Data, analytics and AI teams across the Gulf, Europe, Asia and the Americas.

    We make the whole AI ecosystem work, from clean data to agents that act.

    Good data, good AI. Bad data, bad AI.

    Talk to our teamSee our work
    • Data engineering
    • Analytics
    • Models and document AI
    • Voice and text agents

    Over two hundred programmes, still running today.

    Who we work with

    Banks, government entities, hospitals, telecoms, manufacturers and retailers, across the Gulf, Europe, Asia and the Americas.

    What we do

    One team, from the raw data to the working agent.

    Programmes stall when every piece belongs to a different vendor. We build it as one chain.

    1. 01

      Data engineering and data lakes

      Every system telling the same story, and staying that way.

      • Pipelines and integration
      • Lakehouse on Databricks, Snowflake, Fabric
      • Governance that survives an audit
    2. 02

      Analytics, KPIs and dashboards

      Numbers your board argues with, not about.

      • One definition per metric
      • Dashboards in Arabic and English
      • Forecasting and decision models
    3. 03

      Models, LLM training and document AI

      The right-sized model, trained on your own material.

      • Fine-tuning and evaluation
      • OCR and document understanding
      • Guardrails, testing, monitoring
    4. 04

      Voice, text and data agents

      They finish the task inside your systems, not talk about it.

      • Voice agents on real calls
      • Wired into SAP, Oracle, Salesforce
      • Human handover and audit trail
    Everything we do

    Why programmes fail

    Most of the troublesits under the model.

    No model is clever enough to fix a number that was wrong before it arrived.

    The demo always works. Then it meets systems that disagree, records nobody owns and definitions that changed a reorganisation ago, and people quietly stop trusting the answer.

    So we start underneath. Clean, reconciled data with an owner for every number, then the models and the agents on top of it, which is why what we hand over gets used.

    How we fix the data layer

    The work

    Some of the work, and what actually changed.

    We name the sector, not the client. That is what we agreed with them.

    All case studies

    Retail bank · Western Europe

    −42%

    cost per handled call

    Voice agents on tier-1 service, hosted inside the bank

    Before
    Long queues, and no audio allowed to leave the bank.
    Now
    Agents take the routine calls in-house; people take the hard ones.

    Hospital network · North America

    19% → 7%

    appointment no-show rate

    Scheduling and follow-up agents on a cleaned patient record

    Before
    Reminders went to stale numbers, follow-up to whoever had time.
    Now
    Every patient reached on a record the clinicians trust.

    Government entity · Gulf

    A week → the same day

    time to a defensible answer

    Document AI and an Arabic assistant over a governed data lake

    Before
    Scanned archives nobody could search, so every request went to a person.
    Now
    Staff ask in Arabic or English and get the answer with its source.

    Tell us where you are

    Tell us where you are and we'll tell you where we'd start.

    Answer a few quick questions and one of our engineers writes back with an honest read of your data and what is worth doing first.

    • Takes about a minute to fill in, and we reply within one working day.
    • The first call is with an engineer, not a sales pitch.
    • Your answers stay with the two people who read them. No lists, no spam.