An internal knowledge assistant
An organization with a large library of policies and procedures wants an assistant that answers staff accurately and cites the exact document and section, respecting each department’s permissions.
CIntelligence & Data
AI & Intelligent SystemsAI that works inside your operations, not beside them.
We engineer AI systems that understand your data, your language and your business context. They integrate with the systems you already run and are governed by clear controls on quality, security and cost.
- Code
- C
- Practice
- Intelligence & Data
- Scope areas
- 11
- Delivery phases
- 6
Overview
LLMs, agents, RAG and computer vision grounded in your data, with clear controls on quality, security and cost.
AI has moved from lab experiment to everyday workplace tool remarkably quickly, yet the gap between an impressive demo and a system you can rely on in production remains wide. A language model on its own knows nothing of your policies, your customer data or your employees’ permissions. Worse, it can answer with complete confidence about things it does not know. Closing that gap is primarily an engineering problem, not a matter of picking a model.
AMA GROUP starts from the problem, not the technology: where does your team lose time, and which decisions need faster, better-grounded data? Then we build what fits: retrieval-augmented generation grounded in your documents, agents that act across your systems with scoped permissions, or machine learning models trained on your historical data. Arabic receives particular attention: dialectal variety, rich morphology, undiacritized script, text that mixes Arabic and English, and scanned Arabic documents.
We treat governance as an integral part of the engineering: systematic evaluation of output quality, cost and usage tracking, control over where data is processed according to its classification and the requirements of Saudi Arabia’s Personal Data Protection Law (PDPL), and a human in the loop wherever the consequences matter. With interest in AI growing across the Kingdom as part of the Vision 2030 journey, building these capabilities on sound foundations is a genuine competitive advantage, not just a way of keeping pace.
Scope of work
What this service can cover. Most engagements combine several of these areas.
LLM integration
Connecting models such as Claude, GPT, Gemini and open-weight alternatives to your applications, choosing per task on quality, cost and latency, behind a unified layer that makes switching providers straightforward.
AI agents
Agents that carry out multi-step tasks across your tools (querying systems, opening tickets, updating records) with scoped permissions, a full audit trail and human approval checkpoints.
Workflow automation
Automating repetitive processes that need language understanding or light judgment, such as triaging requests, routing inbound email, or extracting data and entering it into systems.
Intelligent assistants
Customer- or employee-facing assistants on web, mobile or WhatsApp that answer only from approved sources and hand over to a person, with a clear summary, when needed.
Machine learning
Forecasting, classification and anomaly-detection models trained on your data, for example demand prediction, churn propensity and unusual-transaction detection.
Natural language processing
Arabic and English text classification, summarization, entity extraction and sentiment analysis that accounts for Gulf dialects, undiacritized script, and normalization of hamza and taa marbuta forms for search.
Computer vision
Image and video analysis for quality inspection, object counting, site-safety monitoring and reading plates or meters, processed in the cloud or on edge devices.
Intelligent document processing
Extracting data from invoices, contracts, ID documents and scanned forms, including handwritten or low-quality Arabic documents, with human review of low-confidence results.
Enterprise AI
An internal AI platform (a unified model gateway, permission and cost controls, usage policies and continuous evaluation) so teams across the organization can build safely.
Retrieval-augmented generation (RAG)
Systems that answer from your documents and knowledge bases with source citations, using hybrid semantic and keyword search while honoring each user’s access rights.
AI-powered analytics
Querying your data in natural language, summarizing KPIs and surfacing trends, always showing the executed query so every figure remains verifiable.
Typical use cases
Representative scenarios where organizations need this service: examples of the need, not past client engagements.
Automated invoice processing
A finance department receiving invoices in many formats wants line items extracted and matched against purchase orders and goods receipts, with only exceptions routed for review.
Always-on customer service
An assistant on WhatsApp and the website that understands queries in Modern Standard Arabic and dialect, answers from approved content, and opens a ticket with a conversation summary when handing over to a human agent.
Demand forecasting for inventory
A distributor wants item- and branch-level demand estimates that account for seasonal peaks such as Ramadan, Eid and back-to-school, to help reduce both overstock and stockouts.
Visual inspection on the production line
A manufacturer wants cameras to flag product defects, alert the operator immediately and log each case for later root-cause analysis.
A contract review agent
A legal team wants incoming contracts compared against its approved templates, with deviations and risk areas highlighted, while the final decision stays with the legal professional.
Delivery approach
Clear phases with defined outputs, so you can follow progress and make decisions at every stage.
- Step 1
Use-case & value framing
We define the task, the user and a measurable success criterion, and first confirm that AI is the right tool for the problem at all.
- Step 2
Data readiness assessment
We review data sources, quality, classification and access rights, and decide what may be sent to external models and what must stay inside your environment.
- Step 3
An evaluation set before the build
With your subject-matter experts we assemble test questions and cases with reference answers: an objective yardstick for every later improvement or change.
- Step 4
Scoped proof of value
We release a limited-scope version to real users and measure accuracy, latency and cost per request before any decision to scale.
- Step 5
Production hardening
We add guardrails against prompt injection and data leakage, observability and logging, fallback paths for failures, and integration with identity systems.
- Step 6
Operate & improve
We monitor quality, cost and data drift, and re-run evaluations whenever the model or the content it relies on changes.
Technical capabilities
Tools and platforms we select from, based on your context, constraints and existing stack.
Language models
- Claude
- GPT
- Gemini
- Llama
- Mistral
- Qwen
- ALLaM
- Jais
Orchestration & retrieval
- LangGraph
- LangChain
- LlamaIndex
- Model Context Protocol
- pgvector
- Qdrant
- OpenSearch
Machine learning
- Python
- PyTorch
- TensorFlow
- scikit-learn
- XGBoost
- Hugging Face Transformers
- MLflow
Vision & documents
- OpenCV
- YOLO
- Tesseract OCR
- Azure AI Document Intelligence
- Amazon Textract
- ONNX Runtime
Serving & evaluation
- vLLM
- Ollama
- FastAPI
- Langfuse
- Ragas
- Docker
- Kubernetes
Technologies listed are capabilities and ecosystems we work with, not partnerships, certifications or endorsements.
Deliverables
What you receive during and at the end of the engagement. The exact set depends on the agreed scope.
- Use-case and data-readiness assessment report
- Solution architecture with a data-flow map and privacy controls
- Evaluation dataset with reference answers and benchmark results
- A production system with its complete source code
- Documented guardrails and acceptable-use policies
- Monitoring dashboard for quality, cost and usage
- Operations runbook and training for the owning team
Only with your approval, and only in line with your data classification. There are several options: enterprise offerings whose terms exclude training on your data, cloud-hosted models in a region you choose, or open-weight models running entirely on your own infrastructure. We select the option with your compliance team in light of the PDPL and any sector-specific requirements.
Related industries
Sectors where this service is commonly needed.
Financial Services & Fintech
Secure digital banking, payments and analytics platforms engineered for trust and scale.
Healthcare
Patient-centered digital health platforms, from booking and telehealth to interoperable clinical data.
Government & Public Sector
Citizen-centered digital services, interoperable platforms and Arabic-first experiences for public entities.
Retail & E-commerce
Unified commerce platforms that connect online stores, apps, branches and fulfillment in one experience.
Logistics & Supply Chain
Fleet, warehouse and delivery platforms that give real-time visibility from first mile to last.
Related concept projects
Design explorations showing how this service could come together in a real product, not delivered client work.
Concept Project: A design concept that shows how we think, not a delivered client project.Internal Operations in Financial Services
Sanad: An Arabic-First Enterprise Assistant
An AI assistant concept that answers a financial institution’s staff from its own policies and procedures, citing its sources and respecting permissions.
View conceptIllustrative Solution: A model of a possible solution, not completed client work.Finance & Performance Management
Marsad: A Financial Analytics Workspace
An illustrative analytics platform that gives the finance office one trusted number, from the executive dashboard down to the original journal line.
View conceptConcept Project: A design concept that shows how we think, not a delivered client project.Transport & Logistics
Masar: Fleet & Last-Mile Orchestration
A logistics platform concept that connects planning, dispatch, drivers and customers in one live picture, from the warehouse to the recipient’s door.
View conceptConcept Project: A design concept that shows how we think, not a delivered client project.Fintech
Hasala: A Digital Wallet & Money App
A consumer fintech concept combining account, card, instant payments and savings goals, built on a ledger with no tolerance for error.
View conceptRelated services
Services that often work alongside this one.
Data Engineering & BI
Reliable pipelines, governed warehouses, and executive dashboards that turn scattered data into decisions.
API & Integration
REST and GraphQL APIs, payments, webhooks, and event-driven integrations that make separate systems work as one.
Cloud & DevOps
Secure, scalable cloud foundations, automated delivery pipelines and full observability on AWS, Azure and Google Cloud.
Security Engineering
Security built into architecture, code, and delivery pipelines from day one, not bolted on after launch.
Tell us what you are building.
Share your goals for AI & Intelligent Systems. We will come back with a clear approach, a proposed team shape and practical next steps.