Custom LLM applications
Build assistants, search experiences, and language-powered products around a defined business task and the people who use them.
LLM development for the way you work.
Turn your business knowledge into useful AI experiences. AsonTech Solutions builds custom language applications, retrieval systems, and enterprise integrations with clear measures for quality, cost, and control.
Business-firstuse casesData-awarearchitectureMeasurabledelivery
Language AI built around your business.
From an initial proof of value to an integrated application, choose the capabilities your workflow needs.
Retrieval-augmented generation
Connect approved documents and business knowledge to model responses, with source references and permission-aware retrieval.
Model adaptation & fine-tuning
Adapt a suitable foundation model to specialized language, response formats, or repeatable tasks when evaluation supports the investment.
Enterprise integration
Connect language AI to your product, CRM, support tools, and internal systems through APIs and controlled workflows.
Evaluation & safety controls
Test groundedness, task accuracy, prompt injection, data exposure, and failure handling before releasing to users.
Deployment & ongoing improvement
Track quality, latency, and usage costs. Improve retrieval, prompts, and models as your content and requirements change.
Make knowledge easier to put to work.
Practical applications for teams serving customers, managing information, and building digital products.
Internal knowledge assistants
Help employees find relevant policies, product information, and operating guidance with references to approved sources.
Customer support assistance
Draft responses, summarize conversations, and help agents locate answers while keeping escalation paths clear.
Document processing
Extract structured information, classify documents, and prepare summaries for review in existing workflows.
Product copilots
Add contextual assistance to software products with permissions, usage controls, and feedback built in.
A clear path from idea to implementation.
Shared milestones keep business stakeholders and engineering teams aligned throughout delivery.
Define the outcome
Identify the workflow, users, success measures, and decisions that require human review.
Assess your data
Review source quality, access rights, sensitive information, and integration requirements.
Validate the approach
Compare prompting, retrieval, and fine-tuning against a representative evaluation set.
Build & integrate
Develop the application, retrieval pipeline, permissions, and system connections.
Test & release
Evaluate real-world scenarios and failure cases, then introduce the system through a controlled rollout.
Monitor & improve
Review feedback, quality, response time, and cost to guide subsequent releases.
Plan for trust before you launch.
We define data boundaries, access permissions, evaluation criteria, and escalation rules alongside the application. Your team gets a clear view of what the system can do, where review is needed, and how performance will be measured.
Permission-aware retrievalSource referencesHuman reviewQuality evaluationsUsage monitoringDocumented handover
LLM development FAQs
What to consider before bringing language AI into your business.
What are LLM development services?
LLM development turns large language models into useful business applications. It includes use-case planning, data preparation, model selection, retrieval or fine-tuning, integrations, evaluation, and deployment.
Do we need to train a model from scratch?
Usually, the first step is evaluating an existing foundation model. Prompting and retrieval may meet your needs. Fine-tuning is considered when a measured gap justifies it; training from scratch requires a separate assessment of data, compute, and cost.
What is the difference between RAG and fine-tuning?
RAG retrieves relevant information at request time, making it useful for changing business knowledge. Fine-tuning adjusts model behavior using training examples. The two approaches can be combined when the use case calls for it.
Can an LLM work with our private company data?
We can design access-controlled retrieval and deployment around your data requirements. Provider terms, retention, hosting, and user permissions should be agreed before connecting sensitive information.
How do you manage incorrect AI responses?
We use task-specific evaluations, source grounding, output validation, and fallback behavior. For consequential decisions, the workflow should include human review. No language model can be promised to be error-free.
What determines the timeline and cost?
Scope, source quality, integrations, deployment requirements, and evaluation targets all affect delivery. A discovery phase establishes a practical plan and estimate before a production commitment.
Can you support our existing engineering team?
Yes. We can contribute focused LLM engineering expertise or work as a dedicated delivery team, with scope, responsibilities, documentation, and handover agreed at the start.
Have a workflow in mind?
Let’s identify where language AI can help and what it will take to deliver.
Talk to Our Team