Enterprise software that does more than record transactions — platforms that automate decisions, analyse data as it arrives, and improve as they accumulate history.
Core capability runs live before the platform is half built, so value is testable early.
Standard single-platform delivery from requirements through to production handover.
Search, chat, document processing and voice, with dialect handling and RTL interfaces.
Model governance, audit trails and data residency specified during design, not after.
Adding a chatbot to a form does not make an application intelligent. The distinction is architectural: an AI-powered application treats prediction as a first-class part of its logic, alongside the database and the interface.
Traditional enterprise software encodes rules a person wrote down. When conditions change, someone edits the rules. An AI-powered platform infers its rules from data, which makes it adaptive — and also makes it something that has to be monitored, because a model trained on last year's behaviour will quietly decay when behaviour changes.
That trade-off is the design conversation. Some decisions should stay deterministic: an application that calculates VAT should not be probabilistic. Others benefit from inference: demand forecasting, anomaly detection, prioritisation. Deciding which is which, per feature, is the first thing we do.
Model versioning and rollback, drift monitoring with alerting thresholds, explainability for any decision affecting a person, human override paths on automated actions, and audit logging sufficient for a PDPL review. These are not add-ons priced separately — a platform without them is not finished.
Most engagements are one of these, or a deliberate combination of two. Each carries a different AI architecture.
Turns enterprise data into a decision surface: live dashboards, predicted KPIs rather than only historic ones, natural-language querying, and anomaly alerts that fire before a threshold is formally breached.
Personalises each touchpoint using behavioural signals: recommendation engines, intelligent search, sentiment analysis on service interactions, next-best-action prompts, and Arabic-language conversational interfaces.
Embeds forecasting into operational workflow rather than leaving it in a separate reporting tool: demand planning, equipment failure prediction, financial scenario modelling and churn risk scoring.
Replaces manual dashboard-watching with systems that monitor assets, dispatch resources against changing conditions, track KPIs continuously and escalate what needs a human.
Combines deterministic business rules with model scoring to make or recommend high volumes of decisions, with explainability on every output and a human override path where the decision affects a person.
Drawn on as the use case requires. Not every platform needs every capability.
Trained models — classification, regression, forecasting, NLP — embedded in application logic and returning within the latency budget the interface can tolerate.
Streaming pipelines feeding live dashboards, alerting and inference, from operational data sources through to the screens people actually watch.
Behavioural modelling, collaborative filtering and content-based recommendation, serving individual users at scale without a per-user rule set.
Rules engines paired with model scoring, handling high decision volume with explainable outputs and a human override path on anything consequential.
LLM-backed document generation, intelligent search, Arabic question-answering and assistants, scoped to tasks where generation is genuinely better than retrieval.
Data residency, model governance, audit trails and PDPL controls built into the platform rather than documented alongside it.
Requirements, data audit, AI capability mapping and regulatory review. Ends with a decision on which features are deterministic and which are inferred.
Architecture, interface design, data pipeline design, and the SDAIA and PDPL compliance plan that governs the build.
Agile development with core AI capability running in production from week eight, not demonstrated in a staging environment.
Connection to ERP, CRM and cloud systems, with end-to-end validation against real data volumes.
Production deployment, monitoring thresholds set, team training and handover documentation in Arabic and English.
Total 13–16 weeks to production, with first AI capability live at week 8.
The week-eight milestone is deliberate. Putting one AI capability into real use early tells you whether the assumptions behind the whole platform hold, while there is still budget and time to change direction.
It also gives the team using it eight weeks of familiarity before the full platform arrives, which does more for adoption than any amount of training at go-live.
Book a scoping sessionRegulatory context shapes architecture more than sector does. Both are established during discovery.
| Sector | Typical applications | Regulatory context |
|---|---|---|
| Financial services | Credit scoring, fraud detection, regulatory reporting automation | SAMA · PDPL |
| Government and public sector | Citizen service platforms, e-government intelligence, KPI tracking | SDAIA · NDMO |
| Energy and utilities | Predictive maintenance, consumption optimisation, HSE intelligence | Sector-specific |
| Telecom and digital | Network intelligence, customer AI systems, revenue assurance | CST · PDPL |
| Healthcare | Clinical decision support, patient management, Arabic medical NLP | SFDA · NDMO |
| Retail and e-commerce | Personalisation, demand forecasting, dynamic pricing | PDPL · ZATCA |
| Real estate and hospitality | Asset management AI, guest personalisation, market prediction | Sector-specific |
| Education | Adaptive learning, Arabic-language tutoring, student analytics | MOE · PDPL |
| Logistics and supply chain | Supply chain intelligence, route optimisation, warehouse automation | ZATCA · PDPL |
A logistics operator ran shipment tracking, exception handling and client reporting across three disconnected systems, with staff rekeying between them. Crux built a single operations platform with anomaly detection on shipment events, so routine movements clear automatically and the team sees only what has gone wrong. First capability went live at week eight; the full platform completed at sixteen.
What technology and procurement teams ask before committing to a platform build.
Business platforms that embed machine learning, natural language processing and intelligent automation as core capabilities rather than bolt-on features — supporting automated decisions, prediction, personalisation and continuous improvement from accumulated data.
Traditional software follows rules a person wrote and needs editing when conditions change. AI-powered applications infer patterns from data and adapt without reprogramming. The trade-off is that they need monitoring: a model trained on last year's behaviour degrades as behaviour shifts.
Yes, and they are designed in from the start. Arabic support covers search, conversational interfaces, document processing and voice, with attention to dialect variation and right-to-left interface behaviour. Retrofitting Arabic into an English-first platform costs substantially more.
A standard platform runs 13 to 16 weeks from requirements to production, with core AI capability live at week eight. Multi-module platforms typically take six to nine months depending on integration count.
Scope drives price. Focused single-capability platforms typically start around SAR 350,000; comprehensive multi-module systems reach SAR 3,500,000 and above. A discovery session produces a fixed proposal rather than a range.
Models degrade as conditions change, so every platform ships with drift monitoring and a retraining plan. Crux hands over documentation and training, and can either support the platform ongoing or transition it entirely to your team.
The core models and application logic that give an intelligent platform something to reason with.
Explore → AutomateEmbedded automation that removes the manual steps between what the platform decides and what happens.
Explore → ConnectConnect the platform to the ERP, CRM and data systems your organization already runs.
Explore →A scoping session works through your requirements feature by feature and returns a plan showing which need AI, which are better served by deterministic logic, and what the phased build looks like.