AI & Machine Learning Company in Cairo

Cost tracks scope. A scoped AI proof of concept runs six to ten weeks, covering data audit, a baseline model, and a hosted demo. A custom production ML model (fraud scoring, credit decisioning, merchant scoring, KYC document intelligence) adds MLOps, monitoring, and a CBE or FRA-aligned model card. Full production AI systems add RAG, multiple models, fine-tuning, and enterprise integrations. Cairo rates sit below Dubai and Riyadh because of the EGP and the local talent base, while delivery quality is benchmarked against the Cohere-and-Borealis-AI bar we hold elsewhere. Fixed-fee in EGP with a refreshed USD shadow rate every quarter.

Yes, and this is where most off-the-shelf Arabic models fail Cairo clients. Egyptian Arabic is genuinely different from Gulf MSA (vocabulary, syntax, common loanwords, the negation patterns, the question particles), and a sentiment model trained on Saudi tweets will misclassify Cairene customer-service transcripts at a rate that does not survive production. We fine-tune open-weight models (Llama 3, Mistral, AceGPT, Jais) on Egyptian dialect data, maintain dialect benchmark sets across Cairene, Said (Upper Egypt), Alexandrian, and Delta speech, and evaluate against Egyptian production transcripts rather than MSA benchmark sets. For chatbots and IVR we tune diacritic handling and code-switching (Arabic-English-French mixing is common in Cairo professional contexts) so the model does not break on real input.

Fawry-scale anti-fraud (200,000-plus agent outlets, multi-product flows across bill payment, top-up, remittance, and bank handoffs) needs agent-level behavioural signal density that a transaction-only model misses. We build graph models that capture agent-merchant-customer relationships, classical gradient-boosted models on transaction tabular features, and sequence models on time-windowed agent activity. Agent-credit scoring (deciding which agents qualify for higher float ceilings) layers on top, treating the agent itself as the credit subject. Models ship with SHAP per-decision explainability because CBE expects high-impact financial AI to justify each adverse action, and they run in shadow mode against the incumbent fraud system for at least a sprint before promotion.

Law 151/2020, enforced by MCIT, requires lawful basis for processing, subject rights (access, correction, deletion), data protection impact assessments for high-risk processing, and cross-border transfer cover for any data leaving Egypt. Training data that includes personal data needs documented lawful basis, our DPIA covers risk to subjects, and cross-border transfer to AWS me-south-1 Bahrain or Azure UAE North uses Standard Contractual Clauses that we draft with the client's counsel. For credit and fraud AI we also document fairness review under FRA expectations, because non-banking financial AI is moving toward explicit FRA oversight regardless of where the underlying personal data lives.

Yes. Super-app AI (lending, BNPL, ride-hailing, merchant payments in one app) needs a unified customer representation so signal from one product feeds risk in another. We build a shared customer-embedding layer that captures behaviour across products, then per-product decision models (BNPL approval, micro-loan underwriting, driver rating, merchant onboarding) draw from that representation. Because MNT-Halan is publicly listed, the model documentation has to satisfy IPO-grade audit (model lineage, training data snapshot, version history, decision sample reproducibility), which we treat as default rather than an afterthought. CBE expects clear separation between regulated lending models and the rest of the super-app's AI surface, and we wire that boundary explicitly.

We default to AWS me-south-1 Bahrain (closest stable AWS region with around 30 to 50 ms latency to Cairo) for inference and storage of Egyptian personal data, with Azure UAE North in Dubai as the regional alternate. For training that needs scale we use AWS eu-south-1 Milan or eu-west-3 Paris under Law 151/2020 cross-border SCCs. Egypt-resident infrastructure (when a client demands it for government or regulated banking projects) routes through Telecom Egypt and EGX-listed datacentre operators with a Cairo or Alexandria origin. The Law 151/2020 cross-border transfer instrument and the CBE or FRA notification (where required) are part of our default delivery, not a separate phase.

A typical Cairo fintech model (fraud scoring, credit decisioning, KYC document extraction, AML transaction monitoring, merchant scoring) takes fourteen to twenty-two weeks from kickoff to production, assuming clean historical data and CBE plus FRA documentation as part of scope. Week 1 to 4 is data audit and baseline. Week 5 to 12 is modelling, iteration, and challenger testing against the incumbent. Week 13 to 18 is MLOps, monitoring, shadow mode deployment, and internal audit review. Week 19 onward is gradual rollout. If you are pre-data or need labelling, add six to eight weeks. We have shipped to this cadence inside Egyptian-regulated stacks.

Yes, and this is one of the more interesting Cairo AI problems. Egyptian SMB merchants often have no bureau file, irregular invoicing, and cash-heavy operations, so conventional credit signal is sparse. We substitute behavioural data (order frequency on the B2B platform, basket consistency, payment reliability on prior credit lines, geographic stability, agent visit cadence) and ship a model that explicitly handles missingness rather than treating thin-file as a rejection. The model ships with SHAP per-decision explainability, an FRA-aligned model card, and an explicit fairness review across governorates because the merchant base spans Cairo, Alexandria, the Delta, Said, and Sinai and the model cannot quietly underwrite one region better than another.