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.
For the broader AI/data family (RAG pipelines, GraphRAG, events, multi-model routing and observability), see AI, Data & Automation Engineering.
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.
Direct answers on scope, stack, rescue vs build, and how to start, structured for search and AI summaries.
Direct answers on scope, stack, rescue vs build, and how to start, structured for search and AI summaries.
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.