Embed text classification or summarization into an existing application
Reduce repetitive work and make the process more consistent.
Add language-model capabilities to an existing product or workflow.
Connect LLM capabilities to internal applications, data sources and APIs. Add language-model capabilities to an existing product or workflow. The scope depends on your current systems, the quality of available data and the outcome you need to achieve.
Instead of starting with a specific tool, the first step is understanding your current process, the people involved and the systems that need to work together.
Product and engineering teams embedding AI capabilities into existing software
When does it make sense?Best fit when the application has a clear integration point, data controls and test cases.
Users copy content into separate AI tools because the core application lacks summarization, extraction or classification.
Your existing software contains valuable text and document data, but users still need to copy information into external AI tools. You want AI functionality inside the product they already use.
Explore common challenges this service is designed to address.
Reduce repetitive work and make the process more consistent.
Connect information and workflows across relevant teams.
Improve visibility, controls and the quality of day-to-day execution.
A case-management application sends selected text to a language-model API, validates the structured response and displays it inside the existing workflow, with audit logging and access controls.
The exact solution depends on your existing systems, data and operational requirements.
Embed text classification or summarization into an existing application.
Best fit when the application has a clear integration point, data controls and test cases.
API latency; output validity; cost per request; review rate.
Different AI projects need different skills. perivaro reviews your business challenge, current systems and desired outcome before identifying the relevant type of independent specialist.
Product and engineering teams embedding AI capabilities into existing software
What to measureAPI latency; output validity; cost per request; review rate
How matching worksShare your requirements in plain language. A proposed specialist is only confirmed after an individual review of suitability and availability.
Final deliverables, timeline and pricing are agreed with the proposed specialist after a review of your requirements.
Best fit when the application has a clear integration point, data controls and test cases. If you are unsure, describe the underlying problem rather than choosing a technology.
Relevant measures include API latency; output validity; cost per request; review rate. The exact baseline and success criteria should be agreed before work starts.
The indicative range shown for this category is €10k–€50k+. Pricing is not a fixed offer; it depends on the project scope, integrations, data readiness and delivery requirements.
Yes. You can describe your challenge in plain language. We review the request and help identify the relevant specialist expertise before a proposal is prepared.
Most projects are delivered by independent specialists or implementation partners. An individual specialist is only proposed after availability and fit have been reviewed.
Use the project discovery form to describe your goals and provide contact information. A tailored proposal requires a manual project review.
A short description of the current process, relevant tools, the people involved and the outcome you want is enough to begin. Screenshots, workflow diagrams or sample documents can help, but avoid submitting sensitive information through the initial form.
Compatibility depends on your software, available APIs, data permissions and security requirements. Integration feasibility is reviewed during scoping, rather than assumed in advance.
Define a baseline and agree on relevant indicators such as processing time, manual effort, error rates, response quality or AI visibility. Not every benefit is guaranteed, and results need to be measured after implementation.
Timing depends on the scope, data quality, approvals and integrations. A limited pilot may be substantially quicker than a multi-system rollout. The delivery partner provides a timeline after assessing the requirements.
Ownership of deliverables, intellectual property, software licenses, hosting and support should be agreed in writing with the delivery partner before work begins. perivaro does not automatically become the implementation provider.
Describe your challenge. We'll help clarify the scope and review suitable expertise.
Start project discovery →No commitment to purchase. Your requirements are reviewed before a specialist is proposed.