AI Solutions
Answers from your documents,with the source attached.
Retrieval-augmented generation that respects permissions, cites its sources and admits when it doesn't know.
Sound familiar?
- “Your knowledge is spread across thousands of documents nobody can search.”
- “New staff take months to find answers experienced people know instantly.”
- “You tried a document chatbot and it confidently cited things that don't exist.”
Capabilities
What it does.
Cited answers
Every response links to the source passage, so it can be verified in a click.
Permission-aware
Retrieval respects who is allowed to see which document.
Handles messy sources
PDFs, scans, spreadsheets, email and wiki pages ingested and chunked sensibly.
Says 'I don't know'
Answers only when retrieval is confident, rather than filling the gap.
Stays current
Incremental re-indexing as documents change.
Measured quality
Retrieval and answer accuracy scored on your real questions.
How we deploy it
The path from pilot to production.
Most AI projects die between a good demo and a working deployment. This is the sequence that avoids that.
Audit the corpus
Contradictory and outdated documents produce contradictory answers. Cleaning beats clever retrieval.
Chunk deliberately
Chunking strategy affects quality more than model choice, and is where most implementations go wrong.
Hybrid retrieval
Semantic plus keyword search — pure vector search misses exact terms like part numbers.
Evaluate
Retrieval accuracy measured separately from answer quality, because they fail differently.
Ship with citations
Users verify rather than trust. That's what makes it usable for real decisions.
Data & privacy
Where your data goes, plainly.
This is the first thing enterprise procurement asks about AI, and most vendor pages avoid answering it.
- Documents and embeddings stored in your own infrastructure or Indian regions.
- Permission filtering applied at retrieval, so users never see restricted content.
- Enterprise API tiers exclude your content from model training.
- Fully on-premise deployment available with open-weight models.
What it costs to run
Embedding a corpus is a small one-time cost — typically a few thousand rupees for tens of thousands of pages. Query cost is a few rupees per question, and re-indexing is incremental.
Technology
What we build it with.
- Anthropic Claude
- OpenAI
- pgvector
- Pinecone
- LlamaIndex
- LangChain
- Python
- Elasticsearch
We're not tied to a model vendor. Systems are built so a provider can be swapped as capability improves or prices move.
Industries
Where it applies.
Almost always retrieval, not the model. If the right passage isn't retrieved, the model answers from memory and sounds confident doing it. We measure retrieval accuracy separately for exactly this reason.
Yes, with OCR — though quality depends heavily on the scan. Poor scans produce poor extraction, and we'll tell you upfront if your corpus needs re-scanning rather than pretending otherwise.
Yes. Permission filtering is applied at retrieval time, so a user's answer can only be built from documents they're entitled to see. Retrofitting this later is very difficult, so it's designed in.
Incremental re-indexing as documents change, triggered by your storage system or on a schedule. Stale answers erode trust faster than occasional gaps.
Start here
Tell us the process, not the technology.
The best AI projects start from an expensive manual task, not from a decision to use AI. Describe the task and we'll tell you honestly whether this is the right tool.