Practical AI solutions built around your business
We identify practical AI use cases that can improve your operations. From content assistance and document analysis to internal assistants and business knowledge retrieval, we help turn promising ideas into useful solutions.
AI integration means connecting artificial intelligence models (language models, information extraction, classification) to your tools and processes so they can assist with or automate specific tasks: drafting, summarising, sorting, answering and finding information. It is designed for SMEs, sales, marketing and operations teams, and leaders who want to move from occasional experiments to well-governed, everyday use.
We do more than plug in a model. We start from your goals and constraints, select the use cases that genuinely matter, assess quality, cost and risk, and then integrate the solution into the tools your teams already use. In Tunisia, that often means handling customers and documents in French, Arabic and English.
The aim is straightforward: measurable gains, a solution your teams understand, and clear governance over the data involved.
What we fix
Experiments that go nowhere
Teams try generative AI tools on their own, without shared guidelines, measurement or integration into existing processes.
Information that is hard to find
Procedures, offers, contracts and standard answers are scattered across emails, shared folders and PDFs. Finding the right one takes time.
Repetitive, low-value tasks
Summarising conversations, sorting requests, extracting data from documents and writing first drafts keeps skilled people busy.
Legitimate data concerns
You are unsure which data can be sent to an AI service, or how to govern usage to limit risk.
Hard to prioritise
There is no shortage of ideas, but it is difficult to tell which ones will deliver a concrete result for the effort involved.
How we work
- Website form received
- Duplicate check in the CRM
- Contact created, source and campaign added
- Lead qualified by a salesperson
- Follow-up email scheduled
Identify use cases
Workshops with your teams to spot where AI can help, then prioritisation by business value, feasibility and risk.
Prototype on your data
A focused prototype tested on real examples, anonymised where needed, against quality criteria agreed in advance.
Assess quality, cost and risk
We measure answer relevance, estimate running costs and identify where human review must remain in place.
Integrate with your tools
Connection to your CRM, inbox, intranet or workflows via API, with logging, error handling and access control.
Support adoption
User training, documentation, usage guidelines and KPI tracking so the solution can be refined after launch.
Use cases
Internal knowledge assistant
An assistant that answers staff questions from your procedures and internal documents, citing its sources (RAG approach).
Request triage
Incoming emails or form submissions classified by topic, urgency or language, then routed to the right person.
Document data extraction
Reading purchase orders, invoices or forms to extract the relevant fields and pass them to your tools.
Writing assistance
First drafts of customer replies, product descriptions or meeting notes, reviewed and approved by your team.
Conversation summaries
Summaries of meetings, calls or long email threads, with action items and open questions.
Customer-facing assistant
An assistant on your website or messaging channels that answers common questions and hands over to a person when needed.
Deliverables
Prerequisites and limits
Prerequisites
- One or two priority use cases and an available business contact.
- Representative examples (documents, requests, expected answers) to test the solution.
- Access to the tools to be connected, or a technical contact who can provide it.
Limitations
- AI models can produce inaccurate answers. For sensitive, legal, financial or binding content, human review is built in.
- A document assistant is only as good as the quality and freshness of the documents it relies on.
- AI services carry a recurring, volume-based usage cost. We estimate it before integration.
- We do not recommend AI when a simple rule or conventional automation will do the job.
Frequently asked questions
How much does an AI integration cost?
Cost depends on the number of use cases, the tools to connect, data volume and the level of review required. AI services also carry a usage cost that varies with volume. After a consultation, we provide a detailed quote and an estimate of recurring costs.
How long does a first use case take?
A focused prototype can often be tested within a few weeks. Full integration depends on the availability of your data, tools and people. We set a realistic schedule after the assessment.
Is our data sent to external services?
That depends on the chosen solution. We identify the data involved, favour offerings that do not train on your data, minimise what is sent and can anonymise certain content. Every choice is documented and approved with you.
Which tools and models do you use?
We are not tied to any vendor. We select models and services based on results on your use cases, cost, content language and your data requirements.
Will our teams actually use it?
Adoption is planned from day one: future users take part in workshops and testing, the solution fits into their usual tools, and we provide training, documentation and post-launch adjustments.
Does AI work with Arabic content?
Recent models handle Arabic, French and English, with quality that varies by task and dialect. We always test on your own examples before approving a use case.
Have a usecase in mind?
Tell us about it in a consultation. Together we will assess feasibility, value and next steps.