AI for Product, Data & Developer Teams
The teams that build and run digital products are AI’s most immediate users, and the ones with the clearest measure of value: time to insight, time to integrate, defects found before release. Eurisko builds the product, data and developer use cases that move those numbers: natural-language business intelligence over governed metrics, synthetic customer and transaction data, data-quality and reconciliation anomaly detection, an API documentation and integration copilot, automated test-case and mock-payload generation, and brand-safe generation of product propositions and campaign creative, for enterprises across the GCC, the wider Middle East and Lebanon.
Each one is grounded in your own specifications, metric definitions and brand rules, validates what it generates, and leaves a traceable record from question to answer and from requirement to test.
The answer, the dataset and the test, generated and verified.
Enterprises in the region are investing heavily in data platforms and developer tooling, and the bottleneck has moved from infrastructure to people: analysts fielding the same executive questions, data engineers chasing silent reconciliation breaks, integration partners waiting on documentation, testers writing payloads by hand.
Generative and predictive AI remove those queues when they are grounded. A governed semantic layer lets executives ask questions in plain language and get trusted charts. Synthetic data lets teams build and test without touching production records. Anomaly models watch data quality continuously. Copilots answer integration questions from the actual API specification and generate tests that trace back to requirements.
Eurisko builds these as internal products with the same discipline as customer-facing ones: evaluation suites, access control, audit trails and a measured effect on the metric the team already tracks.
Eight product, data and developer use cases, measured on the team’s own metrics.
Each one starts from artefacts you already maintain, specifications, metric dictionaries, brand guidelines and test suites, and is evaluated against a human-reviewed sample before rollout.
Natural-language BI & executive insights
Translates plain-language questions into approved calculations through a governed semantic layer, validates the result and presents a trusted chart with a concise narrative and its data lineage.
Synthetic customer & transaction data
Profiles the statistical and behavioural patterns of an approved sample or specification, generates constrained synthetic datasets and reports both privacy risk and analytical utility, with scenario packs for rare events.
Data quality & reconciliation anomaly detection
Learns expected ranges, relationships and reconciliation patterns, flags row-level issues and KPI-level drift as they appear and estimates the likely source and the reports affected.
API documentation & integration copilot
Indexes OpenAPI specifications, integration guides, code samples and error catalogues, answers implementation questions with grounded snippets and cites the exact source section.
Automated test-case & mock-payload generation
Parses API contracts, acceptance criteria and business rules into a traceable test matrix with valid, invalid and boundary payloads, each checked against the schema before it reaches a test runner.
Product proposition & benefits composer
Retrieves eligible features, benefits and commercial constraints from an approved catalogue and composes a coherent proposition, benefit stack and customer message, with every claim traceable to the catalogue.
Campaign creative with brand guardrails
Generates campaign concepts and channel-specific copy from a verified offer brief, brand voice and mandatory language, and flags unsupported claims, missing disclosures and tone deviations before review.
Generative design studio
Produces visual concept directions and mood boards for product and brand assets from your guidelines, personas and campaign themes, with visual and content guardrails and a brand-compliance checklist for your designers.
Grounded in the specification, validated before it ships.
Developer use cases are a good example of the pattern. The copilot indexes the API specification, guides and error catalogue, answers with retrieved passages and cites the section, so an integration partner gets the same answer your best engineer would give. The test generator reads the same contract plus the acceptance criteria, enumerates valid, invalid and boundary cases, and validates every generated payload against the schema before a human sees it.
Data use cases follow the same logic with a semantic layer instead of a specification: metrics are defined once, questions are translated into those definitions rather than free-form SQL, and results carry lineage. Synthetic data and anomaly detection are evaluated on statistical similarity, downstream utility and privacy risk, reported together.
Creative use cases retrieve from the brand guide and the approved offer facts, generate within them, and run a validation pass that flags claims, disclosures and tone before anything reaches a reviewer.
Data residency, Arabic analytics and the region’s engineering bench.
Data and AI programmes in the Gulf operate under national data strategies and residency expectations: Saudi Arabia’s data and AI authority and personal data law, the UAE’s federal data protection law and sector rules from central banks and telecom regulators. Synthetic data, governed semantic layers and in-perimeter deployment are how teams move fast without moving regulated data, and they are the default in how we build.
Analytics also has to speak both languages. Executives ask questions in Arabic and English, dashboards and narratives are read in both, and dimension values such as city names and product lines exist in two scripts. Our semantic layers and narrative generation are bilingual by design. And because Eurisko runs a 200-strong engineering organization in Lebanon serving the Gulf, the copilots and test generators we build are the ones our own teams use.
A process built on evidence.
Discovery & success metrics
A workshop with the business owner, the data owner and the technology lead fixes the decision to improve and the number that proves it.
Data readiness
We profile the sample, masked or synthetic, and say plainly what it can and cannot support before anything is built.
Prototype
Model, prompt, retrieval or optimization logic plus a working demo your team can click through, not a slide.
Evaluate & play back
Accuracy, groundedness, fairness and cost are measured against an agreed test set and reported with their limitations.
Productionize
Integration, security review, MLOps and monitoring inside your perimeter, scoped only once the evidence supports it.
Frequently asked questions.
Can executives really trust a chart generated from a question?
Yes, when the question is translated into governed metric definitions rather than free-form queries. The semantic layer fixes what “active user” or “net revenue” means, the result is validated, and the chart shows its lineage. If a question cannot be answered from approved metrics, the assistant says so.
Is synthetic data safe to share with vendors and offshore teams?
That is its purpose. Synthetic datasets are generated from statistical profiles or specifications, tested for privacy risk and utility, and reported with both. Teams, including our own, build and test on synthetic data and only touch production records inside your perimeter.
Will the API copilot work on our internal, undocumented services?
It works best on what is documented, which is often the first improvement: we help generate and structure OpenAPI specifications and guides from code and existing material, then index them. Answers always cite a source, so gaps in documentation become visible rather than hallucinated.
How do brand guardrails work for generated creative?
Generation draws only from the brand guide, the approved offer facts and the mandatory legal language, and a validation step flags unsupported claims, missing disclosures and tone deviations. Your reviewers still approve; they just start from a draft that has already passed the checks.
Does our data have to leave the country or our data centre?
No. For banks, telecoms and government entities in Saudi Arabia, the UAE, Kuwait, Oman and Lebanon we deploy inside your private cloud or on-premises, use open-weight models when data cannot leave the perimeter, and start with masked or synthetic samples so no customer record is needed to prove the idea.
Where do we start if we want more than one use case?
Pick a small portfolio rather than isolated ideas: one quick win, one strategic differentiator and one operational efficiency case, ideally sharing one data foundation and one evaluation framework. We run a short selection workshop with your business, data and technology leads to shortlist by value and data readiness.
Explore more from Eurisko.
AI for Enterprise Governance & Responsible AI
Policy assistants, evaluation workbenches, PII redaction, plain language and governed agents.
AI for Customer Service, Operations & Knowledge
Copilots, bilingual assistants, call summaries, QA and enterprise knowledge (RAG).
AI for Merchant & Payments Intelligence
Merchant 360, churn, decline recovery, smart retries, onboarding and risk monitoring.
Artificial Intelligence & Data Analytics
Generative AI, chatbots, computer vision, NLP and predictive systems.
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