What AI features can you add to your application?
We extend your company’s business systems and applications where you have the source code and the right to make changes. We specialise in .NET and Microsoft SQL Server. If you use a packaged ERP, we first check its add-ons and integration options.
Employees can move more quickly from receiving a case to assessing it and replying to the customer. Features that can help include:
- Filling in forms from documents. Data from a message, PDF or scan goes into a draft form for an employee to check.
- Organising requests. The system proposes a category and routes the case to the appropriate queue according to agreed rules.
- Draft replies. The case history and selected data are used to prepare a proposed message for editing and approval.
- Correspondence summaries. An employee receives a summary of a long history, with access to the sources.
- Document search. A user asks a question and receives an answer based on material available to them.
We choose one feature to start with and test it using real examples. We compare processing time with the current way of working, including checks and corrections. This helps you assess the feature’s usefulness before a wider rollout.
When is it worth adding AI to an existing system?
This is a good option when the application supports the process, holds the required data and is where your team works every day. An employee can run the new feature in the current form and check its result there.
First, we check whether the task needs AI. If data has a fixed format or a decision can be described by a simple rule, an ordinary software feature may be easier to maintain.
If your team mainly needs to ask questions about data from several systems, see an AI assistant using company data. When the current system does not support the core process, we first need to assess extending it.
How do we implement an AI feature in an application?
- We understand the task and system. We establish the result you want, which data is available and how the application can be extended. We check the code or interfaces and the deployment process.
- We define the output and how to check it. We specify what AI should prepare, what an employee approves and which mistakes matter. We also collect difficult and incomplete examples.
- We test the model and cost. We compare quality, response time and correction effort. We agree where data is processed.
- We add the feature to the system. We introduce format checks, permission and cost controls, and error handling.
- We launch it in stages. First for selected users or case types. We also assess results on examples not used when preparing the solution.
If the scope is clear after our conversation, we prepare a proposal. If the application or feasibility needs examining first, we suggest a separately priced review or test.
Example scenario: service requests
A company receives email service requests in its own application. An employee reads the message, identifies the customer and equipment, and selects a case category.
After AI is added, they see proposed field values and a draft reply in the same form. They can focus on assessing the request and helping the customer. They check and approve the result; incomplete or ambiguous requests continue to be handled manually.
AI integration with .NET and Microsoft SQL Server
We specialise in .NET and C# applications with Microsoft SQL Server (MSSQL) databases. We have over 20 years of experience designing, programming and implementing business systems. Read more about DO IT GLOBAL’s experience.
We can add an AI feature as an application module or a separate service that exchanges data with it. The application continues to handle permissions, rules and data storage; the model prepares the output for a defined task.
When choosing the technology, we allow for a future change of model or provider. Each such change requires quality to be checked again on your company’s tasks.
AI in the cloud or on a company server?
The feature can use an AI cloud service or a model running locally. The choice depends on the task, data requirements, answer quality and cost of use.
For a cloud service, we check its conditions: data use for training, storage location and duration, and access. If information needs to stay at your company, we assess a local or offline option. We select hardware based on task tests and the expected workload.
How do we control results and keep work running?
We tailor controls to the task and the consequences of a possible mistake:
- Checking the result. The system verifies required fields and rules, while an employee approves the output where their judgement is needed.
- Working without AI. We prepare and test a way to continue work when the model or connection is unavailable.
- Cost control. Limits on data size and call volume help keep spending within the agreed budget.
- Investigating errors. We log the necessary events, with defined access and retention periods.
Quality is assessed through test results and data checks. A model’s declaration that it is confident in an answer is not enough.
How much does adding AI to an application cost?
The price depends on the feature, system condition, available interfaces and the required output checks. Before work starts, we agree the scope, price and acceptance criteria.
We first review a system built by another supplier to assess the possibility of changes. Read more about taking over application development and how we work.
After launch, you can ask us to provide maintenance and development. If you are an IT company developing an application for a client, see our partnership options.
Content updated: