Turn proprietary knowledge, processes and domain expertise into production AI — without requiring sensitive business data to leave a controlled environment.
I work with organizations that have valuable internal data, specialized workflows or regulated operations and want to build AI around the business — not bolt another generic chatbot onto a public model.
The practice combines AI architecture, software engineering, LLMs, data engineering and domain-specific business logic. Delivery is the same path as the telecom work: discovery → architecture → prototype → production → scale. Remote from Delhi, worldwide. NDAs are normal.
Inference inside a customer cloud, VPC, on-premise stack or another controlled environment.
Terminology, workflows, policies and operational data of that company — not a generic assistant.
Reason over business information, call tools/APIs, run workflows and hand off when confidence drops.
Retrieval over documents, databases, knowledge bases, call records and policies with access control.
Model selection, fine-tuning, evaluation, inference optimization and domain ML where an LLM is the wrong tool.
Serving, pipelines, vector stores, observability, security and the deploy path that operations can own.
CRM, ERP, PBX/CCaaS, healthcare and finance systems, SaaS APIs and internal apps.
Open-source and commercial models — OpenAI, Anthropic, Gemini, OpenRouter — chosen per job, not per fashion.
ML / application: PyTorch, TensorFlow, Hugging Face, LangChain, embeddings, structured outputs, tool calling. Serving and data: vector search, evaluation harnesses, Linux/Docker, AWS, GCP and Azure when the customer already lives there.
Healthcare, pharma, insurance, financial services, government, telecom/VoIP, legal, manufacturing, product companies, and SMBs whose advantage is internal process — not a public knowledge cutoff.
Sensitive information should not automatically go to a third-party AI service. The first reply is whether the job is a retrieval problem, an agent problem, a voice problem, or a process that should stay a spreadsheet for another quarter.
Send the current architecture, call volume, carriers and the workflow you need to automate or productize. Discovery can start from a broken PBX or a blank product brief.
Discuss Your Project hello@unifiedpbx.inA practical technical discussion focused on SIP, media, tenants, AI and the delivery path — not a generic sales deck.