Answer search
Help people find a relevant answer and open the documents that support it.
[ services — rag & llm integration ]
RAG systems that answer from your data.
Make company knowledge easier to search and use. Connect documents and business systems to an AI assistant with source references, access controls and measurable answer-quality checks.
2014
founded
AI
delivery focus
2–4 wks
MVP launch
( what we build )
From answer engines to AI copilots.
Start with the questions people need to answer and the sources they are permitted to use.
RAG chatbots
Support and internal assistants that answer from current knowledge.
Search pipelines
Prepare documents, PDFs and database content so relevant passages can be found reliably.
LLM API integration
Connect a language model to your application with prompts, response validation and cost controls.
( engineering for grounded AI )
Check the sources, answers and operating costs.
Retrieval quality
Test document preparation and search ranking against representative questions and expected sources.
Answer quality
Check whether answers use the retrieved evidence and whether citations support the claims.
Access controls
Limit retrieval to permitted sources and test that users cannot retrieve another team's private data.
Ongoing operation
Plan source updates, caching, cost limits and monitoring as usage grows.
( comparison )
Choose the integration your task needs
Scroll across to see all columns.
| Need | Recommended approach | Why it works |
|---|---|---|
| Questions about company knowledge | RAG with access-controlled retrieval | Supplies relevant business context for the answer |
| Tasks using information already in the request | Direct model API integration | Avoids a separate document-search pipeline |
| Finding and checking supporting material | Search with answers and source links | Lets users inspect the evidence behind a response |
( faq )
Common questions
What is retrieval-augmented generation (RAG)?
RAG retrieves relevant information from selected sources and supplies it to a large language model with the question. This gives the model useful context for its answer, but the retrieval and response still need to be evaluated.
What can I use RAG for?
RAG powers answer engines over product docs, support wikis, policies, contracts, and internal knowledge. It's ideal for chatbots, search that returns cited answers, copilots, and tools that need grounded, up-to-date responses.
How do you check answers and citations?
We build a set of representative questions, measure whether retrieval finds the right material, and check whether answers and citations are supported by it. We also test missing, conflicting and outdated information.
Which vector databases and frameworks do you use?
We build with pgvector, Pinecone, Qdrant, and Weaviate and orchestrate with LangChain, LlamaIndex, or direct provider SDKs, choosing the stack that fits your data size and cost.
Can you integrate an LLM by API without RAG?
Yes, for simpler cases. We also build plain LLM API integrations with prompting, guardrails, and structured output when retrieval isn't needed.
How long does a RAG or LLM integration take?
A retrieval-grounded MVP over one source typically ships in 2-4 weeks. Production RAG with multi-source ingestion, eval, and guardrails runs 4-10 weeks.