Ground the answers
The retrieval layer that grounds your AI in your own data
An AI is only as good as the data it can reach. We build the pipeline that connects your models to your private knowledge, so every answer is grounded in fact instead of a guess, and cites the source it came from.
Free 30-min call · You leave with the right RAG architecture for your data
Teams we ship for
logos à fournir : clients citables
The new AI software engineering
Coding changed. Engineers stopped being the bottleneck.
The bottleneck moved to orchestration. Claude Code is the brain, MCP plugs into your stack, and one senior engineer reviews every line the agents ship. See how we build, automate and ship on every engagement.
- 01Claude Codethe brain · subagents · MCP
- 02Anthropicmanaged agents · runtime
- 03CursorIDE pair-programming
- 04n8ncron · webhooks · glue
- 05Supabasedata · memory · retrieval
The foundation of reliable AI is data, not prompts.
Better prompting cannot compensate for missing knowledge. Reliability comes from the data layer underneath the model, so that is where we start.
Most AI failures are context failures
The model is rarely the weak part: it simply does not have the right information in front of it when it answers. Without a properly built data layer, every response is a guess wearing confident prose, and nobody notices until it costs something.
Chunk, index, wire
Our fix is context engineering: one senior engineer directs a fleet of AI agents that chunk your documents, index them by meaning and wire the result into a retrieval layer your model interrogates on every request. Scattered files become an asset your AI can trust.
A powerful model is useless without good context.
Without a structured pipeline behind it, your AI hallucinates, and an automated process that acts on a hallucination is a business decision built on fiction.
Trained on the internet, not on you
Public models never saw your SOPs, your pricing logic or your client history. Asked about them, they improvise, and in a B2B environment a confident improvisation is a liability you end up paying for.
A hallucination is a bad decision
When the model invents a clause or a number inside an automated workflow, everything downstream executes on the invention. Grounded retrieval keeps each step tied to your verified knowledge base.
Slow retrieval fails twice
Unstructured data means slow lookups and misread documents. If the system needs thirty seconds to find a file and then misinterprets it, the automation failed on speed and on truth. You need both, instantly.
Custom RAG development for precision retrieval.
We build the bridge between your private data and your model, so every response is grounded in your own verified knowledge base instead of the model's training data.
Keyword search misses the answer whenever the document does not use the same words as the question. We turn your text, PDFs and databases into vectors so the retrieval layer works on meaning: someone asking about a late payment finds the clause written as an overdue balance.
Three steps that make retrieval a precision instrument
We follow the same three-step process on every engagement, so your pipeline returns accurate results from day one.
Intelligent chunking
We never cut text at arbitrary lengths. The agents analyse the semantic structure of each document, headings, tables, clause boundaries, so meaning survives the split into fragments and no answer is stitched from half a thought.
Hybrid search orchestration
Keyword search combined with vector embeddings in a single query path, so the system catches exact technical jargon, part numbers and clause references, as well as questions asked in plain natural language.
Evaluation before launch
Continuous testing loops score every answer for faithfulness and relevance, the RAGAS metrics, so hallucinations are caught in the harness before your team ever sees one, and quality keeps being measured after launch.
Precision retrieval, engineered end to end
The bridge between your private data and your model, built and kept fresh: every response draws on your verified knowledge base instead of whatever the model remembers from training.
Semantic search and embeddings. Your text, PDFs and database rows transformed into vectors that carry the meaning behind a query, so the system matches intent, not just keyword overlap.
Real-time synchronisation. Sync pipelines update the vector index the moment a document changes in Notion, Drive or your database. The AI never answers from a stale copy.
Source attribution. Every answer cites the exact passage it was built from, so your team verifies any claim in one click instead of taking the model on faith.
Vector store that scales. Supabase in your accounts by default, Pinecone or Weaviate when volume demands a dedicated store, tuned for sub-second latency as the corpus grows.
Access control. Retrieval respects who is asking: private sources stay scoped to the people and agents cleared to see them, enforced at query time, not by convention.
Guardrails and evaluation. Continuous tests score answers for faithfulness and relevance, so retrieval quality is a number you watch on a dashboard, not a feeling you argue about.
The stack behind every pipeline we ship
We do not reinvent the stack for every project. Every engagement runs on the same proven set of tools, swapped only where your systems require it.
Claude Code
The reasoning model behind agent decisions and tool calls
Anthropic
Managed agent runtime for long-running, autonomous tasks
MCP
The protocol that connects agents to your APIs and tools
Cursor
IDE pair-programming where the engineer reviews every diff
n8n
Workflow orchestration for cron jobs, webhooks and handoffs
Supabase
The data layer for agent memory, logs and private retrieval
Vercel
Deployment and hosting for agent-facing apps and dashboards
GitHub
Version control and review for every agent we ship
Everything runs in your accounts and your repositories. Self-hosted where data control matters. [Engagements précis à valider]
Get your questions answered
Get answers to common questions about our automation process, pricing, and results.
Internal documents, Notion, Google Drive, databases, support tickets, knowledge bases, PDFs and structured business data.
See what your team
can hand to AI
A free 30-minute call. We map one workflow together and you leave with a plan you can use, with or without us. Meeting us at the AI Summit Barcelona on September 22? Grab your slot here.
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