AI/LLM Integration
Make AI work for your business, not the other way around.
Artificial intelligence is transforming every industry, but most businesses struggle to move beyond proof-of-concepts. Our AI/LLM Integration service helps you embed practical, production-ready AI capabilities into your existing applications and workflows.
We work with leading AI models and frameworks - including OpenAI GPT, Anthropic Claude, open-source models, and custom fine-tuned solutions - to build intelligent features like automated customer support, document processing, content generation, predictive analytics, and more.
Our approach is pragmatic: we start with your business problem, evaluate whether AI is the right solution, prototype quickly, and iterate based on real-world performance. We handle the entire pipeline - from data preparation and model selection to deployment, monitoring, and continuous improvement.
How AI/LLM Works in Practice
Large language models are useful in production when they are connected to your data, constrained to your use case and measured against real outcomes. A chatbot that answers from your documentation, an assistant that drafts responses for your support team, a pipeline that extracts fields from thousands of invoices, or a classifier that routes tickets: these are the integrations that pay for themselves, and they need engineering, not just a prompt.
We build with the model that fits the job, including OpenAI GPT, Anthropic Claude, Google Gemini and open-source models such as Llama and Mistral hosted in your own cloud when data must not leave it. Retrieval-augmented generation (RAG) over your documents, structured outputs, function calling and evaluation harnesses are standard parts of every build.
Security and cost are designed in from the start. Prompt injection defences, data access controls, PII handling, audit logging and per-feature cost tracking are part of the architecture, and our DevSecOps background means the AI service is deployed with the same pipeline discipline as any other production system.
Our AI/LLM Process
Every engagement follows the same six steps, scoped to your size and your goals, so you always know what happens next and what you will receive.
- 01
Use case discovery
We identify the tasks where an LLM will save measurable time or money, estimate value and risk for each, and pick one or two to prove first.
- 02
Data and model assessment
We review the data the model needs, its sensitivity and quality, and select a model and hosting option that satisfies your privacy and cost constraints.
- 03
Prototype and evaluation
A working prototype is built quickly and evaluated against a labelled test set, so decisions are based on accuracy numbers rather than demos.
- 04
Production build
Retrieval pipeline, prompt management, guardrails, caching, observability and integration with your existing systems, built as a maintainable service.
- 05
Security review
Prompt injection testing, access control validation and data leakage checks before launch.
- 06
Launch and optimisation
Gradual rollout with usage and quality monitoring, then cost and latency optimisation once real traffic patterns are known.
What We Deliver
Technologies We Use
Who Needs This
Why Choose Warans Tech for AI/LLM?
Proven Expertise
250+ projects delivered with deep domain experience across industries.
Fast Delivery
Agile methodology with rapid iterations and transparent communication.
Security-First
Every solution is built with security best practices from day one.
Ongoing Support
We do not disappear after launch - continuous support and optimization.
What You Receive
- Use case assessment with value and risk scoring
- Architecture document covering model choice, data flow and security
- Evaluation harness and baseline accuracy report
- Production RAG or agent service with API and documentation
- Prompt and configuration management with version control
- Guardrails, logging and cost dashboards
- Integration with your CRM, helpdesk, document store or product
- Handover training for your engineers
AI/LLM Questions, Answered
Will our data be used to train public models?
No. We use enterprise API tiers where providers contractually exclude your data from training, or we host open-source models inside your own cloud account so data never leaves your environment.
Which model should we use?
It depends on the task, the required accuracy, latency, cost and data residency rules. We evaluate two or three candidates on your real data during the prototype phase and show you the numbers before recommending one.
How accurate will a RAG chatbot be?
Accuracy depends more on the quality and structure of your documents than on the model. Well-prepared knowledge bases typically achieve high answer accuracy on in-scope questions, and the evaluation harness we deliver lets you measure and improve it over time.
How do you prevent the model from saying something wrong or harmful?
Grounding answers in retrieved sources with citations, restricting the model to its task, output validation, content filters and human review for high-impact actions. We also test for prompt injection before launch.
What does an LLM integration cost to run?
Usage costs are driven by tokens processed. We design for cost from the start with caching, smaller models for simple steps and clear per-feature tracking, and we report projected monthly costs before you commit to production.
Can you build AI agents that take actions?
Yes, with function calling and tool use, for example creating tickets, updating records or running workflows. Actions with real consequences are gated behind confirmation or human approval by design.
Ready to Get Started with AI/LLM?
Fill out the form and our team will get back to you within 24 hours with a tailored proposal.