Case studyEnterprise · AI platform

Fifteen years of project knowledge, answerable

ATÖLYE Intelligence — a multi-phase business intelligence platform that lets a data-heavy organisation search, reason over and report on its own work.

Business intelligence platform
Internal platform — access controlledPhase 3 in progress
Client
ATÖLYE — strategic design and innovation
Our role
Product design, engineering and ongoing development
Shape
Multi-phase — knowledge, then search, then reporting
Stack
Next.js · Claude API · Pinecone
01
Knowledge base. Scattered project documents unified into one indexed corpus.
02
Semantic search. Ask in plain language; get passages with their sources attached.
03
Reporting. Recurring questions become saved views instead of new requests.
01 — The brief

The answer already existed — in a folder no one could find

A studio that has run hundreds of projects accumulates proposals, research, decks and post-mortems. The knowledge is real; the retrieval is the problem. The brief was to make the archive usable by the people doing the next project — without asking them to learn a new system.

01 Answers with citations — never an unsourced claim
02 Permissions that mirror the organisation, not the file system
03 Delivered in phases, each one useful on its own
04 Costs that stay predictable as usage grows
02 — Under the hood

Retrieval first, generation second

Documents are chunked, embedded and indexed in a vector store. The model only ever answers from what retrieval returns.

03 — Trust

An internal tool is only used if it can be checked

Every answer carries the passages it came from and a link to the source document. When retrieval finds nothing, the platform says so instead of improvising. That single rule is what moved the tool from a demo to something people open on a Monday morning.

Next.js Claude API Pinecone Vector search
Question
“What did we propose the last time a client asked for a maturity model?”
Answer
Three passages, three documents, each one openable.
Team reviewing a dashboard
04 — Delivered in phases

Each phase shipped something the team could use, rather than a step toward something they couldn't yet see.

Knowledge base → semantic search → reporting
05 — How it ran

Define to Operate, with the same team throughout

01 Define Interviews to find the questions people actually ask, then a phased roadmap.
02 Design Interface built around the citation, not the chat bubble.
03 Build Ingestion pipeline, vector index and application, phase by phase.
04 Launch Rolled out to a pilot group first, then widened as retrieval quality held.
05 Operate Continued development, new sources and cost monitoring as usage grows.

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