AI
AI that can be audited.
Most AI projects stall at the same question: how do we know it’s right? We build AI into workflows with the controls that answer it — every output logged, every sensitive recommendation reviewed before a person sees it.
Two practices, one principle: legible intelligence.
What we build
AI in real workflows.
Examples from our products and delivered engagements.
Receptionist & call intelligence
Calls transcribed and summarised into the patient record, with an AI agent handling appointment scheduling — inside a HIPAA boundary.
Healthcare communications →Multi-agent financial planning
Specialised agents analyse and recommend; a mandatory compliance gate reviews every recommendation before a customer sees it.
AI financial planning →Delivery governance
Automated auditing blocks structural and security issues from merging; AI validation catches ambiguous requirements before planning.
AI delivery →Assistants & retrieval
Assistants that answer from your own documents and data, with sources shown and human review where the answer matters.
Retrieval · pgvectorTranscription & summarisation
Speech and long documents turned into structured, searchable records.
NLP · summarisationExplainable analytics
Reporting and data pipelines where every number can be traced to where it came from.
Power BI · PythonClaude & Azure OpenAI · LangGraph · Python · pgvector · SonarQube · Power BI
Connect & consult
Talk to the person who builds it.
Planning to bring AI into a regulated workflow, or into how your team ships software? Start with a conversation with Prithipal Thakur, who leads Pramilia’s AI governance and AI delivery work. No sales layer in between.
- Where AI fits in your workflow — and where it shouldn’t
- The review and audit controls your context requires
- How to bring AI into delivery without losing quality signals
- A straight read on approach, effort and risk