Intelligence built into your product.
We integrate AI where it actually adds value — not as a buzzword, but as a functional layer that saves real time, surfaces real insights, and automates the genuinely repetitive. From LLM integrations to custom ML pipelines.
Flexonixs Infosoft builds production-grade AI solutions for companies that are done with AI as a buzzword. Our AI development services cover LLM integrations (OpenAI, Anthropic, Gemini), custom AI agents, Retrieval-Augmented Generation (RAG) pipelines, and predictive analytics systems that actually run in production.
We work with startups embedding AI into their core product, and enterprises automating workflows that currently eat 20+ hours per week. No science projects. No 'it works in the demo.' Just AI that earns its keep in your product.
Our AI development process starts with a value audit — identifying where automation or intelligence creates genuine ROI — before writing a single line of model code. We've seen too many AI projects fail because the use case was chosen for its impressiveness, not its impact.
What's included
- LLM integration (OpenAI, Anthropic, Gemini, Mistral)
- Custom AI agents & multi-agent workflows
- Retrieval-Augmented Generation (RAG) systems
- AI-powered document processing & extraction
- Predictive analytics & forecasting models
- AI automation pipelines & workflow orchestration
- Model fine-tuning & deployment
- AI feature evaluation & ROI audit
Common questions
How do you decide which AI approach to use for my project?
We start with a value audit — mapping your workflows to identify where AI creates genuine ROI vs. where it adds complexity without benefit. Then we select the right approach: LLM integration for language tasks, custom ML for prediction, RAG for knowledge retrieval, or agents for multi-step automation.
Do you build with OpenAI or do you use open-source models?
Both. For most commercial use cases, OpenAI, Anthropic, or Google's APIs offer the best performance-to-cost ratio and fastest time to market. For use cases with data privacy requirements or high volume, we evaluate open-source models (Llama, Mistral) deployed on your own infrastructure.
How long does an AI integration project take?
A focused LLM integration (e.g., adding a document Q&A feature to an existing product) typically takes 3–6 weeks. A full custom AI pipeline with training data pipelines, evaluation frameworks, and production infrastructure is 8–16 weeks. We scope precisely after a discovery session.
Can you build AI features into our existing product?
Yes — this is our most common AI engagement. We integrate AI capabilities into existing codebases via API layers and feature flags, so you can ship incrementally without a full rewrite.
What industries do you build AI solutions for?
Healthcare (clinical documentation, patient triage, insurance processing), SaaS products (AI features, automation), hospitality (guest communications, demand forecasting), and operations teams across industries automating repetitive workflows.
Let's build something
that matters.
Whether you have a fully formed spec or just a napkin sketch — we'd love to hear about it. First consultation is always free.