Foundations that make AI safe to scale

Data products with owners, lineage, and SLAs; secure access and open architectures; MLOps/LLMOps and AIOps to keep quality, privacy, and uptime on track-so innovation can run.

Capabilities

Core Services & Frameworks

We deliver structured solutions aligned with enterprise governance standards.

Data Readiness & Governance

Maintain metadata catalogs, lineage maps, quality checks, and privacy rules from a single pane.

Data Products & SLAs

Turn data into reusable, domain-owned products with clear contracts to cut delivery cycles.

Connectors, CDC & Federation

Unify sources using real-time CDC and federated access to curb duplicate storage and egress.

Lakehouse & Open Tables

Standardize on open table formats like Iceberg to keep datasets portable across engines.

Feature Store & Model Registry

Track versioned models, prompt configurations, and training features to reuse assets safely.

MLOps & LLMOps pipelines

Automate delivery, bias checks, drift alerts, and safe model rollback in production.

Governance & Guardrails

Deploy PII/prompt injection guardrails and role-based access rules across application layers.

Enterprise Focus

AI Readiness Across Roles

How data and AI capabilities align across business, technology, and compliance functions.

Custom AI Enablement for the Platform Engineering

Data Integration Backbone

Real-time CDC and connectors across cloud, ERP, and databases to speed up pipeline setup.

Real-Time Data Fabric

Event-driven ingestion layers delivering fresh metadata and tables to model contexts.

Platform Observability

Monitor pipeline load times, data freshness, and service health to head off downstream issues.

Custom AI Enablement for the AI/ML Engineering

Centralized Feature Stores

Govern feature datasets across training stages to ensure consistent inference results.

Drift & Explainability Tools

Automated dashboards tracking model accuracy, prediction drift, and prompt changes.

LLMOps Pipeline Automation

Deploy prompt updates, fine-tuning scripts, and vector indexes with CI/CD rigor.

Custom AI Enablement for the FinOps & Cost Control

Cost Visibility Dashboards

Trace compute, storage, and API token usage per business case, model, and developer.

Workload Optimizer

Auto-recommend instance sizing, vector indexing, and caching configurations to optimize spend.

Budget Guardrails

Set strict alert thresholds to prevent runaway GPU and cloud bills.

Case Studies

AI & Data Success Stories

See how Artha Solutions drives measurable efficiency, sales lift, and cost savings across industries.

BFSI 90-Day Release

ML-Based Account Deduplication

A leading US retail bank automated their audit workflow, resolving account metadata duplication and saving OPEX.

80% Steps Automated
Faster Decisions
Healthcare 6-Week Setup

AI Patient Record Matching

Implemented a structured Quick-Start AI/ML Lab for a global distributor to validate and sync clinical records safely.

25% Efficiency Lift
100% Audit Accuracy
Retail Inventory Optimization

Assortment & Stock Forecasts

Optimized shelf assortment levels at localized stores for a large retail network to meet fluctuating seasonal demand.

+5% Sales Revenue
-15% Markdown Cost
Manufacturing Predictive IoT

IoT Predictive Maintenance

AI predicts machine component failures on global factory floors, automating test triggers to avoid shutdown delays.

-25% Machine Downtime
-10% Maintenance Cost
FAQ

Common Questions Answered

Is Artha locked into a specific cloud or model provider?

No. Artha is platform-agnostic. We design and build open architectures that support AWS, Azure, GCP, and private server clusters, using open-source formats like Apache Iceberg.

How do you ensure platforms are ready for GenAI and Agentic AI?

We configure the vector database indexing, dynamic caching, LLM orchestration frameworks (like LangChain/LlamaIndex), and secure API routing required by autonomous agents.

Is this about tools or custom engineering?

Artha is a services firm. We build on top of your existing investments (Databricks, Snowflake, Talend, etc.), stitching together components using governed architecture patterns.

How do you address AI security and compliance at the platform level?

We embed PII filters, query guardrails, encryption at rest and in transit, and role-based access rules directly into your data pipelines and model API endpoints.

How does Artha use FinOps to control AI and cloud costs?

We design caching strategies, optimize embedding pipelines, and establish scale-to-zero compute rules to keep API and compute bills from spiraling.

Start Your AI Solutions Journey

Let's evaluate your database files, API endpoints, and model architecture. Schedule a complimentary data readiness audit with our certified architects today.

Consult An Architect
AI for Platform & Engineering Services AI Overview

Executive Summary: AI Platform Engineering services deploy feature registries, model registers, MLOps orchestration, security filters, and FinOps metrics to build safe, scalable AI platforms.

Key Entities: MLOps LLMOps Feature store Model registry Vector database Model monitoring AIOps FinOps

Consult With Our AI Architects

Accelerate your models, secure your data foundations, and build value-driven platforms.