Services / Product engineering
AI Integration Services
Add AI to a defined business task rather than building a demo without an operational purpose. We connect model APIs to the information, permissions and review steps your application already needs.
01 / The business problem
When this service helps
For teams with an existing product or workflow and a clear use for document understanding, knowledge search or assisted responses. A useful starting point is a small set of representative inputs and examples of what an acceptable answer should contain.
- Staff repeatedly searching documents or extracting the same fields
- Support replies that need business context rather than generic answers
- AI prototypes that ignore access boundaries, uncertain results or operating cost
02 / Scope and delivery
What we can deliver
- A scoped AI use case and evaluation examples
- LLM API integration and structured output validation
- Knowledge retrieval or document processing where appropriate
- Review, fallback and failure behavior inside the existing product
- Usage and cost considerations for the proposed workflow
Technical approach
We connect LLM APIs to backend services and, when the task calls for it, retrieval over business knowledge. NestJS, PostgreSQL and vector search can support the application boundary. Model output is treated as untrusted input: permissions, validation and consequential actions remain in application code. We agree review rules and measure results on representative examples before widening access.
03 / Relevant experience
Related product work
Recruity demonstrates AI-assisted candidate analysis; MsoPak includes AI language practice. Foljent is an in-development reference for replies grounded in business knowledge. None of these examples establish a guaranteed accuracy rate or an autonomous decision-making service.
04 / The process
How the work takes shape
Define a useful result
Select one task, representative data and the limits of acceptable output.
Assess data and access
Review what can be sent to a provider and which records each user may access.
Integrate and evaluate
Connect the model to the workflow and test errors, uncertainty and review steps.
Operate with feedback
Release gradually and review quality, latency and usage costs.
05 / Before we start
Common project questions
Can you add an AI chatbot to an existing application?
Yes, where there is a useful support or knowledge task. We scope the information it may use, how it identifies uncertainty and when it should hand off to a person. Access to private records must follow the application’s permissions.
Will AI always return the right result?
No. We design around uncertain and incorrect output using evaluation examples, validation and human review. Tasks that can be solved with ordinary rules or APIs may be better served without a model.
06 / Next steps
Tell us about the problem
Share the workflow or product you want to improve, the systems already involved, and any timing or budget constraints. We can start by understanding the scope and deciding on a practical next step. Based in Kosovo, working with businesses locally and internationally.