RAG + Knowledge Systems
Retrieval-augmented chat over a real corpus, with citations.
RAG over your domain corpus — legal, medical, technical, internal docs. Bilingual and multilingual where it matters. Curator workflows, citation tracking, content-gap reporting.
What you get
4 pillarsDomain-tuned retrieval
Index the corpus the way a domain expert would: by section, amendment, jurisdiction, version — not by raw page chunks.
Citations + provenance
Every answer points back to the source passage, with version and date. Auditors and lawyers actually need this.
Bilingual / multilingual
Same retrieval over English + Bangla (or your language pair). Query in either, answer in either — with cross-language citations.
Curator + content gaps
A second-pass agent flags answers that retrieved nothing useful, queues them for human review, and grows the corpus where it actually leaks.
Tools we reach for
Not exhaustiveWork that maps here
All projects →More in AI Systems Building
Core overview →Personal AI Assistance
A principal-grade assistant across every channel you use.
Business Operations Manager
A multi-agent team that runs a business function 24/7.
AI Software Developer
Agent harnesses that write, review, and ship code.
Messaging Agents
WhatsApp, Telegram, Discord, Slack, iMessage, and web-chat bots.
Agent Orchestration Platform
A fleet of specialised agents, one bridge across your messaging apps.
Real-Time Voice Agents
Live phone and browser-voice agents with streaming and barge-in.
Custom AI Workflows
Document understanding, autonomous loops, extraction, intelligence.
AI Evals + Observability
Test, trace, and keep agents honest in production.
Frequently asked
5 questionsWhat is a RAG knowledge system and when do I need one?
Retrieval-Augmented Generation pairs an LLM with your own document corpus — the model reads the relevant passages at answer time and cites them. Use it when answers must come from your data, not the model's training set.
Can it handle bilingual or non-English content?
Yes — production builds in English + Bangla today, and the same architecture works for any language pair. Retrieval is embedding-based, so it stays accurate across mixed-language queries against mixed-language corpora.
How accurate are the citations?
Every answer is grounded in retrieved passages and emits an explicit source list — section, paragraph, page. The system is tuned to refuse rather than hallucinate when retrieval comes back weak.
What kinds of documents work?
PDFs, Word docs, HTML, Markdown, web pages, transcripts — anything that ingests as text. Image-heavy docs go through OCR first. Best results when the corpus has clear structure (sections, headings, dates).
How do you keep the corpus fresh?
A curator workflow handles new docs and updates: ingest → chunk → embed → review → publish. Automated re-indexing on scheduled intervals or webhook triggers, with version history per document.
Sounds like the bucket you’re in?
Tell me what you’re trying to build. I’ll send a written proposal within 48 hours of our discovery call.