AI and software services
Generative AI development services for business applications
Turn an AI use case into software your team can use and maintain.
A useful generative AI application needs more than a prompt. It needs the right information, a usable interface, connections to business systems and a way to judge the output. Elatre develops applications for knowledge search, document work and assisted business workflows, with the scope built around the task your team needs to complete.
Custom applications · Knowledge retrieval · Business-system integrations
Start with the business need
Define the business task before choosing the model
The right starting point is a workflow, an accountable owner and a measurable standard for useful output.
We examine the inputs, decisions and handoffs in the current process. Some steps need a language model; others are better handled by a search query, a rule or an ordinary software integration. The application should use each where it helps the task.
Retrieval-augmented generation, or RAG, gives a model relevant information from approved sources when it prepares an answer. Fine-tuning changes model behavior using a training process. They solve different problems. We assess the need before recommending either, rather than treating a custom model as the default.
- 01 · SCOPEChoose the taskDefine the user, inputs, output and business decision.
- 02 · DESIGNSelect the approachCompare retrieval, prompting, model choices and conventional software.
- 03 · EVALUATETest useful outputUse representative examples and clear acceptance criteria.
- 04 · INTEGRATEPut it into the workflowConnect the interface, permissions, systems and ongoing review.
Scope and deliverables
What our generative AI development services cover
We can build a new application or add an agreed AI capability to existing software. The deliverables follow the use case.
Use-case and architecture design
Map the workflow and identify where generated output needs review. Compare the integration, retrieval and model options against quality, speed and operating cost.
You receive: An application design and delivery scope.
Knowledge retrieval and RAG
Prepare approved sources, retrieval rules and access controls. Design how the application finds relevant information and shows supporting references where appropriate.
You receive: A retrieval workflow and source-update process.
LLM application development
Build the user experience, instructions, output structure and supporting software. Include clear states for incomplete information, system errors and human review.
You receive: A working application for the agreed task.
Data and system integration
Connect the agreed document stores, APIs, databases or business tools. Define what can be read, what can be changed and which actions need approval.
You receive: Tested data paths and action permissions.
Evaluation and quality controls
Create representative tests for useful output, factual support, access restrictions and failure cases. Review changes against the same test set.
You receive: Evaluation results and acceptance criteria.
Deployment and operation
Prepare the agreed hosting, monitoring, documentation and change process. Separate application behavior from the recurring services it relies on.
You receive: A deployment plan, operating notes and support scope.
Possible applications
Practical applications for knowledge and document work
The following examples illustrate possible scopes. They are not client-result claims.
Find an answer with its supporting material
An employee needs to understand an internal process spread across several approved documents. The application retrieves relevant passages, presents an answer and identifies the sources for review.
- Respect the employee’s document permissions.
- Make source dates and update ownership visible.
- Explain when the available material cannot answer the question.
Prepare a draft your team can review
A team receives a document and needs to extract agreed information into a structured draft. The application checks required fields, highlights missing information and sends the result to the person responsible for approval.
- Define the exact fields and acceptable output.
- Retain a route back to the source material.
- Require approval before changing a business record where the workflow calls for it.
Delivery process
Build around a clear acceptance decision
We agree timing after reviewing the sources, software environment and complexity. Each stage should produce something your team can inspect.
Set the boundary
Choose one task, review sample inputs and agree the output, constraints and quality checks.
Checkpoint: An approved scope and evaluation examples.
Build and compare
Implement the application, retrieval and integrations. Compare alternatives using the same test cases.
Checkpoint: A working release candidate and test results.
Launch and operate
Deploy the agreed scope, document the system and establish how changes and errors will be reviewed.
Checkpoint: A handover and a plan for ongoing improvement.
How we define the engagement
The scope depends on the application, information preparation, integrations, hosting requirements and review process. Your proposal separates development from recurring model, infrastructure and third-party charges. It also identifies source-code ownership, access, documentation and support terms so the handover is clear.
Reporting that supports decisions
Evaluate quality, usability and cost together
An application that produces fluent text can still fail the task. Testing should reflect the decision a person needs to make.
01 · Task quality
Check completeness, factual support and adherence to the required output. Include examples that should produce an incomplete-information response.
02 · Workflow fit
Review how much editing, verification or rework is needed and whether the output reaches the correct person or system.
03 · Operating behavior
Measure response time, system failures and cost per useful task. Recheck these when the model, sources or workflow changes.
| What we review | Useful evidence | What to watch |
|---|---|---|
| Knowledge answer | Relevant answer with accessible supporting sources | Unsupported claims or restricted information |
| Structured draft | Required fields in the agreed format | Missing values, inconsistent extraction or manual rework |
| Connected workflow | Correct output reaches its destination | Unauthorized actions and integration failures |
Working with Elatre
Keep the application connected to your business software
Elatre combines custom software, integration and AI development. The interface, business logic and operating process can be designed together.
Use the software you already have
We review whether the capability belongs inside an existing website, internal tool or business application before recommending a separate platform.
Document decisions your team will inherit
The handover explains the sources, model choices, integrations, evaluation process and support responsibilities. That makes the next change easier to assess.
Explore our custom AI logistics workflow and multi-location software platform projects for related implementation experience. These examples show different project scopes; they are not forecasts for a new generative AI application.
Questions before you start
Generative AI Development Services: practical answers
Scope, integrations, operating responsibilities and the next step.
What can you build with generative AI?
We scope applications around a useful task, such as knowledge retrieval, document drafting or structured information preparation. The project includes the interface, data connections, evaluation and operating process needed for that task.
Do we need to train a model from scratch?
Not automatically. An existing model combined with suitable instructions, retrieval and software may meet the need. We assess quality, data and deployment requirements before recommending a training or fine-tuning project.
What is RAG and when is it useful?
RAG retrieves relevant information from selected sources and supplies it to the model for the current task. It can help an application use changing business knowledge and present supporting material. Source quality, permissions and retrieval performance still need attention.
Can the application work with our existing software?
Yes, integrations can be included. We review available APIs, data formats, authentication and the actions you want to support. The proposal names the systems and any constraints rather than assuming every connection works the same way.
How will we know whether the output is good enough?
We agree acceptance criteria and representative examples before release. The evaluation checks the task itself, including incomplete information and failure cases. Your process owners help define which errors require human review or block release.
How do you handle confidential business information?
Data access, hosting, provider settings and retention are part of the design discussion. We define which sources and users are in scope before connecting them, and document the agreed controls and responsibilities.
Can you improve an AI prototype we already have?
Yes. A review can identify gaps in sources, evaluation, integrations, cost or usability. We can retain useful parts of the prototype and scope the work needed to make it fit the business workflow.
Start with the task
Build an AI application with a clear purpose
Tell us who will use the application, what they need to complete and which systems or documents are involved. We will help define a practical development scope and a way to judge the result.