Technology & digital transformation

AI and Automation: practical assistants with clear human oversight

We apply AI where it saves real time: assistants that support staff, document analysis and data extraction, search across an organisation's internal knowledge, chatbots for recurring questions and automation of routine steps. Each solution is designed so that important decisions stay with a responsible person, data is protected and the system's limits are clearly understood.

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What we offer

We start with a simple question: which task consumes your team's time and repeats in a way that can be described? It may be reading hundreds of requests to classify them, searching scattered manuals for an answer, answering the same questions daily or extracting data from invoices and scanned documents. We assess each case for expected benefit, risk and data quality, and recommend AI only where it fits; sometimes conventional rule-based automation is simpler and more reliable.

We build solutions such as an internal assistant that answers staff questions from the organisation's documents and cites the source, document analysis that extracts fields, classifies and summarises documents, an Arabic and English chatbot for your website or messaging channels that hands complex cases to a person, and automation flows connecting email, forms and systems. We choose models and the operating mode, cloud-based or inside a private environment, according to data sensitivity and your requirements.

We are open about the limits of AI: it can make mistakes, it can produce an answer that sounds convincing but is wrong, and its quality depends on the quality of the data. We therefore design human review for any output that affects people's rights or official, financial or medical decisions, measure performance on real examples before launch, and define what the system is not allowed to do. We do not use your data for purposes outside the project, and we apply access controls and usage logging.

Who it is for

Public institutionsLarge volumes of requests, correspondence and documents that need faster sorting and search while decisions stay with the responsible officer.
HospitalsRepetitive administrative and documentation tasks that can be eased, under strict controls because data is sensitive and clinical decisions belong to doctors.
Companies and customer serviceRecurring customer questions and heavy correspondence where replies and routing can be supported.
Administration and finance teamsInvoices and documents whose data is typed manually and can be extracted automatically with verification.
Organisations with scattered knowledgeManuals, instructions and reports spread around that new staff struggle to find.

Problems we address

Staff spend hours searching for information in scattered documents and manuals.
Manual entry of data from invoices, requests and scanned forms, with recurring errors.
The same customer or visitor questions come in every day and take up team time.
Incoming correspondence and requests are classified and routed by hand, causing delays.
Earlier use of general AI tools without controls raised concerns about confidentiality and accuracy.
No clear benchmark to judge whether an AI tool actually works well.

Tasks and deliverables

Use case assessment: A prioritised list of candidate tasks with estimated benefit, risk and data quality, and a clear recommendation for each.
Internal knowledge assistant: An assistant that answers staff questions from approved organisational documents, cites the source and respects each user's permissions.
Document analysis and data extraction: Extracting fields, classifying and summarising documents, with a review screen where staff correct results before approval.
Chatbot: An Arabic and English chatbot for recurring questions on your website or messaging channels, handing complex cases to a staff member.
Automation flows: Automation of routine steps between email, forms and systems, with alerts for exceptions.
Review and control framework: Written rules on what is reviewed by people, what the system may not do, how usage is logged and how errors are handled.
Performance evaluation report: Results of testing the solution on real examples before launch, including where it failed and how that is handled.

Who does what

What IAG does

Analyse tasks and assess whether AI or conventional automation fits better.
Select models, tools and an operating mode suited to data sensitivity.
Develop the solution and connect it to systems and approved documents.
Design human review, access controls and usage logging.
Test performance on real examples and document results and limits.
Train users and monitor performance after launch.

What specialists, partners and authorities do

The client defines the tasks and approved source documents and appoints who reviews outputs and holds the final decision.
Legal requirements for processing personal and sensitive data are defined by the client's legal adviser or the competent authority.
Use of models from external providers is subject to their terms and policies, which we review with the client before adoption.

Practical example

Illustrative scenario

An assistant answering staff questions about internal policies

An organisation has many procedure manuals and internal instructions, and staff call HR daily with questions whose answers are already in those documents.

Collect the approved documents and name an owner responsible for updating each.
Prepare a set of real questions with correct answers to test the assistant.
Build the assistant to answer only from the documents, cite the source and say so when it finds no answer.
Test, review errors and improve until the agreed level is reached.
Pilot with a group of staff, with a button to report wrong answers.
Review unanswered questions regularly and update the documents.

This is an illustrative scenario to explain our approach, not a client reference.

How we work together

Discover use cases
We collect candidate tasks and assess them with you for benefit, risk and data availability.
Proof of concept
We build a small version for one case and test it on real examples before any larger investment.
Design and controls
We define human review, permissions, operating mode and what the system may not do.
Development and integration
We develop the solution, connect it to systems and documents and build user and review interfaces.
Pilot launch
We launch to a limited group, collect feedback and measure performance.
Monitoring and improvement
We check quality regularly and update the solution as documents and needs change.

Intended results

Staff time saved on searching, data entry and repetitive replies.
Easier access to internal knowledge, with sources cited.
Important decisions stay with a responsible person, supported by automated preparation.
A clear, test-based understanding of what the solution does well and what it does not.
Documented controls for data protection and usage tracking.

What we need from you

A description of the target tasks, their volume and how they are done today.
The approved source documents or data, after deciding what may be used.
Real or anonymised examples for testing, exchanged through agreed secure channels.
Named reviewers for outputs and the person holding the final decision.
Data protection and confidentiality requirements and permitted hosting location.
User feedback during the pilot.

Basis for cost and timeline

Cost depends on the number and complexity of use cases, the volume of documents and data, the operating mode (cloud service or private environment), required integrations and the level of review and testing. There may be ongoing running costs linked to usage volume or servers. We recommend starting with a fixed-price, fixed-duration proof of concept for one case, then pricing full development after seeing the results, with a contract for monitoring and improvement.

Frequently asked questions

Can AI answers be relied on without review?

Not for important decisions. AI can make mistakes or state a wrong answer confidently. We therefore design human review for any output that affects people's rights or official, financial or medical decisions.

Will our data go to external parties?

That depends on the chosen operating mode. For sensitive data, operation inside a private environment is possible. In every case we explain the data path and provider terms before you decide.

Does it work well in Arabic?

Current models generally handle Arabic well, but quality varies by task, dialect and document type. That is why we test on your real examples before launch.

How do we know the solution works as intended?

We agree a test set and acceptance criteria in advance, deliver a report on results and errors, and keep measuring after launch.

Is AI suitable for every task?

No. Some tasks are solved more reliably and cheaply with simple rule-based automation, and we will tell you so during the assessment.

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