Healthcare scheduling
Appointment booking assistant
Handles appointment requests, reschedules, and insurance verification for a network of clinics across six cities. Runs in Ukrainian and English.
1,840
calls handled last month
Voice assistants that understand context
We build conversational AI systems that handle real customer questions without breaking down or sending people to support tickets.
see what we buildBefore we start
You need more than a budget
Building a voice assistant means defining what it should say when someone asks an unexpected question. It means writing scripts for edge cases that happen once every two weeks but still need answers.
You need someone on your team who can review conversation logs and decide whether the bot misunderstood the user or the user asked something outside the scope. That person will work with us through testing and after launch.
Most projects take eight to twelve weeks from kickoff to deployment. We spend the first two weeks mapping conversation flows with your team. If you cannot commit to regular check-ins during that phase, the timeline stretches and the system ends up less accurate.
Three systems handling conversations right now
E-commerce support
Order status chatbot
Answers questions about shipping, returns, and product availability. Integrated with inventory and logistics APIs to pull live data.
4,200
conversations per week
Financial services
Loan application assistant
Walks applicants through eligibility checks and document submission. Escalates to human agents when it detects confusion or complex cases.
680
applications started this month
How this differs from chatbot templates
Template platforms
You get a drag-and-drop interface and prebuilt responses. Works fine for FAQ pages. Breaks down when users rephrase questions or combine requests.
No one trains the model on your specific terminology. No one maps the conversational patterns unique to your business.
Our approach
Custom training on your data
We analyze real support tickets and call transcripts from your business. We train the model to recognize how your customers actually phrase requests, not how a template assumes they will.
Offshore development shops
They build what you spec. If the spec misses edge cases or assumes users will behave predictably, you get a system that works in theory but fails in production.
Revisions take weeks because the team is juggling six other projects and no one on their side understands your domain deeply enough to suggest improvements.
Who comes to us and what problem they had
Hanna Levchenko
Operations manager at a logistics company handling hundreds of delivery status calls daily. Support team was overwhelmed and response times were creeping past acceptable limits.
- Needed a system that could pull live tracking data and explain delays without human intervention
- Required escalation to agents only when shipments were genuinely lost or damaged
- Had to work in both Ukrainian and Russian because customer base spans multiple regions
Dmytro Kovalenko
Product lead at a SaaS company with a complex onboarding flow. New users were dropping off because they could not figure out configuration steps without watching tutorial videos.
- Wanted an in-app assistant that could guide users through setup based on their specific use case
- Needed it to recognize when a user was stuck and offer contextual help without being intrusive
- Required integration with their analytics platform to track where assistance improved completion rates
Olena Shevchuk
Director of patient services at a dental clinic network. Receptionists spent most of their time answering the same questions about appointment availability, insurance coverage, and preparation instructions.
- Needed a voice assistant that could handle appointment booking and answer pre-visit questions accurately
- Required HIPAA-compliant data handling because conversations would reference patient information
- Had to sound natural enough that patients would not immediately ask to speak to a human