Agentic & Generative AI Services
We build LLM agents, retrieval pipelines, and AI copilots on your business logic and your data systems that survive production traffic, messy inputs, and users who don't read instructions. Not demos.
What is Agentic & Generative AI?
Agentic AI describes systems that pursue a goal across multiple steps rather than answering a single prompt. An agent reads context, selects a tool, takes an action, evaluates the result, and adjusts repeating until the task is done or it hands off to a person.
Generative AI is the underlying capability: models that produce text, code, images, or structured data. Agentic systems use generative models as their reasoning layer, wrapped in retrieval, tools, and guardrails.
What's Included
- Custom LLM Integration
- RAG Pipelines
- AI Chatbots
- Computer Vision
Why Agentic AI Changes the Economics
Traditional automation follows rules you write in advance. It breaks the moment reality doesn't match the flowchart, an unusual invoice format, a customer question phrased three ways, a document that's mostly right.
Agentic systems reason through the gap. They read context, choose a tool, take an action, and check the result which means they handle the long tail of cases that made automation projects stall at 60% coverage.
See How We've Deployed This
What's Included
Explore the core capabilities we deliver under Agentic & Generative AI.
Custom LLM Integration
Embed GPT, Claude, Llama, or an open model directly into your product or workflow. We handle prompt architecture, structured output, fallback logic, and cost control including routing simple calls to cheaper models so your bill scales slower than your usage
Embed GPT, Claude, Llama, or an open model directly into your product or workflow. We handle prompt architecture, structured output, fallback logic, and cost control including routing simple calls to cheaper models so your bill scales slower than your usage
Why Choose Us
We don't just write code we build products that drive real business outcomes.
Fast Delivery
Discovery to deployed prototype, typical range
Proven Track Record
Across AI, SaaS, cloud, and data since 2017
Dedicated Team
You get a dedicated team of experts who treat your project like their own product.
Enterprise Security
Security-first architecture with best practices baked into every layer of the product.
24/7 Support
Round-the-clock monitoring and support — we don't sleep when your product is live.
Scalable by Design
Systems built to grow from your first user to your millionth without costly rewrites.
Our Process
Four phases from product strategy to launch. Here's what happens in each, and what you get at the end of it.
Discovery & Use Case Analysis
We work through what you're trying to automate, what data exists to support it, and importantly whether an LLM is the right tool at all. Some problems are better solved with a database query and we'll say so.
You Get: A written use case assessment, a feasibility call, and a cost range.
Model Selection & Fine tuning
We benchmark candidate models against your actual data rather than public leaderboards, and decide between prompting, RAG, and fine-tuning based on results and running cost.
You Get: A model recommendation with benchmark data and projected token spend
Integration & Testing
Building the pipeline into your systems, with an evaluation harness that scores output quality on every change. Guardrails, fallback behaviour, and human handoff defined before launch.
You Get: A working system in staging, plus the eval suite so quality is measurable rather than felt.
Deployment & Monitoring
Staged rollout with monitoring on latency, cost per call, and output quality. Drift detection so you find out when performance degrades before your users do.
You Get: Production deployment, a monitoring dashboard, and a defined support window.
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Here's what you gain when you choose Agentic & Generative AI with Aridian Technologies.
Coverage of the Long Tail
Rule-based automation typically stalls around 60% of cases. Agentic systems handle the exceptions that made the remaining 40% manual which is where the cost usually sits.
Decisions Grounded in Your Data
RAG pipelines mean the system answers from your documents and your records, with citations, rather than from whatever the model absorbed in training.
Cost That Scales Slower Than Usage
Model routing, caching, and prompt optimization keep per-call cost falling as volume rises. We report token spend alongside delivery from the first sprint.
Real Projects, Real Outcomes
Ten industry claims backed by shipped software. Here are three.
Frontline Ticketing
SaaS Event Ticketing Platform
5+ years of continuous collaboration and 24/7 uptime
Delivered a full-featured multi-currency event management SaaS with AI identity verification, QR entry, and escrow payouts — deployed on AWS.
Read Case Study
ICE Innovations
AI-Powered Ride-Hailing Platform
AI-based driver verification reduced fraud by 80%
Built an AI-powered ride-hailing platform with real-time driver verification using computer vision and automated insurance integration.
Read Case Study
VIA Consulting
Enterprise Azure Data Pipeline
Reporting time reduced from days to hours
Designed and deployed a full ETL pipeline on Azure Synapse migrating Blob Storage to Azure SQL with Power BI dashboards for real-time business insights.
Read Case StudyFrequently Asked Questions
Generative AI produces content text, code, images in response to a prompt. Agentic AI wraps that capability in a loop: planning, calling tools, checking results, and retrying until a task completes. Agents act; generative models produce.
GPT models via OpenAI and Azure OpenAI, Claude via Anthropic and AWS Bedrock, and open models including Llama and Mistral where self-hosting matters for cost or data residency. We benchmark against your data before recommending one.
Yes grounded in your documentation and connected to your systems so it can act, not just answer. Deployed to your website, Slack, WhatsApp, or voice, with human handoff for anything it shouldn't handle alone.
A working prototype typically takes four to six weeks. Full production deployment usually runs three to four months depending on integration complexity and how clean the underlying data is.
We use enterprise API tiers with zero data retention, so your inputs are never used to train third-party models. For stricter requirements we deploy open models in your own cloud environment, where data never leaves your infrastructure.
Retrieval grounds responses in your source documents with citations. An evaluation suite scores output quality on every change. Guardrails constrain what the system can do, and low-confidence cases route to a human rather than guessing.