AI search that finds the right passage, then answers from it

Not necessarily. PostgreSQL with the pgvector extension handles many knowledge bases comfortably and keeps your stack simple. OpenSearch or Elasticsearch make sense when you need strong keyword search, facets and vectors together at larger scale. Dedicated vector databases such as Qdrant suit very large, vector-heavy workloads. We choose on document volume, query load, filtering needs and what your team can operate.

Yes, with multilingual embedding models, which place questions and passages with similar meaning close together across languages. Quality depends on the language and how technical the vocabulary is, so we test with real questions from your users, including Hinglish in Roman script. Where needed, we translate the query as an extra retrieval step, and the answer can be written back in the user’s language with citations to the originals.

Connectors sync on a schedule or through webhooks, re-index only changed files and remove deleted ones. Each document carries an effective date and version, so superseded circulars or SOPs are excluded or ranked lower, and answers show the date of the source they cite. For fast-changing facts such as prices or stock, we query the live system instead of relying on indexed documents.

A chatbot is a conversational front end, usually customer-facing, handling dialogue, hand-offs and actions. A knowledge system is the retrieval layer underneath: indexing, permissions, ranking and cited answers. Internal teams often use it directly as a search tool. Many chatbots sit on top of one, and getting retrieval right first makes any later chatbot, assistant or workflow more reliable.