AI assistants
Assistants grounded in your documentation, catalogue or ticket history, with citations.
AI Products
Start with the business problem, not the AI buzzword.
AI engineering that begins with a workflow worth improving, then chooses the model, retrieval strategy and guardrails that fit it.
LLMsRAGAI APIsPythonLangChain
The problem we solve
A model call is easy. The hard parts are grounding answers in your data, keeping private information private, measuring whether the output is actually correct, and knowing when a rules-based solution would have been better.
What we build
RAG and knowledge systems
Retrieval pipelines over private content, with chunking, embeddings and evaluation.
AI automation
Document extraction, classification, summarisation and routing inside existing workflows.
AI product features
Search, generation and recommendation features built into your application, not bolted on.
How we work
- 1
Define the task, the acceptable answer and how it will be measured.
- 2
Choose the smallest architecture that solves it, model APIs included.
- 3
Build retrieval and guardrails, then evaluate on real examples.
- 4
Ship with logging, cost controls and a human fallback path.
Our own site assistant is grounded strictly in published Yashotantra content and escalates to email when it cannot answer.
Questions clients ask
Where does our data go?
We design for the privacy posture you need: provider selection, data-retention settings, redaction, and self-hosted or regional options where required.
Which models do you use?
Whatever fits the task and budget — hosted APIs from major providers, or open-weight models where privacy or cost demands it.
Can AI be added to an existing product?
Usually yes. We start with a discovery engagement to confirm the use case is worth building.
Have a project in mind?
Tell us what you are building and we will reply with a clear next step.