Enterprise RAG Applications
Ground language-model responses in approved business knowledge using document ingestion, embeddings, vector retrieval, citations, access controls, and measurable retrieval quality.
AI Engineering
Production-Ready AI Applications, Agents and RAG Systems
ARINITS helps enterprises move from AI experiments to dependable software. We design retrieval-augmented generation, agent workflows, contextual memory, policy guardrails, evaluation pipelines, and secure integrations around real business processes.
Discuss Your ProjectWhat We Deliver
AI Agents and Workflow Automation
Design bounded agents that plan, use tools, maintain context, request human approval, and recover safely when a dependency or model call fails.
AI Safety and Governance
Apply policy checks, prompt-injection defenses, sensitive-data controls, audit trails, rate limits, and human-in-the-loop decisions to high-impact workflows.
LLM Integration and Evaluation
Select models based on quality, latency, privacy, and cost; then validate outputs through test datasets, automated scoring, observability, and regression checks.
How We Work
- 1
Use-case discovery
Define the user decision, source data, accuracy threshold, risks, and measurable business outcome before selecting a model.
- 2
Architecture and prototype
Prove retrieval quality, integration feasibility, security boundaries, and unit economics with a focused vertical slice.
- 3
Production engineering
Build APIs, orchestration, evaluation, monitoring, fallbacks, deployment automation, and operational controls.
- 4
Continuous improvement
Review user feedback, failure patterns, retrieval gaps, latency, and model cost to improve the system safely.
Technology Expertise
PythonFastAPISpring BootVector DatabasesRAGLLM APIsOllamaRedisDockerKubernetes
Engineering Outcomes
- ✓Answers grounded in controlled enterprise knowledge
- ✓Observable AI quality, latency, usage, and cost
- ✓Secure integration with existing applications and APIs
- ✓Graceful fallbacks and human approval for sensitive actions
Frequently Asked Questions
Can you add AI to an existing enterprise application?
Yes. We expose AI capabilities through secured APIs and asynchronous workflows, allowing existing Java, web, mobile, and microservice applications to adopt AI incrementally.
How do you reduce AI hallucinations?
We combine retrieval grounding, constrained prompts, tool validation, output schemas, citations, evaluation datasets, confidence-based fallbacks, and human review where the business risk requires it.
Can models run privately?
Depending on quality and infrastructure needs, we can design for private-cloud or locally hosted models as well as managed model APIs. The choice follows data sensitivity, latency, cost, and operational requirements.