Bilytica · Company profile
Who we are, how we make data AI-ready, and the results
Bilytica Company Profile
Good morning, and thank you for your time. This is the Bilytica company profile. We are a Saudi data and AI company based in Riyadh, and we help organisations in the Kingdom with trusted data, sovereign AI and AI agents. Along the bottom are some of the more than 200 Saudi organisations we are proud to work with: stc, Saudi Aramco, PIF, NEOM, the Saudi Central Bank, the Ministries of Interior and Health, Mobily, Najm and Elm.
A Riyadh-based data and AI company
Who we are. Bilytica is a data and AI company based in Riyadh. We turn scattered data into trusted decisions, then put secure, sovereign AI to work on top of it. More than 200 Saudi organisations work with us, including stc, Saudi Aramco, PIF, NEOM, the Saudi Central Bank, the Ministries of Interior and Health, Mobily, Najm and Elm, the Ministries of Hajj and Umrah and Municipal Affairs, Imam Abdulrahman University, SWPC, Nesma, Tawuniya and the Royal Commission for Riyadh City. Our solutions handle more than 600 million AI interactions a year. We have been in Riyadh since 2015 and have offices in five countries. And we don't only deliver projects: we build and scale technology companies. Cloudpital, our AI health cloud, runs in more than 400 clinics across Saudi Arabia and MENA. PayPeople runs payroll and HR for more than 900 businesses. And Bilytica AI has nine AI products, including Sawal, Idrak and Idrak Engage. We are a technology company that stays a step ahead.
Everyone wants AI. Most data isn’t ready for it.
Every organisation we meet wants AI. But most are not ready for it, for three reasons. First, the data is scattered: ERP, CRM, HR and spreadsheets that don’t talk to each other. Second, the numbers don’t match: two reports give two answers, and leaders stop trusting the data. Third, the data can’t leave: under PDPL and NDMO, public AI tools are simply not an option for sensitive data. AI is only as good as the data under it. That is exactly where we start. Ask: which of these sounds most like your organisation today?
How an AI agent gets its answers, and where it breaks
This slide shows the full path from your data to an AI agent's answer, and it works for technical and non-technical people in the same room. For the non-technical view: data starts in your systems, like ERP, CRM, HR and spreadsheets. We connect to them and pull the data out safely. We clean and govern it. We put it in one trusted layer with one version of every number. Then the agent finds the right facts, only what that user is allowed to see, and answers or takes action, with its source. For the technical view: connectors through APIs, ETL or ELT and change data capture; data quality, master data management and a catalog; a warehouse or lakehouse with a semantic model; text-to-SQL and retrieval with row-level security; and a sovereign language model with tools and an audit log. The key point is the orange zone. Your data is where AI projects usually break. Look at the red line under each step: one customer spelled three ways, two definitions of revenue, feeds that break silently. Bad data in means a confident wrong answer out. That is why we start with the data. Ask: at which of these steps do you think your problem sits today?
Same AI, same question. Only clean data gets it right.
This slide makes the data problem concrete, with examples we find in almost every project in the Kingdom. First, duplicates: one customer entered three times under different names, so the agent counts three customers and sends three offers. Second, Arabic and English: Riyadh written four different ways, so sales are split across four "cities". Third, Hijri and Gregorian dates: the same date in three formats, so months get mixed up and trends are wrong. Fourth, numbers and currency: Arabic digits, blanks and mixed units, so totals drop values or mix currencies. On the right is the result. Same model, same question. Before cleansing, the agent gives a confident wrong number. After cleansing, it gives the right number with its source. This is an illustrative example; the figures are for illustration only. The key message is the bar at the bottom. Without clean data you get wrong answers, then users stop trusting the agent, and then the AI investment is wasted. That is why we never switch on an agent before the data is clean. Ask: which of these examples do you have in your data today?
Data first, then AI.
This is how we fix it, and it follows the path you have just seen. Data first, then AI, in that order. First, connect and clean. This covers the first three steps of the path: your systems, connecting to them, and cleaning and governing the data. This is where we fix the examples from the last slide: one customer instead of three, one spelling of Riyadh, Hijri and Gregorian dates aligned. Second, govern and combine everything into one trusted warehouse on Fabric, Snowflake or your own platform. That is the trusted data layer: one definition of revenue, one version of every number, and dashboards leaders trust. Third, and only then, we put AI to work: the AI-ready context and the agent itself. The agent now gives right answers with their source, shows each user only what they may see, and runs inside the Kingdom, in a Saudi cloud or on-premises. Look at the bottom bar. On the last slide, without clean data, the chain was wrong answers, lost trust and wasted investment. With clean data, it is right answers, users who trust the agent, and an AI investment that pays off. One team does all three steps, so nothing gets lost between vendors.
A proven method, not improvisation
Why do our frameworks matter to you? Because every project runs on a proven method, not on improvisation. Together they form the Bilytica AI Operating System. Four things this gives you. First, faster and more predictable delivery: every project starts from tested stages, not a blank page. Second, gates, not guidelines: every stage ends with a “done when” test, and if it is not passed, the next stage does not start. Third, AI never runs on bad data: nothing is built on uncertified data, and no agent goes live without containment tests. Fourth, it is ready for Saudi regulators: mapped to NDMO, PDPL, NCA and SAMA, with the Arabic version as the governing one. On the right is one framework up close, the Data Foundation Framework, the one that fixes the data problems we just showed you. Five stages: capture, where every source gets a named owner; contract, where a breach of the data contract fails the build; consolidate, where reconciliation is explainable line by line, and this is where three Al-Rashid records become one customer; curate, where there is one definition per metric, one spelling of Riyadh and one definition of revenue; and certify, where tests pass on a schedule, not once. The client keeps the data contracts, the metric dictionary and an AI-ready certificate. And the certificate expires if the tests stop passing, so any AI built on that data is flagged. Along the bottom is the full system: readiness, value, data foundation, delivery, agent operations, assurance and sovereign AI. Every framework works the same way.
AI products, built in Riyadh
Once the data is fixed, you do not have to build every AI use case from zero. These are our own AI products, built here in Riyadh, and they all run on the trusted data layer we just described. Three lead the way, and each one starts from a problem you will recognise. Sawal, our flagship, is for when leaders wait days for a simple number: anyone can ask their data in plain Arabic and get a governed answer, with the source behind every number. Idrak Engage is for when customers wait on hold or on WhatsApp: AI agents on web chat, WhatsApp and voice answer, qualify and hand off to your team. And Idrak is for when your data cannot leave your building: enterprise AI that runs entirely on your own premises. Below them, the rest of the family, grouped by the job they do. To govern and comply: Burhan proves PDPL compliance from your own data, and Watheeq tracks contract expiry and renewals. To win and buy: Rasw drafts RFP answers from your past bids, and Miyar builds RFPs and scores the bids you receive. To run operations: TrackQlik verifies field visits with geofencing and AI photo checks, and VoomHub runs CRM, projects, field force and invoicing in one place. All of them are Arabic-first and run in the cloud or on your premises, inside the Kingdom. Pick the one closest to this customer’s need and go deeper.
Real client stories. Fix the data, and the results follow.
Two real client stories, kept short. The first is the data story. A central bank in the Middle East assembled its supervisory reports by hand, from returns filed by dozens of regulated institutions and merged in spreadsheets. That was slow, and it was hard to trace any figure back to its source. We built one governed data warehouse. Automatic reconciliation catches late or broken returns, and every published number has an owner and a definition. The result: 85 percent less time to produce supervisory reports, and every figure traces back to a filed return. The second is the AI story. A national programme office tracked 380 programme KPIs, but there was no single trusted place to ask about them, so leaders waited for answers they could trust. We put all the KPIs into one sovereign, certified lakehouse and added a bilingual Arabic and English question layer, where every answer is cited back to its source indicator. The result: 82 percent less time from question to a Cabinet-ready briefing pack. Notice the lesson in both: the data was fixed first, so the results could be trusted. Client names are withheld under NDA, and every figure is one the client’s own teams can audit. If the customer asks for more, we have stories in health, government, banking and telecom on our website.
Why leading Saudi organisations choose Bilytica
Why do leading Saudi organisations choose Bilytica? Because the usual way of doing AI does not fit the Kingdom. Read each column from the top, then the line at the bottom. With us your data stays in the Kingdom, on your premises or in a Saudi cloud, instead of leaving the country. We start with clean, trusted data, backed by more than 25 years in data, instead of a flashy AI demo. We build Arabic-first, here in Riyadh, with nine AI products of our own, instead of English first and Arabic later. You get one accountable team from strategy to support, instead of many vendors and no single owner. And you get AI live at scale, more than 600 million interactions a year, instead of pilots that stall. We work under NDMO, PDPL and ISO 42001, and in 2025 we signed an MoU with Saudi Arabia’s AI Society, under SDAIA technical supervision.
Chosen by the Kingdom’s leaders
More than 200 organisations in Saudi Arabia trust us, more than 50 across the GCC and more than 400 worldwide. The slide is grouped by sector, to show that we work across every major part of the Saudi economy. In government: the Ministries of Interior, Health, Hajj and Umrah and Municipal Affairs, the Royal Commission for Riyadh City and Nazaha. In energy, industry and giga-projects: Saudi Aramco, NEOM, the Saudi Water Partnership Company, Saudi Cement, Nesma and Emdad Al Khebrat. In finance and insurance: PIF, the Saudi Central Bank, Najm and Tawuniya. In telecom and technology: stc, Mobily and Elm. In education: Imam Abdulrahman Bin Faisal University and the Islamic University of Madinah. Across the GCC: Ooredoo and the Amiri Diwan in Qatar. And around the world: Vodafone, Deutsche Telekom, AT&T, Nokia, Shell, Walmart, Unilever, Coca-Cola, DHL, Lufthansa, Roche and Deloitte, along with Bayer, PepsiCo, H&M, LEGO, Ericsson, Novartis, Maersk and many more. Point to the sector closest to this customer.
Let’s build your first AI win.
So here is the next step. We don’t start with a big programme. We start with one focused workshop with your business, IT and risk leads. Together we pick the use case that matters most, prove it in a pilot on your own data, and then scale it with full support. You leave the workshop with a clear plan for your leadership. You can reach us at sales@bilytica.sa or bilytica.sa. We are headquartered in Riyadh, with offices in the UAE, Australia, Pakistan and Spain. If you are ready, let’s put the workshop in the diary today. Thank you.
