AI Development Services

From agentic AI and LLM-powered applications to predictive machine learning, we design, build, and run AI systems that work reliably in production.

AI & Machine Learning at SanixTechnologies

Most AI initiatives stall somewhere between a promising demo and a system the business can depend on. SanixTechnologies closes that gap. We start from a concrete business problem, such as slow manual workflows, knowledge scattered across systems, or decisions made without data, and build AI solutions that are measurable, secure, and maintainable.

Our work spans the modern AI stack: autonomous AI agents that plan and act across your tools, generative AI features built on models such as OpenAI GPT, Anthropic Claude, and Google Gemini, retrieval-augmented generation (RAG) over your own data, and classical machine learning for forecasting, classification, and computer vision.

Every solution ships with the engineering that production AI needs: evaluation suites, guardrails, observability, cost controls, and clear human-in-the-loop checkpoints.

Capabilities

What We Deliver

Agentic AI & Multi-Agent Systems

AI agents that reason, call tools and APIs, and complete multi-step workflows, built with frameworks such as LangGraph and CrewAI.

Generative AI & LLM Integration

Assistants, content generation, summarization, and document processing integrated into your products and internal systems.

RAG & Enterprise Knowledge Search

Retrieval-augmented generation over documents, tickets, and databases using vector search, so answers are grounded in your own data.

Model Context Protocol (MCP) Servers

Securely expose internal systems and data to AI assistants and agents through standard MCP integrations.

Machine Learning & Predictive Analytics

Forecasting, recommendation, anomaly detection, and classification models trained on your data and deployed as reliable services.

Computer Vision & NLP

Image and video analysis, OCR and document understanding, and natural language processing for classification and extraction.

Technology

Tools & Technologies We Use

OpenAI
Anthropic Claude
Google Gemini
Llama
Mistral
LangChain
LangGraph
CrewAI
Model Context Protocol (MCP)
PyTorch
TensorFlow
Hugging Face
pgvector
Pinecone
Weaviate
AWS Bedrock
Azure OpenAI
Vertex AI
MLflow

Our Approach

How We Deliver

01
Opportunity Assessment
We identify where AI can create measurable value, check data readiness, and define success metrics before any build begins.
02
Prototype & Evaluate
A focused proof of concept is tested against real data and an evaluation set, so decisions are based on accuracy, latency, and cost rather than demos.
03
Build for Production
We harden the solution with integrations, security, guardrails, monitoring, and fallbacks, then deploy it into your cloud environment.
04
Monitor & Improve
Ongoing evaluation, model updates, and cost optimization keep quality high as your data, models, and users change.

Why SanixTechnologies

Why Work With Us

Business Outcomes First

Each engagement is tied to metrics such as hours saved, faster resolution times, or revenue impact.

Model-Agnostic Architecture

We choose the right commercial or open-weight model for each task and avoid lock-in to a single provider.

Security & Data Privacy

Private deployments, access controls, and clear data handling policies keep sensitive information protected.

Production-Grade Engineering

Evaluation, observability, and CI/CD come standard, so AI features stay reliable after launch.

FAQ

Frequently Asked Questions

What is agentic AI?

Agentic AI refers to systems that go beyond answering prompts. They plan steps, use tools and APIs, and take actions to complete a goal, with humans reviewing important decisions. Typical examples are support agents that resolve tickets and operations agents that automate back-office workflows.

Which AI models do you work with?

We work with leading commercial models such as OpenAI GPT, Anthropic Claude, and Google Gemini, as well as open-weight models like Llama and Mistral. Model choice is based on quality, latency, cost, and data residency requirements.

Can you build AI on top of our private data?

Yes. Using retrieval-augmented generation, fine-tuning where appropriate, and secure MCP integrations, we connect AI to your documents and systems while keeping your access controls in place.

How do you make sure AI outputs are accurate?

We build evaluation datasets, automated tests, and guardrails for every solution, measure accuracy before launch, and monitor quality in production so issues are caught early.

How long does an AI project take?

A focused proof of concept can usually be scoped to a few weeks, while production timelines depend on integrations and scope. We define milestones up front so you see working results early.