We work with commercial language models from OpenAI, Anthropic, Google, and Mistral, selected based on your performance, deployment, and cost requirements. We also deploy open-source models via Hugging Face when flexibility, self-hosting, or greater control over the model layer is needed. For clients with stricter security, compliance, or infrastructure requirements, we offer local/private deployments with models like Llama or Mixtral.

For voice agents, we use specialized models such as Parakeet for streamed speech-to-text and Kokoro for text-to-speech in real-time conversational interfaces. These enable low-latency, natural voice interactions for phone-based agents and conversational assistants.

Yes. We offer local and private deployments using models like Llama or Mixtral for clients with stricter security, compliance, or infrastructure requirements. We can deploy via Docker, Kubernetes, and Terraform for repeatable infrastructure provisioning and controlled enterprise rollouts. Self-hosted solutions are available for clients that require greater control over deployment and operations.

Our core app stack uses TypeScript, Node.js, and Next.js for production applications, APIs, and client-facing AI experiences. We use Python where it is the best fit for model pipelines, data processing, and AI-specific backend logic. Backend services are built in Node.js, Next.js, and Python FastAPI depending on the product, integration, and model-serving requirements.

Yes. We use n8n for workflow automation, orchestration, integrations, approvals, and operational AI workflows without heavy custom build overhead. This allows us to build and iterate quickly while maintaining flexibility for complex use cases.

RAG (Retrieval-Augmented Generation) is a technique for grounding AI model outputs in your documents, internal knowledge, and connected business data. We implement RAG pipelines using vector databases such as OpenSearch, Pinecone, Qdrant, and pgvector, chosen based on scale, hosting preferences, and retrieval needs. We also build document ingestion and chunking pipelines for preparing unstructured content for retrieval, search, and downstream AI workflows.

We build connectors to Slack, Microsoft Teams, SharePoint, Google Drive, CRMs, Outlook, ERP systems, and other common tools so AI systems can work within your existing environment. Our API-based integration approach means we can connect to virtually any system with an API.

We deploy across Azure, GCP, AWS, Hetzner, IONOS, Vercel, Scalingo, and Heroku depending on your preferences, geography, and compliance constraints. For EU-based clients requiring data sovereignty, we can deploy on European cloud infrastructure or fully on-premise.

We implement comprehensive governance layers including logging and observability via n8n for workflow logs and Microsoft AI tooling for app/model telemetry. We use Langfuse for deeper LLM tracing when needed. For security and compliance, we implement guardrails and moderation using n8n workflow checks plus Microsoft AI safety/moderation tooling for filtering, risk checks, and policy enforcement. We handle access control, SSO, and GDPR compliance in controlled client environments.

Yes. We build approval steps into n8n workflows with Microsoft tools for review surfaces. Human-in-the-loop review ensures that AI outputs can be verified by humans before taking action, which is essential for high-stakes decisions and maintaining quality control.

For rapid deployment, we use Open WebUI and the Azure sample chat repository to deliver usable AI interfaces quickly. For custom solutions, we build web apps in React and Next.js for AI products, internal tools, and client-facing interfaces. We create internal chat interfaces for employee copilots, knowledge assistants, and workflow-driven AI applications. We also build Slack and Teams bots for embedding AI into existing communication workflows, voice interfaces for conversational assistants, and custom UI widgets that can be embedded in third-party webpages.

We use LiveKit as the streaming server layer for voice and video agents that require low-latency, real-time interaction. Combined with speech-to-text models like Parakeet and text-to-speech models like Kokoro, we build voice agents that can handle phone calls, answer customer inquiries, and provide conversational assistance with natural, human-like interaction.

Yes. We prioritize EU-compliant infrastructure and data sovereignty. We can deploy on European cloud providers, implement GDPR-compliant data handling, and use open-source or self-hosted models to ensure your data stays within your control. Our governance layers include compliance monitoring, access control, and audit logging.

We use Microsoft AI tooling for evaluations, with Langfuse for prompt/version comparisons and output quality analysis. Our monitoring stack includes n8n for workflow logs, Microsoft AI tooling for app/model telemetry, and Langfuse for deeper LLM tracing when we need visibility into prompts and model behavior.