AI for Enterprise Governance & Responsible AI
The enterprises that get lasting value from AI are the ones that can govern it: know what each model does, prove how well it does it, keep personal data out of it and keep a person accountable for every consequential action. Eurisko builds the enterprise governance and responsible AI use cases that make this operational: policy and regulatory knowledge assistants, RFP and proposal assistants, a model evaluation and prompt testing workbench, PII detection and redaction, plain-language communication, personalized education content, scenario and portfolio simulators and governed agentic workflow orchestration, for banks, telecoms, government entities and large enterprises across the GCC, the wider Middle East and Lebanon.
Together they form the control layer that the rest of our AI use cases run on: evaluation before release, privacy before processing, human approval before action and a record of all three.
Responsible AI as working software, not a policy document.
Most organizations in the region now have an AI policy. Far fewer have the tooling to apply it: a way to test a model or a prompt before it changes, to prove that personal data never reached a third-party API, to show a regulator which rule a decision followed, or to let an agent act inside operational systems without losing control of it.
Eurisko builds that tooling as products your teams run every day. An evaluation workbench scores models and prompt versions against a human-calibrated rubric and tracks regressions, cost and latency. A redaction assistant detects and masks personal, financial and organization-specific data before documents and transcripts reach any model. Knowledge assistants answer policy and regulatory questions with citations, effective dates and a clear statement of when legal review is required. Agentic workflows plan and execute bounded operational tasks with approval gates and a complete audit trace.
The same layer underpins every other use case we deliver, which is why clients who start here find the next use cases faster to approve and cheaper to run.
Eight governance and responsible AI use cases, ready to prove on your data.
Each one is scoped to produce evidence a risk, compliance or audit function can use, and each is evaluated against a human-reviewed sample before it is relied on.
Policy & regulatory knowledge assistant
Indexes policies, regulations and procedures with effective dates, answers with citations and version comparisons, produces an operational-impact checklist and states clearly when legal review is required.
RFP & solution proposal assistant
Extracts requirements from a request for proposal, maps them to approved capabilities and references, drafts responses grounded in approved sources and flags the gaps that need a human answer.
AI model evaluation & prompt testing workbench
Runs a controlled set of prompts and edge cases across models and prompt versions, scores them against a human-calibrated rubric and tracks regressions, cost and latency, producing a decision-ready comparison.
PII detection & redaction assistant
Detects personal, financial and organization-specific sensitive fields in documents, transcripts and spreadsheets, then redacts or tokenizes them with a human-review queue for uncertain cases.
Plain-language communication transformer
Rewrites dense statements, notices and terms into accessible plain language while preserving amounts, dates, obligations and approved terminology, with a side-by-side verification view.
Personalized education content
Selects a lesson from an approved library and personalizes the examples, tone and next step to the customer or employee, generating micro-lessons, tips and quizzes without giving advice.
Scenario forecast & portfolio simulator
Builds a baseline from historical KPIs, lets decision makers adjust the key drivers and simulates outcomes with uncertainty ranges, sensitivity analysis and transparent assumptions.
Governed agentic workflow orchestrator
Plans and executes a bounded operational workflow using procedures, a knowledge base and approved tools, requests human approval at defined gates, records every action and stops when policy or data is insufficient.
Evaluate before release, mask before processing, approve before action.
Governance use cases are built around three controls. Before any model or prompt reaches users it passes the evaluation workbench: a test suite of real and edge cases, a rubric calibrated against human reviewers, and pass criteria for quality, safety, Arabic and English parity, cost and latency. Regressions block the release.
Before any document, transcript or dataset reaches a model it passes the redaction layer: named-entity recognition, pattern rules and OCR detect personal and sensitive fields, which are masked or tokenized, with uncertain cases routed to a reviewer. This is what makes in-perimeter deployment and third-party model use defensible.
Before any agent acts it passes the policy layer: the workflow is bounded by procedures and approved tools, approval gates are explicit, every step is logged, and the agent stops and escalates when the policy or the data does not cover the situation.
Aligned with the region’s AI principles, data laws and supervisors.
The region has moved quickly from AI ambition to AI governance. Saudi Arabia’s data and AI authority publishes AI ethics principles and administers the Personal Data Protection Law; the UAE has issued an AI charter and a federal data protection law; central banks and telecom regulators set model-risk, outsourcing and consumer-protection expectations; and Lebanon’s banking supervisor and data law apply their own obligations. International frameworks such as the NIST AI Risk Management Framework and ISO/IEC 42001 give boards a common language. Our governance use cases are built to produce the evidence those regimes ask for.
Two regional requirements shape the tooling directly. Arabic and English parity is a release criterion, not a nice-to-have, so the evaluation workbench scores both languages separately. And data residency is the default, so redaction, evaluation and agent orchestration all run inside your perimeter, with open-weight models where data cannot leave it.
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.
We already have an AI policy. What does this add?
The means to apply it. A policy says models must be evaluated, personal data protected and consequential actions approved; these use cases are the workbench, the redaction layer and the approval gates that do it, with the records to prove it. They turn a document into controls a regulator can test.
Can agents be trusted to act inside our operational systems?
Only within bounds, which is how we build them. An agent plans and executes a defined workflow using approved procedures and tools, asks for human approval at the gates you set, logs every action and stops when the policy or the data does not cover the case. Autonomy is widened only as the audit trace earns it.
How do you evaluate Arabic quality, not just English?
Separately and explicitly. The workbench carries Arabic and English test suites, including dialect and code-switched cases, scored by rubric against human reviewers in each language. Parity between the two is a release criterion.
Does redaction mean we can use third-party models?
It makes it defensible where your policy allows it: personal and sensitive data is masked or tokenized before any external call, with a review queue for uncertain cases and a log of what left the perimeter. Where policy does not allow it, the same stack runs open-weight models inside your environment.
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.
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