Build Generative AI that knows what your business needs it to do.
Next Tech Solution designs and develops Generative AI applications that work with your products, knowledge, data and workflows — from intelligent assistants and RAG systems to custom LLM applications and AI-powered business tools.
Turn foundation models into applications that understand the context around the task.
Custom LLM Applications
Generative AI experiences designed around specific product and business requirements.
RAG & Knowledge Systems
Connect AI with relevant company knowledge and information sources.
AI Assistants
Conversational applications for customers, employees and specialised workflows.
Enterprise Integration
Connect AI capabilities with APIs, applications and business processes.
GENERATIVE AI DEVELOPMENT
A model can generate content. A product has to generate value.
Generative AI makes it possible for software to work with language, documents, images and information in ways that traditional rule-based interfaces often cannot.
But connecting an application to a language model is only the beginning. The model still needs to understand what the user is trying to accomplish, what information it is allowed to use, what format the application expects and what should happen when the model is uncertain.
For enterprise applications, the challenge becomes even more practical. The AI may need access to internal knowledge, APIs, customer context, product information or existing business systems. It may also need permissions, evaluation and human review around important actions.
Next Tech Solution approaches Generative AI as a software engineering problem as much as an AI problem — connecting models with the architecture, information and workflows required to make the experience useful.
“A good Generative AI experience isn't impressive because the model can answer almost anything. It's useful because it understands what it should answer here.”
GENERATIVE AI SERVICES
From model capability to a working AI experience.
We help businesses build Generative AI into customer products, internal tools and workflows where natural language and intelligent information processing can create practical value.
Custom LLM Application Development
Build language-model-powered products with application logic, context, structured outputs, integrations and user experiences designed around a specific purpose.
RAG Application Development
Connect Generative AI with relevant organisational knowledge through retrieval workflows designed to provide useful context at the time of the request.
AI Assistants & Copilots
Build conversational experiences that help customers or employees understand information, complete tasks and navigate complex workflows.
Enterprise Knowledge Assistants
Help teams discover and work with information across approved documents, policies, product knowledge and internal resources.
Generative Document Intelligence
Use AI to summarise, extract, classify, compare and transform information contained within business documents.
AI Content Workflows
Design controlled content-generation workflows for drafting, rewriting, summarisation and other repeatable content operations.
Multimodal AI Applications
Explore applications that work across combinations of text, images, documents and other supported forms of information.
Generative AI Integration
Introduce GenAI capabilities into existing web applications, mobile products, internal systems and digital workflows.
AI Evaluation & Improvement
Test model behaviour against representative use cases and improve prompts, retrieval, application logic and workflow design.
BEYOND THE PROMPT
The prompt may start the interaction. The product around it determines what happens next.
Production Generative AI applications usually combine models with context, retrieval, software logic, tools and controls.
A practical Generative AI application
The architecture changes by use case, but these responsibilities commonly work together.
User Request
A user or system initiates the interaction.
Context
The application identifies relevant instructions and information.
Retrieval
Relevant knowledge can be retrieved when the use case requires it.
LLM
The model processes the request with the available context.
Tools
Approved APIs or systems may participate in the workflow.
Response
The application returns or uses the resulting output.
CUSTOM LLM APPLICATIONS
Build around the model instead of making the model carry the entire product.
Language models are powerful components, but application logic should still decide how users interact with them, what context is available and how outputs participate in the wider product.
Prompt Architecture
Structure instructions around the role and purpose of the AI experience.
Context Management
Provide useful application and conversation context without unnecessary information.
Structured Outputs
Design responses that downstream software can understand and use.
Business Logic
Keep deterministic rules in application logic where deterministic behaviour matters.
Application Experience
Design the interface around what users are trying to accomplish with the AI.
RETRIEVAL-AUGMENTED GENERATION
When the answer depends on your knowledge, give the model the right information at the right moment.
RAG applications combine information retrieval with Generative AI, allowing a system to locate relevant content and provide that context to the model for the current request.
Bring Knowledge In
Prepare appropriate documents and knowledge sources for the retrieval workflow.
Make It Discoverable
Structure information so relevant content can be identified efficiently.
Find Useful Context
Retrieve information related to the user's current question or task.
Use the Context
Provide relevant retrieved information to the model when generating the application response.
GENERATIVE AI USE CASES
Put Generative AI where language and information slow people down.
GenAI can support many workflows, but the best use case depends on what users need to understand, create or accomplish.
Customer Support
Help customers find answers and navigate product or service information.
Employee Knowledge
Help internal teams search and understand approved organisational information.
Document Summaries
Turn long documents into useful summaries for relevant workflows.
Information Extraction
Identify relevant details contained within unstructured business content.
Sales Assistance
Support research, preparation and information-heavy sales workflows.
Content Workflows
Assist teams with drafting, rewriting, transformation and summarisation.
Product Copilots
Add contextual AI assistance directly into existing software products.
Research Assistance
Help users work through large amounts of relevant information more efficiently.
CONTENT & DOCUMENT INTELLIGENCE
Your business already has information. Generative AI can help people work with it differently.
Summarisation
Condense large amounts of information into useful, task-specific summaries.
Extraction
Identify specific information within documents and unstructured text.
Classification
Help organise incoming information into useful categories and workflows.
Transformation
Convert information into formats better suited to the next business task.
Comparison
Help users understand meaningful differences across documents or information.
Question Answering
Allow users to interact with relevant information through natural language.
MULTIMODAL GENERATIVE AI
Business information doesn't always arrive as a clean block of text.
Modern AI experiences can work across multiple forms of supported information. That creates opportunities for applications where documents, images and text need to be understood together.
Text
Work with conversations, articles, records and other language-based information.
Documents
Build workflows around information contained in business documents.
Images
Explore applications where visual information contributes to the AI workflow.
Combined Context
Bring multiple supported information types into a unified application experience.
GENERATIVE AI INTEGRATION
Your AI application probably needs to understand more than the conversation happening in front of it.
Useful GenAI experiences often depend on existing software, customer context and business data outside the language model.
Add Generative AI without rebuilding everything around it.
AI capabilities can be introduced into suitable existing products through APIs and application-level integration.
Give the AI access to the right systems — not every system.
Integrations should be designed around the specific information and actions required for the workflow.
EVALUATION & RELIABILITY
“It gave a good answer when we tested it” isn't enough for software people will depend on.
Generative AI is probabilistic. Evaluation helps teams understand how the application behaves across the situations that actually matter.
Output Quality
Evaluate whether responses are useful for the intended application.
Retrieval Quality
Check whether the system is finding context relevant to the request.
Edge Cases
Test how the experience behaves when requests are unclear or unusual.
Failure Behaviour
Design how the product responds when the model cannot confidently complete the task.
HUMAN + AI WORKFLOWS
Automation doesn't have to mean removing judgement from the process.
Some AI outputs can move directly into low-risk workflows. Others may affect customers, important records or business decisions and deserve additional review.
A well-designed GenAI workflow can distinguish between those situations. The system can prepare information, generate a draft, recommend a next step or organise the work while keeping a person responsible for the final decision where appropriate.
The goal is not to remove people from every process. It is to decide where AI can reduce unnecessary effort and where human context remains essential.
OUR DEVELOPMENT PROCESS
Don't begin with “Which model should we use?” Begin with “What should the user be able to do?”
Understand
Define the user, workflow, problem and desired outcome.
Assess
Review information sources, systems, constraints and risks.
Design
Define prompts, retrieval, integrations and product experience.
Build
Develop the application and connect the required components.
Evaluate
Test outputs and behaviour against representative scenarios.
Improve
Refine the experience as usage and requirements evolve.
TECHNICAL CAPABILITIES
Generative AI engineering across models, retrieval, software and infrastructure.
Generative AI
RAG
AI Application
Data
Operations
INDUSTRY APPLICATIONS
The model may be general-purpose. The application shouldn't ignore the industry around it.
Healthcare
Knowledge, document and administrative AI workflows designed around appropriate use cases.
Education
Learning assistance, knowledge tools, content workflows and administrative applications.
Retail & eCommerce
Product assistance, support, catalogue workflows and customer-facing AI experiences.
Financial Services
Information-intensive internal and customer workflows with appropriate controls.
Real Estate
Property information, document workflows, customer assistance and internal knowledge.
Technology & SaaS
Add intelligent features, copilots and knowledge capabilities directly into digital products.
Professional Services
Research, document, knowledge and information-heavy employee workflows.
Travel & Hospitality
Customer assistance, information discovery and operational support experiences.
Enterprise
Internal knowledge, document intelligence and workflow-specific GenAI applications.
WHY NEXT TECH SOLUTION
Generative AI development that thinks about what happens after the model responds.
Use-Case First
Start with what the user or business needs to accomplish.
Product Thinking
Treat the model as part of the application rather than the complete application.
Knowledge Integration
Connect AI with relevant information when generic model knowledge isn't enough.
Existing-System Mindset
Design GenAI capabilities to work with the products and workflows already in place.
Evaluation Mindset
Consider how the AI behaves across representative real-world scenarios.
“Good Generative AI development isn't about finding the cleverest prompt. It's about understanding the user's task, giving the model the right context, connecting it to the right parts of the product and designing what should happen when the answer isn't as simple as the demo suggested.”
Our approach to Generative AI — Next Tech SolutionGENERATIVE AI FAQ
Questions businesses ask before building with Generative AI.
What is Generative AI?
Generative AI refers to AI systems capable of generating or transforming content such as text, images and other supported information based on instructions and context.
What Generative AI services does Next Tech Solution provide?
Services can include custom LLM applications, RAG systems, AI assistants, enterprise knowledge applications, document intelligence, multimodal applications, GenAI integrations and AI evaluation depending on project requirements.
Can you build a custom Generative AI application?
Yes. A GenAI application can be designed around specific users, information sources, workflows, integrations and business requirements.
Can Generative AI work with our company data?
Depending on the architecture and data requirements, AI applications can be designed to retrieve or work with approved company information.
What is RAG?
Retrieval-Augmented Generation is an architecture in which relevant information is retrieved from a knowledge source and supplied as context to a generative model for a particular request.
Can you build an internal company knowledge assistant?
Yes. Knowledge assistants can be designed to help employees search and work with approved internal information using natural language.
Can you build customer-facing AI assistants?
Yes. Generative AI can be incorporated into customer-facing applications for suitable support, product and information workflows.
Can Generative AI be added to our existing application?
Depending on the existing architecture, GenAI capabilities can often be introduced through APIs and integrations without rebuilding the entire product.
Do we need to train our own language model?
Not necessarily. Many applications can use existing foundation models combined with application logic, prompting, retrieval and integrations. The appropriate approach depends on the use case.
What is prompt engineering?
Prompt engineering involves designing instructions and context that help a generative model behave appropriately for a particular application or task.
What is context engineering?
Context engineering focuses on determining what instructions, retrieved information, conversation state and other relevant context should be supplied to the model for a given interaction.
Can GenAI connect with our CRM or other business software?
Where suitable APIs and permissions are available, Generative AI applications can be integrated with existing business systems.
Can humans approve AI-generated work before it continues?
Yes. Human review and approval steps can be incorporated into workflows where additional judgement or accountability is appropriate.
How do you evaluate a Generative AI application?
Evaluation can consider output quality, retrieval relevance, representative user scenarios, edge cases and how the application behaves when the model cannot complete a request appropriately.
How do we start a Generative AI project?
Begin with the workflow or product experience you want to improve. From there, the required information, integrations, model capability and application architecture can be defined.
Have a Generative AI idea? Bring us the problem before worrying about the model.
Talk to Next Tech Solution about custom LLM applications, RAG systems, AI assistants, document intelligence, multimodal experiences or adding Generative AI to an existing product.
