AI for Customer Service, Operations & Knowledge
Service is where generative AI pays back fastest, and where it fails most visibly when it is not grounded. Eurisko builds the customer service, operations and knowledge use cases that enterprises in the region run at scale: agent copilots with cited answers, Arabic and English self-service assistants, call and chat summarization with after-call work, complaint classification and root-cause intelligence, interaction QA, enterprise knowledge assistants built on retrieval-augmented generation, case prioritization and voice-of-customer intelligence, for banks, telecoms, airlines, insurers and government services across the GCC, the wider Middle East and Lebanon.
Every answer is grounded in your approved content and cites its source, every hand-off to a person carries a clean summary, and every model is evaluated separately in Arabic and in English before it meets a customer.
Assistants that resolve, copilots that cite, operations that learn.
The region’s contact centres handle some of the most bilingual, most regulated conversations in the world: a customer who opens in Gulf Arabic, switches to English for a product name and expects the disclosure the regulator requires. First-generation chatbots deflected those conversations; the systems enterprises now need resolve them.
Eurisko builds the full service stack on one grounded foundation: a knowledge layer over your manuals, policies and product documents with access-aware retrieval, a copilot that gives agents cited answers and next actions, a self-service assistant that keeps context across languages, and the analytics layer that turns every transcript into summaries, QA scores, complaint themes and executive insight.
Because the same foundation serves every channel, the answer a customer gets in the app matches the one the agent sees on the desktop and the one the knowledge assistant gives a back-office team.
Eight service and operations use cases, grounded in your own content.
Each one starts from transcripts, documents and workflows you already have, is measured against a human-reviewed sample, and ships inside your contact-centre and knowledge tools.
Customer service copilot
Combines approved knowledge with the customer’s history to help an agent understand intent, answer accurately with citations and recommend the next action, with every answer agent-controlled.
Multilingual self-service assistant
Answers customer questions from approved content in Arabic and English, preserves context across languages and produces a structured hand-off summary when a person is needed.
Call & chat summarization with after-call work
Turns a transcript into a structured note with the issue, resolution, commitments and next actions, filling the CRM fields agents would otherwise type after every call.
Complaint classification & root-cause intelligence
Classifies complaints, detects emerging themes and links them to product, channel or process drivers, with management-ready root-cause briefs generated from the analysis.
Interaction QA & compliance review
Scores calls and chats against your quality rubric and required disclosures, quotes the supporting evidence and surfaces coaching moments, extending review coverage from a sample to every interaction.
Enterprise knowledge assistant (RAG)
Indexes approved manuals, procedures and policies, answers with source citations and access-aware retrieval, and recognises when the knowledge base does not contain an answer.
Case prioritization & resolution recommendation
Ranks the work queue from case text, customer context, sentiment and service-level rules, and recommends the most appropriate resolver or next step under real capacity constraints.
Voice-of-customer executive intelligence
Combines feedback across channels, quantifies themes, detects emerging experience risks and writes a grounded executive narrative of what changed, why it matters and where to look.
Retrieval first, generation second, a person when it matters.
Every assistant and copilot follows the same path. The question is classified for intent and language; personal data is masked before any model sees it; the relevant passages are retrieved from your approved content with the user’s access rights applied; the model drafts an answer constrained to those passages and cites them; and guardrails check policy, safety and tone before anything is shown.
When confidence is low, the content is silent or the request is sensitive, the system says so and hands over to an agent with a structured summary rather than improvising. Those hand-offs, along with agent edits and QA scores, feed the evaluation set, so the assistant improves on your real conversations instead of a generic benchmark.
The same retrieval layer powers summarization, QA and complaint analytics, which is why rolling out one use case makes the next three cheaper.
Arabic-first service, regulator-ready conversations.
Service AI in this region is judged on Arabic first. Gulf, Levantine and Egyptian dialects, Modern Standard Arabic for formal replies, English for product terms and the code-switching in between all appear in one queue, and a model that handles English well may still fail the customer. Eurisko evaluates intent accuracy, grounded-answer rate and language quality separately per language and dialect group, with terminology controlled against your approved glossary.
Conversations are also regulated conversations. Consumer-protection rules from the Saudi Central Bank and the Central Bank of the UAE, telecom regulators’ complaint-handling requirements and government service standards define disclosures, hand-off rules and record-keeping. These are built into the guardrails and the QA rubric, so compliance is evidenced on every interaction, not sampled once a quarter.
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.
How do you stop the assistant from making things up?
By never letting it answer from memory. Every reply is generated from passages retrieved from your approved content and cites them; if nothing relevant is retrieved, the assistant says it does not know and hands over. Grounded-answer rate and correct no-answer behaviour are measured on every release.
Does it handle dialects, or only formal Arabic?
Both. Customers write in Gulf, Levantine and Egyptian dialects and mix in English; the assistant understands them and replies in the register your brand sets, typically Modern Standard Arabic or the dialect you choose. We evaluate each dialect group separately.
Can it work with our existing contact-centre platform and CRM?
Yes. The copilot, summaries and QA scores are delivered through APIs and embedded screens into the platforms you run today, and the knowledge layer indexes the content systems you already maintain. We have integrated with core banking, CRM and telephony platforms across the region.
What about personal data in transcripts?
Personal and financial data is detected and masked before transcripts reach any model, retention follows your policy, and the whole stack runs inside your perimeter or private cloud. Masked or synthetic transcripts are used until production.
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.
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Next-best action, churn early warning, segmentation and personalized journeys.
AI for Enterprise Governance & Responsible AI
Policy assistants, evaluation workbenches, PII redaction, plain language and governed agents.
AI for Product, Data & Developer Teams
Natural-language BI, synthetic data, data quality, API copilots and test generation.
Artificial Intelligence & Data Analytics
Generative AI, chatbots, computer vision, NLP and predictive systems.
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