Solutions / RAG and semantic search
Search your documents by meaning, and get answers that show the passage they come from.
These scenarios are illustrations. None of them describes a client.
Banking and finance
What we would build
An assistant that answers advisors' questions from the bank's own research and product documents, quoting the exact paragraph each answer comes from. It only searches documents the advisor is already allowed to open, and it runs on the bank's servers.
FAISS, vLLM, Llama, Keycloak
Banking and finance
What we would build
An agent that gathers the client file from each system, checks expiry dates and missing items, and drafts the review summary with its sources. The analyst signs off on the complete file, and the agent has no permission to contact clients or approve anything.
LangGraph, Python, PostgreSQL, Avaloq API
Watchmaking and luxury
What we would build
A briefing assistant that reads the client's purchase history, service records and advisor notes, then writes a one-page brief in the advisor's language with a source for every line. It runs on infrastructure the house controls and stores nothing of its own.
Mistral, vLLM, Salesforce API
Watchmaking and luxury
What we would build
An intake assistant that reads the photos and the client's description, finds similar past repairs and drafts a preliminary quote with the likely operations and parts. A watchmaker validates or corrects the draft, and each corrected quote joins the examples used for the next ones.
PyTorch, FAISS, FastAPI, Salesforce API
Pharma and life sciences
What we would build
A search assistant over your submissions, SOPs and Swissmedic, EMA and ICH guidelines that answers in a few sentences and links each claim to the exact section. It is read-only, and it declines to answer when the documents do not support one.
FAISS, bge-reranker-v2-m3, vLLM, Veeva Vault
Pharma and life sciences
What we would build
A literature agent that queries PubMed every week for each team's topics, screens abstracts against their criteria and sends a short digest with links and the reason each paper was kept. Scientists rate the picks, which tunes the next week's screening.
PubMed API, BGE-M3, LangGraph, Microsoft Teams
Retail and e-commerce
What we would build
A hybrid search that combines keyword matching with multilingual semantic search over the catalogue, so a query finds the right products whatever the language or wording. It runs next to the current engine and is tuned on real search logs.
OpenSearch, BGE-M3, Python
Retail and e-commerce
What we would build
A service agent that reads each ticket, looks up the order and the return policy, and drafts the reply in the customer's language. Routine answers go out after a quick check; refunds and exceptions wait for a person, with the case already summarised.
LangGraph, Zendesk API, FAISS
Retrieval-augmented generation finds the passages in your documents that relate to a question, then has a language model answer from those passages only, citing them. Semantic search is the first half on its own: finding passages by meaning, across languages and wording. Both depend on how documents are split, indexed and ranked, which is where most of the work goes.
Further reading
Inside FAISS: Billion-Scale Similarity Search →45 min readIt is instructed to answer from retrieved passages and shows them, and a check flags claims they do not support. If retrieval finds nothing relevant, it says so. We measure faithfulness on real questions from your team.
Yes. Retrieval filters by the access rights the user already has, so nobody sees a passage they could not open in the source system.
FAISS when the index fits in memory and filters are simple, pgvector or OpenSearch when permissions and metadata filters must live in the same store. Our FAISS deep dive explains how its indexes trade memory for speed.
Yes, with multilingual embeddings. A question in French can find a passage written in German, and the answer comes back in the language of the question.
We start with a feasibility study on your own data, so you see results before anything is promised.