent includes
Grounded
in your own knowledge
Answers are retrieved from your docs and FAQs, not invented by the model
Bounded
by escalation and tone rules
Forbidden topics, hand-off triggers, and fallback messages are set with you
Reviewable
with every conversation logged
Transcripts drive tuning, gap-closing, and confidence in what it says
What we work with
Using solutions such as React Native, Flutter, AWS, and many others makes it possible for us to upgrade your product as much as possible and to achieve a thriving collaboration. Here you will find our guide through our most used technologies and case studies related to each one.
AWS
AWS offers the tools to build powerful, flexible, and scalable applications, from compute and storage to databases and content delivery.
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Flutter
Simply put, Flutter is Google’s cross-platform framework that lets you build mobile, web, desktop, and embedded apps, all from a single codebase.
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React Native
React Native is Facebook’s cross-platform framework that combines the best of React with native platform features - perfect for new projects or enhancing existing ones.
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Other technologies we use
PHP
Node.js
Vue.js
React.js
Frequently asked questions
A generic chatbot answers from whatever the underlying model happened to learn and will confidently make things up. Our agents are grounded: they retrieve answers from your own documents, FAQs, and SOPs, and when the answer isn't there they escalate to a person instead of inventing one. You also control tone, forbidden topics, and exactly when it hands off.
It handles the repetitive, pattern-following volume: tier-one support, lead qualification, the same internal questions asked over and over. It should not handle pricing negotiations, legal or medical advice, or anything needing real judgement - those are set as forbidden topics and routed to the right person by design.
A website widget, WhatsApp, Slack, Microsoft Teams, or email. We usually launch on one channel, prove it, then expand. Each channel has its own setup considerations - WhatsApp's messaging window, Teams' Azure registration - which we handle as part of delivery.
That risk is what the grounding and escalation design is for. The agent answers from your content and carries a confidence threshold: below it, or on a forbidden topic, it hands off with a fallback message rather than guessing. Every conversation is logged so we can catch and close gaps quickly.
Scope and type drive it. A scheduling agent typically starts around EUR 5,000; support, lead-qualification, and onboarding agents commonly run from around EUR 8,000 to the low tens of thousands; internal knowledge agents similar, depending on the sources involved. Each is scoped and quoted before work begins.
The agent logic is usually built and validated in a few weeks; connecting it to a channel adds a few days depending on the platform. We deploy to one channel first, tune it against real conversations, and expand from there.
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