Capability | CAI Agents & RAG

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AI Agents & RAG That Accelerate Decisions and Deliver ROI

We build AI systems that automate knowledge access, reduce manual work, and deliver measurable results. By combining AI agents with RAG architecture, we help you make faster decisions - powered by your own verified data.

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AI Agents & RAG unlock measurable gains across speed, productivity, and security - and the cost of not using them is growing.

74%

of organizations see measurable ROI within 6 months of deploying GenAI use cases

2x productivity

reported in companies using GenAI-driven workflows vs. traditional methods

54% revenue

of companies improved security through GenAI-powered threat detection and monitoring

66%

enhance care with more personalized, efficient interactions - falling behind puts you at risk

We turn scattered data and tasks into smart, domain-specific AI agents – delivering clarity and real-time impact.

PROBLEM

Teams spend too much time navigating siloed systems and scattered knowledge.

OUR SOLUTION

RAG-powered assistant

We integrate 100K+ documents, tickets, and knowledge bases into one interface – providing precise, cited answers in seconds and reducing search time.

PROBLEM

Your decisions require trusted, auditable outputs based on verified internal data.

OUR SOLUTION

Verified insight engine

Our solutions retrieve answers from your own data, enabling audit-ready responses with full source traceability and compliance confidence.

PROBLEM

Manual processes can’t keep up with growing complexity – simple Q&A isn’t enough.

OUR SOLUTION

Autonomous AI agent

We build agents that plan, decide, and act – handling multi-step workflows with the right tools and logic to generate coherent outputs.

PROBLEM

You need to combine and interpret multiple sources across layers in real time.

OUR SOLUTION

Multi-source reasoning agent

Our agents decompose complex queries, validate data on the fly, and synthesize final insights across dashboards, calendars, and stakeholder input.

1

Discovery & Use Case Definition

We begin by identifying the core problem, running a workshop with your team, and selecting the right approach - whether RAG, AI Agent, or a combination.

2

Golden Data Set

We define all relevant data sources and clearly outline use cases, covering both desired and undesired system behavior.

3

System Design

We design the architecture of individual system modules and select the best-fit technology stack.

4

Prototyping & Orchestration (MVP)

We create a rapid functional prototype to showcase added value, apply prompt engineering, and integrate tools and data sources.

5

Deployment & Scaling

We select the ideal environment (cloud/on-prem) and deploy the system to its target infrastructure.

6

Operation & Continuous Learning

We collect feedback, evolve prompts and capabilities, adapt to new data, and handle ongoing updates and maintenance.

Clinical Decision Support Agent

An AI assistant designed for healthcare environments – it evaluates symptoms, retrieves recommended treatments based on clinical protocols, and suggests next steps using cited medical sources. Improves decision accuracy and supports overburdened clinical teams.

Data Insight Agent

A data aggregation agent that extracts key insights from dashboards, official reports, and stakeholder inputs like calendars or meeting notes. It generates summarized, real-time reports that help teams act on data faster.

Maintenance Planning Agent

An intelligent planner that analyzes operational data across systems, monitors current versions and risks, and schedules proactive maintenance. Helps engineering teams stay ahead of system issues and downtime.

E-Commerce Product Agent

Built for product and marketing teams – this agent tracks product performance, recommends price changes based on trends, and suggests content updates for SEO. It also summarizes customer feedback to support better decision-making.