Where AI meets numbers people already trust

AI on top of a governed data platform — because a natural-language answer is only as good as the model underneath it.

Le problème

A natural-language layer over untrusted data produces confident nonsense

"Ask your data a question" demos beautifully and fails quietly. If two teams disagree on what revenue means, an AI assistant will pick one definition without telling anyone, and the answer will be presented with more confidence than a human analyst would use.

Notre approche

Fix the semantic layer first, then add the assistant

We model the data and agree the definitions before any AI touches it. The assistant then queries the modelled layer rather than raw tables, and shows the query it ran — so an analyst can check the work rather than take it on faith.

Ce que nous construisons

Nos capacités

  • AI-powered dashboards

    Generated narrative summaries alongside the visuals, grounded in the same governed model.

  • Natural-language analytics

    Questions answered against a semantic layer, with the generated query always visible.

  • Automated reporting

    Recurring packs assembled from source systems instead of rebuilt by hand.

  • Data quality automation

    Tests on transformations and freshness monitoring that alert before a stale number is presented.

  • Data classification

    Tagging sensitive fields so downstream tools can enforce handling rules.

  • Anomaly detection

    Alerting on values outside expected bounds, with the threshold explained.

  • Forecasting

    Baseline forecasts with published error ranges, not a single confident line.

  • Data assistants

    Self-service question answering for teams who currently queue for an analyst.

  • Power BI AI integration

    AI features wired into existing Power BI estates rather than replacing them.

  • Automated KPI explanations

    Drafted "why did this move" narratives for a human to verify before circulation.

Architecture

Comment tout s'articule

From source system to business user

The assistant sits at the end, behind the semantic layer. That ordering is the whole design — reverse it and you get fast answers to the wrong question.

  1. 01Business database
  2. 02Data pipelineTested transformations
  3. 03Data warehouseModelled, documented, versioned
  4. 04Power BIGoverned semantic model
  5. 05AI analytics assistantQueries the model, shows the query
  6. 06Business userCan verify the work
Intégrations

Systèmes avec lesquels nous travaillons

Plateformes et services que TOOKLI utilise activement sur ses missions.

Microsoft data platform

  • Power BI
  • Microsoft Fabric
  • Dataverse
  • Azure Data Factory
  • Azure Synapse

Databases

  • SQL Server
  • PostgreSQL
  • Azure SQL

Transformation

  • dbt
  • Python
  • SQL

Models

  • Azure OpenAI
  • OpenAI

Cette liste indique les systèmes que nous intégrons — ni partenariats, ni revente, ni certifications. Si un outil que vous utilisez n'y figure pas, demandez-nous : nous vous dirons franchement si nous l'avons déjà mis en œuvre.

Cas d'usage

À quoi cela ressemble concrètement

Des exemples de ce que nous pouvons construire par secteur. Les projets réellement livrés figurent dans nos études de cas, avec des résultats chiffrés.

  • Operations

    The monthly pack, assembled automatically

    Two days of a capable person's month returned, with the figures traceable.

  • Finance

    Variance explanations, drafted

    A first-pass narrative on what moved and why, for a human to check and sign off.

  • E-commerce

    Anomaly alerting on trading metrics

    Told when conversion drops, rather than discovering it in next week's review.

  • Real estate

    Portfolio reporting across systems

    One governed view where there are currently several partial spreadsheets.

Notre méthode

Processus de livraison

  1. 01

    Audit the current reporting

    What exists, who uses it, and where two reports disagree. There are always some.

  2. 02

    Agree the definitions

    Written, versioned, reviewed. This is the step that makes everything after it possible.

  3. 03

    Model the semantic layer

    Tested transformations, documented metrics, monitored freshness.

  4. 04

    Rebuild reporting on the model

    Existing dashboards repointed, so nobody loses what they rely on.

  5. 05

    Add AI where it earns its place

    Summaries, anomaly explanations, natural-language querying — each measured.

  6. 06

    Monitor quality

    Freshness and test failures alert an engineer before an executive notices.

Sécurité et fiabilité

Comment nous garantissons sécurité et traçabilité

Des pratiques d'ingénierie, pas des certifications. TOOKLI ne détient aucune certification de sécurité et n'en revendique aucune.

  • The generated query is always visible

    A natural-language answer shows the SQL or DAX it ran. An analyst can check it, which is the difference between a tool and an oracle.

  • We do not claim vendor features that do not exist

    AI capability in the Microsoft data stack changes quickly and is licensed unevenly. We confirm what is actually available on your tenant and licence before designing around it.

  • Definitions are governed

    Metric definitions live in version control and change through review, so a number cannot quietly shift meaning between two reports.

  • Forecasts carry error ranges

    A forecast without a stated confidence interval invites false precision. We publish the range and the backtest.

Questions fréquentes

Services associés

  • Business & Workflow Automation

    Business process, document, data and system automation. We combine ordinary software workflows with AI only where judgement is actually needed.

    En savoir plus
  • AI Integration

    Bring AI capabilities into your existing applications — search, extraction, classification, summarisation and assistants — without building or training models yourself.

    En savoir plus
  • Data Analytics & Business Intelligence

    One trustworthy version of the numbers, and dashboards people actually open.

    En savoir plus

Do people trust your numbers?

If two reports disagree, that is the problem to fix first. We will audit the reporting and tell you what is actually wrong before proposing any AI.

Review your reporting