Generative AI development services
Generative AI Development and LLM Solutions
for Your Products and Teams
SensViz builds generative AI applications that work with your data, products, and business systems. We develop RAG search, knowledge assistants, copilots, multimodal features, and document or content tools, with clear tests, approved data sources, human review where needed, and operating costs considered before launch.
What this service covers
Turn a capable model into useful business software
Generative AI development is the work of designing and building software that creates or transforms content with foundation models. Large language models are a subset focused on language and related tasks. In practice, the work includes model selection, context and retrieval, product design, integration, evaluation, access controls, and ongoing operation.
Modern applications can work with text, images, audio, and documents. SensViz chooses the smallest practical architecture that can meet the use case rather than assuming the newest or largest model is automatically the right one.
What we build
Generative AI and LLM solutions built around a clear task
The right solution may be a focused feature inside existing software or a complete application with its own interface, data layer, permissions, and monitoring. The design starts with what the user needs to do and how the result will be checked.
RAG and knowledge applications
Connect approved documents, databases, and knowledge sources to an LLM so users can ask questions and receive answers based on retrieved material. Depending on the use case, the system can include metadata filters, hybrid search, reranking, and source references.
AI assistants and copilots
Embed an assistant inside a product or internal tool to help users find information, compare options, prepare work, or complete a defined task while keeping the person in control.
Structured content and document generation
Generate drafts, summaries, reports, responses, and structured records from approved inputs, templates, business rules, and review steps.
LLM features for existing software
Add natural-language search, explanation, classification, summarisation, or content generation to an existing web, mobile, or SaaS product through an appropriate application layer.
Multimodal AI applications
Build features that work with combinations of text, images, audio, and documents when the selected models, data, and user experience support the task.
Model integration and adaptation
Select and integrate commercial or open-weight models, then improve performance through prompt and context design, retrieval, or fine-tuning when evaluation shows it is justified.
Where to start
Start with a task you can test
A strong first use case has a clear user, known inputs, examples of acceptable output, and a review path when the result is uncertain. Start with one useful task before expanding into a broad AI programme.
A strong first use case has
- A clear user and a task worth improving
- Known inputs and approved sources
- Examples of what acceptable output looks like
- A review path when the result is uncertain
Internal knowledge assistant
Help authorised employees search policies, procedures, manuals, project material, or product documentation in natural language.Customer support copilot
Retrieve relevant support material and prepare a response or next-step suggestion for an agent to review.Document summary and review
Summarise long files, compare selected content, identify required fields, and present findings for human review.Proposal, report, and response drafting
Prepare consistent first drafts from approved source material, templates, instructions, and structured inputs.Natural-language product interface
Let users search, ask, explain, or create inside an existing software product without learning a complex query language.Multimodal document or media tool
Combine text with images, audio, or documents for use cases where a single input type is not enough.Beyond the model
The model is one part of the product
A strong model can still produce a poor product if the context, interface, permissions, or tests are weak. SensViz designs the application layer around the task, the data, the users, and the cost of a wrong or unusable result.
Model selection and routing
Compare models against the task, quality bar, latency, context, cost, deployment, and data requirements. Use more than one model only when the added complexity has a clear benefit.
Context and retrieval
Prepare sources, metadata, permissions, retrieval, reranking, and context so the model receives relevant information instead of an uncontrolled volume of text.
Prompts and structured outputs
Version the instructions and examples, define the expected output, and validate structured responses before other software uses them.
Access and data boundaries
Enforce identity, permissions, and data access in the application and connected systems. A system prompt is not a security boundary and should not contain secrets.
Evaluations and human review
Test representative and difficult cases against defined criteria, record failure patterns, and require a person to review outputs where the risk or uncertainty calls for it.
Monitoring and change control
Track quality signals, retrieval behaviour, failures, refusals, latency, usage, and cost. Re-test the application when models, prompts, sources, or user behaviour change.
How we deliver
From a testable use case to a monitored application
Use case and success criteria
Define the user, task, expected output, current baseline, acceptable failure, review path, and business reason for building it.
Data, context, and risk review
Inspect the available sources, permissions, privacy needs, model options, integrations, and risks before choosing the architecture.
Prototype and evaluation set
Build a focused prototype and test it with representative inputs, expected answers, difficult cases, and feedback from the people who understand the work.
Product build and integration
Develop the interface, application logic, retrieval, APIs, permissions, output validation, logging, and agreed review controls.
Launch, monitor, and improve
Release to an agreed user group, monitor quality, cost, latency, and failure patterns, then update the system when evidence or requirements change.
Relevant work
Generative AI built into real products
Two SensViz projects where language models sit inside a product people use, rather than in a demonstration.
GrantMatch
SensViz helped build a platform that combines language models, recommendation logic, and vector search to surface grant opportunities based on a user's profile and needs.
Relevant capability: language models, vector search, recommendation logic
CV Jury
SensViz developed an LLM and natural-language processing tool that prepares personalised cover-letter drafts using the user's information and role context.
Relevant capability: language models, natural-language processing, drafting
Why SensViz
Why businesses choose SensViz for Generative AI development
AI and product engineering stay connected
The model, interface, data, application logic, integrations, and cloud environment are designed as one product rather than separate experiments.
Model choice follows the task
Commercial and open-weight options are compared against the use case. The architecture remains replaceable where that is practical and worthwhile.
Quality is defined with tests
Representative examples and agreed criteria give the team a practical way to compare prompts, retrieval, models, and later changes.
Data access is designed explicitly
The application uses the client's identity, permission, retention, and data-handling requirements instead of expecting a prompt to enforce them.
Human control matches the risk
The product can show sources, request confirmation, route uncertain cases, or require approval when the use case needs those controls.
Ownership and change are planned
Code, prompts, evaluation data, model accounts, licences, documentation, monitoring, and support responsibilities are set out in the project agreement.
Choose the right delivery path
Related AI and software services
Some projects need a language-model application. Others need autonomous actions, a fixed workflow, specialist machine learning, or a complete SaaS product. Use the service that matches the main job the system must perform.
Generative AI and LLM development questions
Generative AI development services cover the design, development, integration, testing, and operation of software that creates or transforms text, images, audio, code, or structured data with foundation models. A project may include model selection, RAG, prompts, structured outputs, product UX, APIs, permissions, evaluation, monitoring, and support.
Generative AI is the wider category of models and applications that generate or transform content. A large language model, or LLM, is a type of foundation model designed mainly for language and related tasks. Some Generative AI applications use LLMs alone, while others combine language, vision, audio, or image models.
Usually not. Most business applications start with an existing commercial or open-weight model and improve task performance through clear instructions, useful context, retrieval, output validation, and application design. Training from scratch requires substantial data, compute, research expertise, and a reason that existing models cannot meet.
Retrieval-augmented generation, or RAG, searches approved sources and supplies relevant material to the model before it generates an answer. It is useful when the application must work with private, frequently updated, or source-specific information. RAG can improve grounding, but it does not guarantee a correct answer.
RAG is usually the first option when the model needs access to private or changing knowledge. Fine-tuning can help with repeated behaviour, format, style, or specialised task performance when there is suitable training data and a clear evaluation method. Some applications use both, while many need neither beyond good context and prompts.
Yes, depending on the data and chosen architecture. The design should cover data classification, permissions, provider terms, retention, logging, regional requirements, and deletion. Private data should be supplied only to the components and users authorised to access it. The final controls depend on the project and platform.
We define what a useful result looks like, assemble representative and difficult examples, compare outputs against agreed criteria, and record failure patterns. Depending on the task, evaluation may cover factual support, retrieval quality, format, relevance, refusal behaviour, latency, cost, and human review decisions.
No responsible provider should promise zero hallucinations. The risk can be reduced through better sources, retrieval, context, prompts, structured outputs, validation, evaluation, source display, and human review. The right controls depend on the consequences of an incorrect or misleading response.
This service focuses on applications that understand or generate content. Agentic AI adds goal-directed behaviour, tool use, and multi-step actions. A knowledge assistant may answer from approved sources, while an agent may decide which systems to use and take approved actions after the answer.
The main factors are the use case, number of user roles, data readiness, retrieval needs, integrations, interface, evaluation depth, privacy requirements, expected usage, hosting, model costs, and support scope. SensViz should confirm these during discovery before giving a delivery estimate or quote.
Working out budget? See our AI and software project pricing.
Start with one use case
Bring us the task, the data, and the users
Tell us what the application needs to create, answer, summarise, or help a user complete. We will review the inputs, quality bar, risks, integrations, and operating constraints, then recommend the simplest useful architecture.
