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.
Our solutioning thought process
Enterprise AI starts with questions, not tools
Before choosing an LLM, vector database, cloud service or agent framework, we establish what must be solved, what data can be trusted, what actions AI may take, and how success will be measured.
01
Discover
Business problem, users, pain points, outcomes and constraints.
→
02
Assess Data
Sources, ownership, quality, sensitivity, access and freshness.
→
03
Define AI Pattern
Search, RAG, copilot, workflow automation, agentic AI or ML.
→
04
Blueprint
Logical architecture, security, governance, integrations and NFRs.
→
05
POC / MVP
Prove the riskiest assumptions with measurable acceptance criteria.
→
06
Productionize
Observability, evaluation, guardrails, LLMOps/MLOps, cost and scale.
WITH A CLIENT
Problem-led architecture
Start from a real business problem and existing enterprise landscape. Discovery answers drive the blueprint; the implementation stack is selected only after the target state is clear.
Client Need → Discovery → Data & Risk Assessment → Blueprint → POC → MVP → Production
WITHOUT A CLIENT
Hypothesis-led product build
Choose a repeatable industry problem, define the ideal customer profile and assumptions, then invest in a narrow reusable MVP. Validate demand early before expanding into a full platform.
Market Problem → ICP → Assumptions → Reference Blueprint → Demo/MVP → Pilot Client → Productize
What we need answers to before implementation
Business
Who is the user? What decision or task improves? What is the measurable value?
Who is the user? What decision or task improves? What is the measurable value?
Data
Where is the knowledge? Is it structured, unstructured, real-time, governed and usable?
Where is the knowledge? Is it structured, unstructured, real-time, governed and usable?
AI
Does the use case need generation, retrieval, prediction, reasoning, tools or autonomous actions?
Does the use case need generation, retrieval, prediction, reasoning, tools or autonomous actions?
Security
What is sensitive? Who can see what? What actions require human approval?
What is sensitive? Who can see what? What actions require human approval?
Integration
Which operational systems, APIs, files, warehouses or applications must connect?
Which operational systems, APIs, files, warehouses or applications must connect?
Success
How do we evaluate accuracy, latency, cost, adoption, safety and business outcome?
How do we evaluate accuracy, latency, cost, adoption, safety and business outcome?
Architecture blueprint
A tool-agnostic Enterprise AI reference architecture
The architecture remains broadly stable even when products and cloud services change.
Experience LayerWeb • Mobile • BI • Copilot • APIs
↓
AI Orchestration LayerPrompting • Routing • Agents • Tool Calling • Workflow • Human Approval
↓
Knowledge & Intelligence LayerLLM / SLM • RAG • Semantic Search • Vector Retrieval • ML Models • Business Rules
↓
Enterprise Data & Semantic LayerCanonical Models • Metadata • Ontology • Master Data • Metrics • Data Products
↓
Source & Integration LayerDatabases • Files • SaaS • APIs • Events • Documents • Operational Systems
Security • Governance • Privacy • Evaluation • Observability • LLMOps/MLOps • FinOps
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.