CONVERSATIONAL AI
Not a decision tree with a chat window. An AI bot trained on your business, deployed on the channels your customers already use, with clear handoff logic for the moments a human needs to step in.
WHERE WE DEPLOY
We build and deploy across the platforms your customers already use, so the conversation finds them rather than asking them to go somewhere new.
Website Chat
Live chat on your website that handles FAQs, qualifies leads, routes support tickets, and books meetings without human involvement.
Most popular for lead gen and support
BUILT ON
Intercom, Crisp, Tidio, Drift, or custom widget
Reach customers in the channel they actually use. Support, order updates, appointment reminders, and two-way conversations at scale.
2+ billion active users worldwide
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WhatsApp Cloud API, Twilio WhatsApp, 360dialog, or MessageBird
Voice
Phone bots for intake, triage, and FAQ handling, built for the customers who still prefer to call.
IVR replacement without the frustration
BUILT ON
Twilio Voice, JustCall, or Vonage with OpenAI Realtime or ElevenLabs voice
SMS
Short message bots for appointment reminders, order confirmations, and quick support queries where an app is overkill.
98% average open rate
BUILT ON
Twilio SMS, Vonage, or MessageBird
Slack
Internal bots for your team: IT helpdesk, HR queries, operations assistants, and anything employees ask the same person too often.
High-ROI for internal operations
BUILT ON
Slack Bot API or Microsoft Bot Framework
Custom Channel
Any channel with an API. Teams, LINE, Telegram, in-app chat, or a proprietary support platform. If your customers are there, we build there.
Bespoke integrations available
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Vercel AI SDK, direct LLM APIs, or custom orchestration
SEE IT IN ACTION
Three different use cases, three different channels. The same principles underneath.
UNDER THE HOOD
The gap between a keyword-matched bot and an LLM-based bot is not incremental. These are the four capabilities that separate them.
Scripted bots break the moment a customer phrases something differently. An LLM-based bot understands what the customer means, even when the wording is unexpected, informal, or in a second language.
We fine-tune intent classification on your actual conversation data so the bot handles real-world phrasing, not just the clean version from a script.
A bot that forgets what you said two messages ago is worse than no bot at all. Ours maintains full context through the conversation and can reference earlier messages to give coherent, connected answers.
We configure session memory with appropriate time windows and slot-filling logic so the bot tracks order numbers, names, and preferences throughout a single conversation.
The biggest trust failure is a bot that makes things up when it does not know the answer. We build explicit out-of-scope detection so the bot says it does not know rather than hallucinating a confident wrong answer.
Every bot ships with a confidence threshold and a curated set of fallback responses. When confidence drops below the threshold, the bot acknowledges the gap and offers a handoff.
A bot that can only answer questions is half useful. We connect it to your CRM, helpdesk, calendar, order management system, or database so it can look things up, take actions, and update records mid-conversation.
We handle API integrations, authentication, and error handling so the bot returns live data rather than generic answers.
THE BUILD PROCESS
Every phase has fixed deliverables and a clear handoff point so you always know where the project stands.
Scoping
Week 1
Knowledge and integrations
Week 2
We connect the bot to the systems it needs. The knowledge base (Notion, Confluence, Google Drive, custom CMS), the CRM (HubSpot, Salesforce, Pipedrive), the helpdesk (Intercom, Zendesk, Help Scout), the calendar, the product API. We also wire up the channel layer (Twilio for voice and SMS, WhatsApp Cloud API for messaging, native SDKs for Slack and Teams).
Build and Integration
Weeks 3 to 4
Test and Launch
Weeks 5 to 6
HUMAN HANDOFF LOGIC
A bot that handles everything is a bot that fails visibly. We design the handoff logic during scoping, not as an afterthought.
The diagram shows the default flow. Scope triggers, confidence thresholds, and escalation routing are all configurable per your support workflow.
When a handoff fires, the human agent receives the full conversation history so the customer does not have to repeat themselves.
Decision flow: A message arrives. If it is within bot scope, the bot drafts a response and runs a confidence check. High confidence leads to the bot replying directly. Low confidence triggers a handoff with the draft attached. If the message is outside scope or sensitive, it immediately routes to a human with full context.
THE STACK WE BUILD ON
We are not married to any single platform. Each engagement uses whichever combination of these tools fits the channel, the scale, the budget, and the data sensitivity. Here is what we have actually shipped on.
VOICE AND TELEPHONY
Inbound and outbound calls handled by AI agents. The telephony layer carries the call. The voice model carries the conversation.
WHATSAPP AND MESSAGING
We deploy on the official WhatsApp Cloud API direct from Meta when scale and cost permit, and through certified BSPs (Business Solution Providers) when their tooling makes sense.
SPEECH MODELS
Modern speech models that handle interruptions, pauses, accents, and natural conversational rhythm. We pick the provider based on language, latency, and cost per minute.
LANGUAGE MODELS
We use managed providers for most engagements and open-source models when the use case requires self-hosting. Sometimes one bot uses two different models in different roles.
CHAT PLATFORMS
We deploy into existing chat tools when they already serve your team, or we ship a custom widget when a generic tool will not do.
FRAMEWORKS AND ORCHESTRATION
Frameworks we reach for when they help, custom code when they get in the way. We do not lock you into any framework's roadmap. The framework is not the product.
If you already use a tool not on this list, we have probably worked with it. If we have not, we will figure it out before we promise anything.
SECURITY AND COMPLIANCE
We handle the questions your legal and IT teams will ask before they ask them.
Data never trains the base model
Your conversations, documents, and customer data are used to configure your bot and nothing else. We work with providers who offer zero-retention API agreements and confirm this in writing before build starts.
Role-based access and audit logs
Bot admin access is controlled by role. Every configuration change is logged. You can see who changed what and when, which matters when something behaves unexpectedly.
Data residency options
If your compliance team requires data to stay within a specific region, we configure accordingly. EU, US, and UAE residency options are available depending on the underlying provider.
PII handling and redaction
We configure automatic redaction of sensitive fields like card numbers, ID numbers, and passwords before they reach the model layer. Logs never store data that should not be stored.
On-premise deployment available
For regulated industries where no data can leave your infrastructure, we build against locally hosted models. Slower to set up, but fully air-gapped if that is what your legal team needs.
MULTILINGUAL SUPPORT
Modern LLMs handle natural language across most major languages without separate training. We test language coverage specifically for your user base before launch.
Language detection
The bot detects the customer's language from the first message and responds in kind without any setup by the customer.
Voice quality matches text quality
For voice bots, we use modern speech models like OpenAI Realtime, ElevenLabs, and Cartesia, paired with telephony from Twilio, JustCall, or Vonage. The combination handles natural pauses, interruptions, accents, and conversational rhythm. We test with native speakers per language, not just speech-recognition metrics.
Language-specific QA
We test the bot in all languages relevant to your user base, not just English, before we sign off on go-live.
COMMON QUESTIONS
Still have questions?
Every project starts with a free scoping call. Bring your specific use case and we will tell you honestly whether a bot is the right solution.
Platform chatbots use rule trees and keyword matching. They break when someone phrases something unexpectedly, and they cannot take actions outside the platform. What we build is LLM-based, trained on your specific content, connected to your external systems, and able to handle freeform natural language. The gap in capability is significant.
It depends on your requirements. We work with OpenAI, Anthropic, Google Gemini, Mistral, and locally-hosted open-source models. The choice depends on cost, latency, data residency requirements, and the complexity of the use case. We make a recommendation after scoping.
Every bot we build has a confidence threshold. When confidence drops below it, the bot acknowledges uncertainty rather than generating a plausible-sounding wrong answer. We also run adversarial testing before launch to find the edge cases before your customers do.
It can take actions if we connect it to the right APIs. Booking appointments, updating CRM records, processing simple returns, creating support tickets, sending emails on behalf of a user. What it can do depends on what APIs are available and what permissions you want to grant it.
We define the handoff triggers during scoping: low confidence, specific topics, certain keywords, or explicit customer requests. When a trigger fires, the bot passes the full conversation history to the human agent so they do not have to ask the customer to repeat themselves.
It keeps working. For tasks it can complete autonomously it handles them and sends a summary. For tasks that require human involvement it logs the conversation, sets expectations with the customer about response time, and routes the ticket for the next business day.
Most bots go from brief to live in 4 to 6 weeks. Simple single-channel bots with limited integrations can be faster. Multi-channel deployments with deep CRM integration take longer. We give a firm timeline after the scoping call.
Yes. We offer monthly retainer packages that cover monitoring, retraining on new content, performance reporting, and scope expansion. We also offer a handover model where your team manages it after launch with documentation and training from us.
Yes. Modern LLMs handle most major languages well without separate training. We test the languages relevant to your user base during QA. If you need a bot that defaults to Arabic or switches languages mid-conversation based on user input, we configure that explicitly.
Project cost depends on number of channels, number of integrations, and complexity of the knowledge base. Most initial builds fall in the range of a mid-sized marketing campaign. Retainer support is priced separately. We give a fixed-price quote after the scoping call.
GET STARTED
Tell us the conversation you want to automate. We will design the architecture, identify the integrations, and give you a fixed-price quote.
Hi, I'm Ari 👋
I can help you automate tasks and answer questions about your business.