Our expertise, embodied in Voiager
This voice agent automates interviews, captures data, and manages voice interactions at scale.
Generative AI moves fast, your case should too. Run your project through our ROI Calculator and see exactly where the return is.
Unlock industry-specific opportunities through Generative AI consulting. Oxagile’s experts work closely with your team to identify where AI can make the biggest difference, and guide implementation from pilot to production.
We identify workflows where the output itself is the value – content generation, document synthesis, structured data extraction from unstructured sources, and conversational interfaces. This is distinct from broader AI opportunity mapping: we’re looking specifically for where a generative model produces something a human or traditional system currently produces manually.
We design the prompt structures, context layers, and retrieval pipelines that determine whether a generative model performs reliably on your domain – or produces plausible-sounding output that fails in production. This is the engineering work that sits between “the model exists” and “the model works for your use case.”
We run structured evaluations across foundation models — GPT-4o, Claude, Gemini, Mistral, and domain-specific alternatives — against your actual data and output quality requirements. Selection is driven by accuracy on your inputs, latency, cost per query, and data privacy constraints, not benchmark scores on generic datasets.
Generative models hallucinate. We design evaluation harnesses that test output quality, factual grounding, and consistency at scale — before deployment and continuously after it. This is the step most GenAI pilots skip, and the reason most of them don’t survive contact with real users.
We design and implement retrieval-augmented generation pipelines that connect generative models to your enterprise knowledge — documents, databases, APIs — so outputs are grounded in your data, not the model’s training set. Covers chunking strategy, embedding selection, retrieval tuning, and source attribution.
Learn about our team’s expertise and how they turn GenAI knowledge into effective solutions.
Alexey Karankevich
AI Innovation Lead
Specializes in mobile product development, data mining, and ML.
Yariv Z. Levy, PhD
AI Strategy Advisor
Leads AI adoption and innovation across industries, PhD in AI, MSc (EPFL).
Identify high-impact opportunities with our generative AI consultants before committing resources. Prioritize initiatives, quantify potential outcomes, and create a clear roadmap to develop AI solutions strategically.
Develop bespoke GenAI systems for challenges that off-the-shelf solutions can’t solve. Work with our engineers to prototype, pilot, and refine AI solutions using experimental methods, scientific insight, and engineering.
Rapidly pilot production-ready AI agents on a single workflow. Test autonomous capabilities hands-on, evaluate integration potential, and see how AI can streamline operations from day one.
This voice agent automates interviews, captures data, and manages voice interactions at scale.
With 20+ years in online video, leverage Generative AI to automate tagging, generate highlights, personalize recommendations, and improve moderation.
Use Generative AI to power product descriptions, personalized shopping experiences, and customer support automation.
Enhance financial workflows with GenAI for automated reporting, document processing, fraud analysis, and client communication.
Transform EHR and device data into structured, actionable outputs with strict data security standards.
Streamline hiring and employee experience with GenAI, including screening, interview assistance, and internal copilots.
Improve resolution times, enhance personalization, and allow teams to focus on complex, high-value cases.
LLMs Pre-trained models are adapted to your specific business tasks, domains, and workflows, improving relevance, accuracy, and output quality for practical applications.
RAG AI model reliability is enhanced by connecting LLMs to external knowledge sources, providing responses are factually accurate and contextually relevant.
Private cloud Secure, scalable, and cost-effective GenAI deployments are delivered on private cloud environments using open-source frameworks, keeping your data protected and performance optimized.
Data chunking Data is structured and segmented effectively to improve model comprehension, speed, and consistency, providing more efficient processing of large and complex datasets.
Graphs Knowledge graphs are used to enrich LLMs with structured domain knowledge, boosting reasoning, contextual understanding, and factual accuracy for critical business applications.
Generative AI refers to technologies that can create content, insights, or predictions from data, like text, images, or code. Applied strategically, generative AI consulting services can streamline workflows, automate repetitive tasks, and unlock new ways to deliver products and services.
Consultants are most valuable when you want to identify high-impact AI opportunities, design practical workflows, or implement pilot projects. Our generative AI consultants help map your business processes to AI solutions, prioritize initiatives, and guide deployment for measurable results.
We conduct a thorough assessment of your workflows, data readiness, and operational priorities. This includes identifying repetitive tasks, data-rich processes, and areas where AI can provide tangible efficiency or insight gains.
We leverage a wide range of generative AI technologies, including large language models (LLMs), computer vision, RAG (Retrieval-Augmented Generation), and knowledge graph integrations. We select the right tools based on your business objectives, scalability needs, and infrastructure.
Yes. We make sure seamless integration with your current tech stack and workflows, whether cloud-based or on-premises, so AI solutions enhance rather than disrupt operations.
Success is measured by clear, pre-defined KPIs, such as time saved, process automation rates, improved accuracy, or business outcomes. Pilot programs and proofs of concept help validate effectiveness before scaling enterprise-wide.
Data security is a top priority. We recommend private cloud deployment, secure access controls, and best practices in data handling to keep sensitive information protected throughout development and deployment.
