Banking & Financial Services
Customer lifecycle models, transactional pipelines, KYC standardization, risk scoring & fraud intelligence reporting.
Enterprise Data • AI • Architecture
From Enterprise Data to Trusted AI
We help organizations move from business questions and fragmented data to governed platforms, AI-ready knowledge layers, and production-grade Enterprise AI solutions.
Architecture first. Tools second. Build only after the problem, data, controls and success criteria are understood.
Our Core Expertise
Enterprise data foundations, analytics and practical AI solutions — designed together as one architecture.
Domain Expertise
Pharmaceutical & Life Sciences
Clinical data harmonization, SDTM automation, R&D analytics and compliance-driven transformations for global trials.
Consumer Goods (FMCG)
SKU modeling, demand forecasting, distributor/retail integrations, and supply-chain intelligence layers.
Logistics & Supply Chain
Shipment lifecycle analytics, telemetry ingestion pipelines, warehouse throughput and optimization KPIs.
Media & Entertainment
Audience segmentation, content performance intelligence, subscription analytics, revenue and marketing insights.
Data, AI & Architecture Solutions
Multi-Cloud Platforms
Azure • AWS • GCP • Hybrid integration and modernization.
Data Engineering
Bronze/Silver/Gold layers, ELT frameworks, CDC, orchestration & quality.
Architecture & Streaming
Event-driven systems, real-time pipelines, metadata & lineage.
Analytics & BI
Semantic layers, dashboards, KPI frameworks and governed reporting.
Enterprise AI Solutions
Enterprise search, RAG, knowledge assistants, copilots, workflow automation and agentic AI grounded in business data.
AI-Ready Data & Semantic Layer
Canonical models, business semantics, ontologies, metadata and governed knowledge foundations that make enterprise AI trustworthy.
AI Architecture & Governance
Use-case assessment, logical architecture, model/LLM selection patterns, security, human approvals, evaluation, observability and LLMOps.
AI POC & MVP
Rapidly validate enterprise AI ideas with measurable POCs and MVPs before committing to full-scale platform investment.
How We Approach Enterprise AI
Enterprise AI starts with questions, not tools.
Understand the problem. Establish trusted data. Choose the right AI pattern. Prove value before scaling.
- 01
Discover
Define the problem, users and outcomes.
- 02
Assess Data
Check quality, ownership and access.
- 03
AI Pattern
Choose retrieval, prediction or action.
- 04
Blueprint
Design controls and integrations.
- 05
POC / MVP
Test the riskiest assumptions.
- 06
Productionize
Evaluate, monitor and scale.
WITH A CLIENT
Problem-led architecture
Start with a real business need and the existing enterprise landscape.
- Client need
- Discovery
- Assessment
- Blueprint
- POC
- MVP
- Production
BUILDING OUR OWN SOLUTION
Hypothesis-led product build
Validate a repeatable problem and customer demand with a focused MVP.
- Market problem
- ICP
- Hypothesis
- Blueprint
- Demo
- Pilot
- Product
The questions behind every blueprint
Six decisions to resolve before implementation.
Business
User, problem & value
Key question
Who benefits, and which decision or task improves?
Data
Sources, quality & ownership
Key question
Is the knowledge usable, current and governed?
AI
Pattern & autonomy
Key question
Does the task need retrieval, generation, prediction or actions?
Security
Access, privacy & approvals
Key question
Who can see the data, and which actions need approval?
Integration
Systems, APIs & workflows
Key question
Which enterprise systems must connect?
Success
Accuracy, cost & outcomes
Key question
How will we measure quality, adoption and business value?
Architecture blueprint
From Enterprise Data to Trusted AI
A technology-agnostic blueprint connecting enterprise systems, governed data, semantic context and AI-driven experiences.
- 01
Experience
Where people interact
- 02
AI Orchestration
How intelligence becomes action
- 03
Knowledge & Intelligence
How answers are grounded
- 04
Data & Semantic Foundation
What gives AI business context
- 05
Enterprise Ecosystem
Where enterprise knowledge originates
One connected foundation. Technology choices follow the use case, data and operating constraints.
Portfolio & Case Studies
Examples across enterprise data platforms, analytics and AI-enabled solution patterns.
💳 Enterprise Customer Golden Record (Banking)
Unified customer identities across seven banking applications, eliminating duplicates, harmonizing KYC attributes, and establishing a 360° Customer Golden Record. Improved AML/KYC screening accuracy and reduced onboarding validation time while enabling faster regulatory reporting.
Azure • SQL • MDM • Governance🧬 Clinical Trial Data Harmonization (Pharma)
Built automated SDTM conversion pipelines and metadata-driven lineage for Phase II–IV trials across multiple CROs. Delivered audit-ready traceability, standardized global trial datasets, and reduced manual transformation effort.
AWS • Redshift • Glue • SDTM📦 SKU Demand Forecasting Data Hub (FMCG)
Architected a unified SKU, distributor, and retailer demand hub improving forecasting accuracy and reducing stock-outs. Deployed harmonized Silver layer models powering enterprise reporting and supply chain analytics.
Azure Synapse • Power BI • ELT🚚 Shipment Telemetry & Warehouse Analytics (Logistics)
Developed streaming telemetry pipelines integrating GPS, sensor, and operational logistics data. Enabled real-time delay prediction, warehouse throughput insights, and optimized route planning across regions.
Kafka • AWS • Streaming • IoT🤖 Enterprise Knowledge Assistant (RAG)
Designed a governed RAG pattern that connects enterprise documents, metadata and structured data to an LLM-based assistant. The architecture includes semantic retrieval, access controls, citations, evaluation and observability so answers remain grounded and auditable.
RAG • LLM • Vector Search • Governance🧠 AI-Ready Canonical & Semantic Knowledge Layer
Defined canonical business entities, relationships, ontology and governed semantic definitions to create a reusable knowledge layer for analytics, enterprise search, copilots and future agentic AI use cases.
Canonical Model • Ontology • Semantic Layer • AI⚙️ Agentic Workflow Architecture
Created a tool-agnostic architecture for AI agents that can reason over enterprise knowledge, call approved tools and APIs, execute controlled workflows, and route sensitive actions through human approval and policy guardrails.
Agents • Tool Calling • Workflow • Guardrails🎬 Content Performance & Audience Intelligence (Media)
Standardized subscriber, engagement, and revenue data into a unified analytics layer. Delivered content scoring, churn-prediction signals, and marketing recommendation insights improving customer retention strategies.
GCP • BigQuery • BI • Segmentation
Our story — connecting enterprise data with practical AI
Enterprise AI is only as useful as the data, business context and controls behind it. KonduriX brings data architecture and AI solutioning together: first understanding the business problem, then establishing trusted data and semantic foundations, and finally applying the right AI pattern — from enterprise search and RAG to copilots, automation and agents.
We work across the full path from source systems to intelligence: ingestion, modelling, canonical and semantic architecture, governance, analytics, LLM/RAG patterns, AI orchestration and production controls. The goal is not to add AI for its own sake, but to build solutions that are useful, explainable, secure and connected to measurable business outcomes.
How we work — a simple rhythm
- Listen first: we learn where the pain and opportunity actually are — not where the org chart says they might be.
- Model with domain intent: we shape data models that reflect how people run the business, not just how systems export rows.
- Engineer for trust: lineage, tests, and governance are included by default — trust fuels adoption.
- Deliver iteratively: value as early as possible, with a clear path to scale and operate.
Our work is measured by outcomes: faster insight cycles, fewer incidents, clear ownership, and dashboards that executives actually use. We partner for the long term — not for the slide deck. If a platform is to survive and matter, it must be useful, observable, and owned.