Teams that want to use AI but do not yet have a defined use case or a way to measure results.
GalvySoft / Services
Applied artificial intelligence
Practical integration of AI models and services into applications, internal tools and existing workflows.
AI creates value when it improves a concrete task and we can measure whether it actually helps. We integrate existing models and services into applications and workflows while considering context, validation, privacy and cost. We do not treat a generic chatbot as a universal solution.
When it makes sense
Problems we can help you solve
Applications that need search, classification, extraction or assisted generation without indiscriminately sending data.
Processes where a person must review large volumes of text or repetitive information.
AI prototypes that work in a demo but lack control over context, errors, cost or traceability.
Scope
What this service can cover
Final scope is defined by the project needs, constraints and criticality.
- AI API integration
- Assistants for support, search and operations
- Conversational flows with controlled context
- AI-assisted automation
- Structured response processing
- AI-assisted development, documentation, debugging and technical analysis
Capabilities
What we do in practice
Model integration
We connect AI services to existing applications through controlled interfaces.
- Model and provider APIs as appropriate
- Structured inputs and validated outputs
- Context, error, timeout and limit management
Context-aware assistants
We design assistants around concrete information and tasks rather than empty conversation.
- Authorized documentation or knowledge lookup
- Conversational flows with bounded context
- Human escalation when automation should not decide alone
Processing and automation
We use AI as a component within a workflow, not a replacement for all system rules.
- Information classification and extraction
- Assisted summaries and drafts
- Combination with APIs, rules and traditional automation
Evaluation and control
We design around the fact that models can be wrong.
- Deterministic validation where necessary
- Input, output and failure logging subject to privacy needs
- Quality, cost and latency testing with real examples
How we work
From the problem to an operable solution
Before implementing, we clarify the problem, the risks and how we will verify that the solution works.
Use case
We define the task to improve, who will review the output and what makes the integration useful.
Data and risk
We review what information may be sent, what must stay private and where an incorrect response would be problematic.
Measurable prototype
We build the smallest workflow using real examples to check quality, cost and latency before expanding it.
Integration and monitoring
We connect the solution to the real system with enough validation, limits and observability.
Use cases
Situations where this service is a good fit
Internal documentation assistant
Help locate and summarize authorized information with references and clear context boundaries.
Classification or extraction
Turn unstructured text into data that can then be validated and processed by normal rules.
In-application assistance
Generate drafts, explanations or suggestions a person can review before any action is executed.
Delivery
What can remain when the work is complete
- Functional integration or prototype within scope
- Versioned prompts, schemas and configuration where appropriate
- Defined validation and error handling
- Metrics or test cases to evaluate behavior
- Documentation of limits, data usage and operations
Outcome
What we aim to improve
- Use cases defined by value instead of hype
- Better control over data and context
- Outputs that can be validated before action
- Cost and latency visible from the prototype stage
- Integrations that coexist with existing rules and systems
Real scope
What should be clear from the beginning
We do not present model output as guaranteed fact or automate high-impact decisions without appropriate controls. Privacy, data ownership, cost, provider dependency and the need for human review are treated as system requirements.
GalvySoft
Have an AI idea but are not sure whether it solves a real problem or only adds complexity?
We can turn it into a testable use case, evaluate it with real data and decide with evidence whether it is worth integrating.