Results for "Conversational AI”
Conversational AI
Relevance
We build AI-powered systems that help companies uncover high-value opportunities, accelerate workflows, and unlock new business potential. Leveraging the latest in multi-agent systems, generative AI, and computer vision, and backed by deep engineering expertise, we design and deploy enterprise-grade solutions that drive measurable ROI.
Conversational AI
AI & ML Solutions
Data Analytics and Visualization
HR Platforms
E-Commerce
Python
Next.js
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RELEVANT TALENT
CASE STUDIES
Legal Copilot: Streamlining Legal Document Workflows with AIThe legal department struggled to process multiple provider contracts efficiently, taking weeks to make subtle changes or ensure policy compliance. This manual approach created significant delays in business operations and vendor onboarding processes.
DATAFOREST is an AI-native product and data engineering company delivering outcome-driven solutions with senior AI-capable teams. We build data platforms, AI workflows, and agentic systems that automate operations, unify data, and accelerate delivery using LLMs, RAG, and modern engineering practices.
Conversational AI
Integrations
ERP
Databases
Data Analytics and Visualization
Python
PostgreSQL
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RELEVANT TALENT
CASE STUDIES
Real-Time AI Voice Agent for Cold CallingDeveloping a real-time voice-to-voice AI agent for cold calling, delivering human-like conversations, handling noisy environments, and integrating with the client’s CRM and ATS.
Rubix Code DOO is a full-stack AI development company specialising in building compliant AI ecosystems across healthcare, real estate, business intelligence, and legal tech. We partner with clients to design and deliver end-to-end AI solutions, from data infrastructure and cloud-based inference to industry-facing dashboards and actionable insights.
Conversational AI
AI & ML Solutions
Web Apps
Website Design
Data Analytics and Visualization
Python
Next.js
More +
RELEVANT TALENT
CASE STUDIES
- AI-Powered CTG Fetal MonitoringAnalysing CTG data manually requires significant time and clinical effort, creating potential bottlenecks during labour where timely decisions are critical. The absence of automated support meant healthcare professionals had to dedicate substantial attention to reading and interpreting strips, slowing workflows and diverting focus from direct patient care.
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