AI Company Brain for a Media Agency
A media marketing agency of around 20 people that builds proposals and media plans for its clients. The commercial knowledge (what was proposed to each client, media-mix criteria, inventory and rates) lived scattered across Drive folders and in the team's heads.
We had already built their proposal and media plan generator. The next step was giving the system memory: a central knowledge base where everything the company knows lives, queryable in natural language.
Commercial knowledge lived in Drive folders and in each salesperson's head, with no single source
No way to query the proposal history: what was proposed to each client and with which media mix
Every new proposal started from scratch instead of building on previous ones
Rates and media inventory changed with no versioning: outdated and current documents coexisted with no distinction
A traditional search engine is not enough: it matches exact words, it does not understand the question
Central company brain
A knowledge base where everything the company knows lives: approved proposals, media inventory and internal documents, all indexed and versioned (what is current and what was superseded).
Automatic ingestion
Every proposal approved in the generator is added to the base on its own. The system learns from the team's daily work, with no manual loading.
Hybrid search
Combines semantic search (by meaning) with keyword search, to find the right answer even when the question does not use the document's exact terms.
Answers that cite their source
You query in natural language and every answer cites the document it comes from. If the information is not in the base, the system says it does not have it instead of making things up.
Connected to the proposal generator
The base feeds the generator with the best previous examples, and the generator feeds the base with every new proposal. A loop that improves with use.
Phase 1
Data model: documents, versions and query log
Phase 2
Ingestion pipeline: chunking by document type + embeddings
Phase 3
Query engine: hybrid search, cited answers and an anti-hallucination guard
Phase 4
Integration with the sales team's proposal generator
single source for everything the company knows
semantic + keyword search on every query
of answers with the source document cited
made-up answers: if it is not in the base, it says so
every approved proposal feeds the base on its own
the system and the data belong to the company from day one
How we build a company brain
The full methodology behind this case is documented on the blog:
Does your company's knowledge live in folders and in people's heads?
We build you an AI company brain: a knowledge base your team queries in natural language and that learns from daily work.
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