Augmented BI & Data Copilot
Query your data in plain language and get an answer that cites its sources, instead of waiting for a report that lands after the decision.
A dashboard explains the past, it decides nothing.
Queries stay in the hands of a few experts, the business waits for its report, sometimes for days, and analysis looks backwards once the gap has already happened.
Augmented BI moves the line: the business asks in plain language, the answer arrives with its sources, and AI explains the gap instead of merely displaying it. What makes it usable is not the conversation, it is that every figure can be traced back.
What we do
Four capabilities to move from the monthly report to the question asked right now.
Data Copilot
An assistant wired into your reference data, answering from your data rather than from the model's memory, with the sources attached to every answer.
RAG on your own reference data, hosted to match your residency constraint
- A question in plain language
- Tables cited, freshness shown
- A confidence score on the answer
Semantic model
Business definitions written once and shared: an indicator means the same thing from one directorate to the next, however it is queried.
one owner per indicator, one dated version per definition
- Formula, grain and scope
- Declared synonyms
- What is not defined is not answered
Generative analytics
The AI does not just display the gap, it explains it and produces the chart and the narrative that go with it, in real time.
on the indicators of the semantic model, not on free-form extracts
- The gap broken down by cause
- Chart and narrative generated
- Compared to the reference period
Governed access
Who can ask what is defined at design time. Row Level Security and audit logs cover every access, including those made through the assistant.
the assistant holds no rights of its own, it inherits the directory's
- Row Level Security per profile
- Sensitive columns masked
- Question and query logged
What actually changes
This is not a few more dashboards, it is a change of posture: data becomes conversational and predictive, not only retrospective.
SQL queries reserved for a handful of experts. The business waits for its report, sometimes for days.
The business queries data in plain language. Answers come sourced and auditable.
Frozen dashboards, monthly reporting. You look at the past, once the gaps are already booked.
The AI explains the gap and produces the chart and narrative that go with it, as it happens.
Statistical forecasting in a spreadsheet, slow to react to weak signals and to breaks.
AI forecasting and anomaly detection: anticipate demand, breaks and drift.
Occasional manual checks. No golden record, quality not measured continuously.
MDM and quality measured on every run, with remediation attached to the flow.
What you get
The shared semantic model
Business definitions written once, with their owner, formula, grain and synonyms. This is the file the BI and the assistant both read, and what is not in it will not be answered.
Access, exactly as it already exists
The assistant inherits the directory's rights rather than creating its own. Row Level Security per profile, sensitive columns masked, and a log of every question with the query it produced.
The answer and its sources
Every answer arrives with the tables queried, their freshness, the version of the definition used and a confidence score. Below the threshold, the question goes to the indicator's owner.
The evaluation before opening
A reference set of questions replayed before every go-live: grounding, indicator resolution, rights, latency. An ambiguous indicator blocks, it is never guessed.
How we deliver
Discover
depending on the number of sources, entities and the level of compliance required
- Quality and reference data audit
- Value / feasibility matrix
- Prioritised data roadmap
MVP
depending on the use case retained and the systems to connect
- Data foundation and data copilot in place
- Impact measurement and KPIs
- Go / no-go before industrialisation
Scale
depending on the number of use cases and domains to connect
- Shared data platform
- MLOps and LLMOps pipelines
- Change management and extension
Run
service commitment defined with you
- Continuous observability and quality
- FinOps optimisation of data and AI
- Continuous compliance audit
Data made answerable
Fraud caught in real time, KYC cut by more than half
+40% fraud detection · −60% KYC time
Detect fraud on massive volumes in real time, shorten a KYC slowed by scattered customer data, and make regulatory data reliable, under heavy compliance and sovereignty constraints.
A governed data foundation with MDM for a single customer reference and continuously measured quality, detection models wired into the flows, and a data copilot whose every answer carries its sources.
A data software factory augmented by GenAI agents
×2 delivery velocity · 100% Row Level Security coverage
A rising volume of business requests that data delivery could no longer absorb, and a data estate piling up, with no shared semantic model and no end-to-end security.
A structuring semantic model, shared standards and Row Level Security on sensitive domains, with specialised agents across the cycle and skills transfer from the first sprint.
Cinké Évolution : field operations steered by data
Power BI · Field operations planned
Regional teams planning field operations without a shared view of the data, each directorate preparing its schedule from its own extracts.
An agile visualisation tool that turns operational data into a single steering view, so scheduling runs on the same figures across directorates.
Insights & Perspectives
Semantic search and information retrieval
Embeddings, cosine similarity, the retriever and reranker duo, vector databases: what it takes to build an accurate RAG at scale.
Four retrieval techniques to improve RAG
CRAG, Self-RAG, RAG-fusion and Fast GraphRAG: four advanced techniques that make a RAG system more accurate and more robust.
Data analytics: types and benefits
Descriptive, diagnostic, predictive, prescriptive: what each family of analysis brings, and which question it actually answers.
The rest of the data journey
Make your data answerable
A shared semantic model, an assistant that cites its sources and governed access.
Frequently asked questions
An assistant that lets you query data in plain language and answers from the company's reference data, with sources attached. What makes it usable is not the conversation, it is that every answer can be traced back.
By grounding it in your reference data rather than the model's memory, and exposing the sources of every answer. What it cannot find, it must not produce.
Because without shared definitions, two departments get two different answers to the same question. The semantic model fixes what an indicator means, once.
No, it complements them. A dashboard answers the questions you knew to ask in advance, the assistant answers the ones that come up in a meeting and have no dedicated report.
Through the same permissions as the rest of the foundation: Row Level Security, masking by profile and audit logs. The assistant only sees what the person querying it is allowed to see.
It is not answered. If two definitions of the same indicator coexist, the assistant sends the question back to the indicator's owner rather than picking one. A business arbitration made by a machine does not hold up in a meeting.
Framing takes 4 to 6 weeks depending on the number of sources, entities and the level of compliance required, and produces the quality audit, the value and feasibility matrix and the roadmap. A measured copilot in a real environment follows in 6 to 10 weeks.