Custom GPT applications
Purpose-built assistants and workflows using suitable large language model APIs.
AI integration and custom GPT apps
AI integration and custom GPT app development
Nezore adds AI where it creates practical value: reducing repetitive work, improving support, searching internal knowledge, summarizing information or adding useful intelligence to existing software.
- Focus
- Useful workflows
- Delivery
- Full stack
- Models
- API based
Structured delivery
Practical AI inside real software
- 01Define the business use case and limits
- 02Choose the model, data and user workflow
- 03Build the UI, backend and AI integration
- 04Test quality, safety, cost and deployment
The exact scope, team structure, timeline and pricing are agreed after reviewing the project requirements.
What we build
AI features connected to business workflows
The strongest AI features have a clear user, data source, expected action and fallback when the model is uncertain.
AI chat assistants
Customer or internal assistants integrated into websites, portals and applications.
AI-powered search
Natural-language search across structured records, documents or approved knowledge sources.
Document summarization
Extract, classify and summarize documents, requests, reports or operational records.
Workflow automation
Use AI to support repetitive triage, drafting, classification and handoff tasks.
AI features inside SaaS
Add generation, suggestions, analysis or assistants to an existing digital product.
Who this is for
For businesses with a specific AI use case
We do not add AI only for marketing. The project should connect to a measurable workflow or user need.
SaaS and product teams
Add useful model-powered features to an existing or new product.
Customer support teams
Improve answer discovery, triage and handoff using approved knowledge.
Operations teams
Reduce repetitive reading, classification, summarization and reporting work.
Agencies and businesses
Build a branded AI feature, assistant or automation for clients or internal use.
Common capabilities
Common AI integration capabilities
The solution may combine a model API with application logic, data retrieval, controls and monitoring.
- ✓OpenAI API integration
- ✓Claude API integration
- ✓Gemini API integration
- ✓Custom GPT workflows
- ✓Prompt and response handling
- ✓Document Q&A
- ✓RAG-style knowledge retrieval
- ✓Conversation history
- ✓User roles and usage limits
- ✓Human review workflows
- ✓Logging and analytics
- ✓Web or mobile UI
Ways to start
Start with the use case, not the model
A short planning phase helps determine whether AI is suitable and what the smallest useful implementation should be.
Assessment
AI use-case and scope review
Define the workflow, data, model and expected result.
- •Use-case validation
- •Data and privacy review
- •Model/API recommendation
- •Implementation estimate
Common starting point
Feature
AI integration module
Build one production-oriented AI capability.
- •Frontend interaction
- •Backend and model API
- •Prompt and workflow logic
- •Testing and handover
Product
AI-powered application
Deliver a broader assistant, tool or SaaS feature.
- •Product and architecture plan
- •Full-stack development
- •Quality and cost controls
- •Deployment and support
Pricing is transparent and agreed before development starts. Scope changes are discussed before extra work proceeds. View our pricing approach.
Our process
A clear path from requirement to working software
Each stage has a practical outcome, clear responsibilities and an agreed checkpoint before the project moves forward.
- Step 1
Discovery
Understand the business problem, users, current workflow, constraints and desired outcome.
- Step 2
Planning
Define scope, priorities, user roles, data, integrations, risks, milestones and acceptance criteria.
- Step 3
UI/UX and architecture
Plan the interface, system structure, APIs, database, security and deployment approach.
- Step 4
Development
Build the agreed frontend, backend, database and integrations in reviewable phases.
- Step 5
Testing
Validate critical workflows, permissions, responsiveness, edge cases and release readiness.
- Step 6
Deployment and support
Launch the product, complete the handover and plan maintenance or future development.
Technology
AI and full-stack technologies
AI APIs are combined with standard software engineering so the feature fits securely into the surrounding product.
OpenAI APIClaude APIGemini APIJavaScriptTypeScriptPythonVue.jsReactNode.jsNestJSREST APIsPostgreSQLMongoDBVector searchDockerCloud deployment
Related experience
Experience with complex, data-driven business systems
Nezore combines hands-on software engineering with planning, technical leadership, testing and delivery coordination.
- Forestry and cadastre management platform covering land records, plots, owners, planning workflows and dashboards
- Maritime and Bill of Lading data platform with APIs, search, large data imports and database-driven pages
- Web, mobile, e-commerce, management, analytics and custom business software delivered for international clients
- Full-stack delivery across frontend, backend, databases, APIs, integrations, testing, deployment and support
FAQ
Questions about AI integration
Can you add AI to an existing website or application?
Yes. We can review the current codebase and add an agreed AI feature through a suitable model API, backend workflow and user interface.
Can the assistant answer from our own documents?
Yes. Depending on the content and privacy requirements, we can build document or knowledge-based retrieval so answers use approved sources.
Do you build custom GPT apps?
Yes. We can build standalone or embedded GPT-style applications with custom workflows, data, roles and business logic.
Can you automate internal tasks with AI?
Yes. Suitable tasks can include classification, summarization, drafting, routing, information extraction and assisted review.
How do you control incorrect AI answers?
We define the expected use case, constrain data and instructions, add source references or human review where appropriate, and test known failure cases.
Who pays the model API usage cost?
Normally the client owns the production AI account and pays usage directly. We can help estimate likely usage and configure limits or monitoring.
Start a conversation
Have a practical AI feature in mind?
Send the user workflow, data source, expected result, current application, privacy needs, budget range and target timeline.