Technology · AI data
Vector Databases
Atlas Vector Search · pgvector · Pinecone · Qdrant
A vector database stores text as embeddings — numeric fingerprints of meaning — so a search for "can I get a refund" finds a passage about returns policy that shares none of those words. It is the retrieval half of every RAG system, and the half that quietly decides whether the answers are any good. A weak retriever cannot be rescued by a stronger model.
How TrivialWorks uses it.
We start with the store you already have. MongoDB Atlas has vector search and Postgres has pgvector, so most retrieval features ship without a second data platform on the invoice. A dedicated store earns its place at genuine scale or throughput, not by default. Whichever it is, we tune chunking and embeddings against a set of real questions with known answers, and measure retrieval before anyone judges the model.
Decision guide
Should your project use Vector Databases?
Practical selection guidance — the conversation we would have with you before writing a line of code.
When it’s the right choice
- Retrieval over your own documents — policies, contracts, tickets, manuals — where keyword search keeps missing the point
- Support deflection and internal copilots that must cite a source rather than improvise
- Semantic search and recommendations where meaning matters more than exact terms
- Any RAG feature where answer quality has to be measurable rather than anecdotal
When it isn’t
- Exact-match lookups — order numbers, SKUs, dates. A WHERE clause is faster, cheaper and always right
- Small corpora of a few hundred documents, where full-text search does the job with none of the pipeline
- Adopting a dedicated vector platform before checking whether pgvector or Atlas already covers you — that is an invoice, not an architecture
Consider PostgreSQL with pgvector — Your data is already relational — keep retrieval in one database until scale genuinely forces it out.
Consider MongoDB Atlas Vector Search — The source documents already live in MongoDB; embeddings can sit beside them.
Best use cases
Where Vector Databases makes practical sense.
RAG over private documents
Grounded answers with citations, so every response can be traced back to the passage it came from.
Support deflection
Retrieval across historic tickets and help content, answering the repeat questions and escalating the rest.
Semantic product & content search
Search that understands intent, so a query nobody anticipated still returns the right result.
Deduplication & clustering
Finding near-identical records, claims or listings that exact matching leaves scattered.
Proof
Where we’ve shipped it.
Conversational AI engineering: agents that cannot afford to guess
Offshore engineering team inside a US conversational-AI company — RAG-grounded agents working under HIPAA, SOC 2, GDPR and CCPA.
Read the case studyAgentic platform for cross-border marketplace selling
Agents that scout Indian brands missing from foreign marketplaces, then automate their path onto Amazon UAE, US and UK — onboarding to ad campaigns. In build now.
Read the case studyTechnology pairings
Commonly paired with Vector Databases.
Related services
Services that commonly use it.
Questions
Vector Databases, asked straight.
Do we need a dedicated vector database?
Usually not at first. If your data is in MongoDB, Atlas Vector Search covers it; if it is in Postgres, pgvector does. A dedicated store earns its place at real scale or throughput. Starting with one you already run keeps the moving parts — and the bill — down.
Why do RAG answers come back wrong or irrelevant?
Almost always retrieval, not the model. Chunks too large or too small, embeddings that do not suit the domain, or no reranking — the model can only answer from what it was handed. That is why we evaluate retrieval against known-answer questions before tuning any prompt.
How do you keep embeddings current as documents change?
Re-embedding runs as part of the ingestion pipeline, triggered on change rather than on a hopeful schedule, with versioning so a bad batch can be rolled back without taking search down.
Thinking about Vector Databases?
Send the requirement and you get back a functional specification — screens, data model, stack and an estimate — at no charge. If Vector Databases is the wrong choice for it, that will be in there too.