From use case to production: we build AI agents that read your data, act inside your systems and account for what they do — hosted where your data is allowed to live.
An AI agent is not a chatbot. It combines a language model, memory, retrieval over your documents, your business rules and connections to your applications. Its scope, its permissions and its validation steps are defined **before** deployment — which is what makes it supervisable, measurable, and acceptable to a risk department.
Your systems
Xbit AI agent
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Actions returned
Depending on whether the use case is already identified or still to be found, the first step is not the same.
01
We walk through your processes with your teams, keep the ones whose volume and regularity justify an agent, and rule out the rest. The output is a roadmap costed in effort and lead time — usable even if you do not continue with us.
02
The use case is known: we design the agent, connect it to your sources, define its permissions and validation steps, then roll it out in stages — a narrow scope first, widened on observed results.
Six families of use cases, chosen because they match sectors we already work in. These are not case studies: none describes an identifiable client.
Answering routine requests in French, English and Wolof from validated sources, and handing over to an adviser as soon as a case falls outside the agreed scope.
Reading the documents in a file, checking it is complete, extracting the expected data and preparing the decision — the decision itself remaining human.
Finding the applicable rule across circulars, memos and contracts, and returning the answer with the exact passage it rests on — not a paraphrase.
Reconciling data across several systems, flagging discrepancies and cases to review, and leaving a trace of what was checked and when.
Running authorised tasks inside your applications — create, update, follow up — with human validation wherever the action commits the organisation.
Combining internal data, applying your business rules and proposing the next action, explained well enough that the team can push back on it.
The four words get used interchangeably, yet they describe four levels of autonomy — and therefore four levels of cost, lead time and risk. The right level is the simplest one that produces the expected result, never the most impressive.
Searches and answers within a knowledge base. Changes nothing. The fastest level to put in service, and the only one that writes nothing.
Prepares a summary, a recommendation or a draft, submitted for human validation. The human keeps control of what goes out.
Runs a stable chain of rules and actions across several applications. Predictable, but rigid: it does not adapt to a new case.
Analyses the context, picks an action from those it is authorised to take and executes it with full traceability. The only level that decides — and therefore the one that needs the strictest framing.
Each step handles the business, technical and adoption questions together. Separating the three is what produces successful pilots that never reach production.
01
Choose the process, the users, the accessible data and the success indicators. A written scope is what will later let you say whether it works.
02
Define the architecture, the connections to your systems, the agent’s permissions, its business rules and the points where a human validates.
03
Test under real conditions on a narrow scope, train the teams, then widen in stages rather than opening it to everyone at once.
04
Track handled cases, answer quality and human takeovers, and correct the agent from what is observed — not from what was planned.
In the sectors we work in — banking, microfinance, public finance — this is the chapter that decides, not the demo.
The agent and your data run on infrastructure you are allowed to use: our sovereign cloud, your own datacentre, or yours hosted with us. This is work Xbit already does — not a promise written for the occasion.
The applicable framework is Senegalese first: law no. 2008-12 on personal data protection and the filing obligations with the Commission de protection des données personnelles. GDPR comes on top for organisations that also handle European data.
The agent only sees what its role allows, and only writes where it has been explicitly authorised to write. Permissions are defined at framing and verified at acceptance — not inferred in use.
Sensitive exchanges are logged and reviewable, and any action that commits the organisation goes through a validation step. Your teams can take over at any point without losing the context.
Price depends mostly on how many systems must be connected, the state of the data, the level of autonomy chosen and the compliance requirements. An agent limited to one process is faster to make reliable than a general-purpose system — and that is almost always where to start.
A chatbot answers. An agent acts: it queries your data, picks an action from those it is authorised to take, executes it in your systems and leaves a trace. It is that ability to write that changes the security requirements.
Wherever your regulatory framework requires. This is settled at framing, before any design work, because the choice constrains the architecture — not the other way round.
Yes, provided it exposes an API or an accessible database. It is the first thing examined during the assessment, because it weighs most on the lead time.
It is an explicit goal of our customer-service deployments, and specific work — not a checkbox. The language scope actually covered is settled at framing, use case by use case.
It depends on the scope, and we would rather not announce a lead time we have not yet measured on this kind of project. The schedule is settled at framing, once the first use case has been chosen.
A first conversation is enough to frame the need, the expected value and the next steps. If no use case justifies one, we will say so.
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