CEO and executive team
Answers in the meeting, not after.
Capability
Conversational Data & Self-Service Analytics
Ask a question in Arabic or English and get the number, with the query behind it.
Questions answered without an analystQuery accuracy against certified metricsTime from question to answerReporting backlog
The service at a glance
The problem, our work, your result
- Business problem
Every data question waits in a queue for the BI team.
Leaders ask questions in meetings; answers arrive days later as a new dashboard request.
- What we do
We put a governed conversational layer on your data: people ask in Arabic or English, and the platform answers from certified datasets with the logic shown.
- What you receive
- Ask in Arabic or English
- Answers from certified data
- Shown logic and sources
- Governed access by role
Who it is for
Built for the people who own the decision
Business teams
Self-service without tickets.
BI and data teams
Time for depth, not queue-clearing.
Risk, compliance and security
Only governed data is exposed.
The goal: an AI-ready organisation
Where this service moves you
Data readyThis service
Connected, clean, governed and allowed for AI use.
KPIs readyThis service
Agreed measures, one definition each, a named owner.
Organisation ready
Roles, decision rights and skills to adopt AI safely.
=AI that reaches production and creates value
Approach
How we work with you
- 01
Certify
Define the trusted datasets first.
- 02
Model
Build the semantic layer and KPIs.
- 03
Open
Let people ask, with guardrails.
- 04
Improve
Learn from the questions asked.
Engagement options
Start where you are
One domain pilot
Conversational analytics for one team.
Department roll-out
Certified self-service per function.
Enterprise data dialogue
One governed layer for everyone.
Scope
What is in, what is not
Included
- Semantic layer build
- Conversational interface
- Access governance
- Usage analytics
Handled separately
- Source system fixes
- Data platform rebuild
Bilytica frameworks behind this service
Our own methods, not a generic playbook
DFF
All Bilytica frameworksData Foundation Framework
The layers that turn scattered systems into data an AI system is allowed to use.
See the frameworkVALUEAI Value Framework
From use-case long list to booked benefit, with unit economics attached at every step.
See the frameworkIn short
What this actually is
A natural-language layer over a governed semantic model: the question is translated into a query against certified metrics, the answer shows the SQL and the source, and permissions follow the user. The governance work is the product, without a semantic layer, natural-language analytics produces confident, inconsistent numbers.
Key points
What matters before you buy
- 01
One definition per metric
'Active customer' has to mean one thing before a model can answer questions about it.
- 02
Show the query, always
Analysts trust what they can inspect; hidden SQL is how a wrong number reaches a board pack.
- 03
Permissions follow the person
Row and column security is enforced in the warehouse, not in the prompt.
- 04
Arabic questions, Arabic answers
Business terms are mapped bilingually so the same question works in either language.
Delivery
How we build it
Foundation check
Before any buildWe inspect the systems, the data behind them and the quality of the records this capability will depend on, then write down what has to be fixed before anything is built.
Narrow first release
First releaseOne workflow, one team, real traffic, measured against the baseline we captured before we started.
Controls and evaluation
Runs in parallelGuardrails, approval gates, evaluation sets, logging and the evidence pack your auditor and your regulator will ask for.
Scale and hand over
After sign-offAdditional workflows, sites or languages, then runbooks and training so your team can operate it without us.
Data foundation
What has to be true in your data first
Great AI cannot hide bad data. These are the conditions this capability depends on, we fix them as part of delivery, not after go-live.
- A modelled warehouse or lakehouse, not raw source tables.
- Certified metric definitions with named owners.
- Row- and column-level security already enforced.
- A bilingual business glossary.
Controls
Guardrails we put around it
Every action is logged and replayable
Inputs, retrieved context, model version, output and the human decision are written to an immutable log, so an auditor can reconstruct any single interaction months later.
Data stays where your regulator requires
In-country, in-region or on-premise deployment, with the model portfolio right-sized to the workload instead of routing everything to a frontier endpoint abroad.
Accuracy is monitored, not assumed
A held-out evaluation set runs on every model or prompt change, with drift alerts and a documented rollback path when quality moves.
A human owns every consequential decision
Agents draft, route and execute reversible steps. Anything that moves money, changes a record of consequence or affects a person's rights stops at a named human approver with the reasoning attached.
Fit
Systems it plugs into, and what it moves
Integrations
SnowflakeDatabricksBigQueryMicrosoft FabricPower BIQlikTableaudbt
KPIs it moves
- • Questions answered without an analyst
- • Query accuracy against certified metrics
- • Time from question to answer
- • Reporting backlog
- • Adoption by business unit