AI & Machine Learning Engineering · Production-Grade ML Systems

AI systems built for
production,
not just prototypes.

Q: How is this different from bolting a chatbot onto our product?

We design intelligence into the architecture from day one - data pipelines, feature stores, and inference layers - rather than adding a model on top of an existing system. AI that only works in a demo isn't AI, it's a liability, so everything we ship goes through the same production rigour as our Java microservices.

Q: Can you integrate LLMs into our existing Java or enterprise stack?

Yes. We build on Spring AI, so GenAI and LLM integration - RAG pipelines, vector search, prompt engineering - runs natively alongside your existing Java services, without standing up a separate Python stack to maintain.

Q: What guardrails do you put around AI systems in production?

Every system we ship includes human-in-the-loop review checkpoints, monitoring, and guardrails on high-impact decisions, plus versioned models and reproducible pipelines - so intelligent automation never means unsupervised automation.

Q: How do you handle bias, explainability, and regulatory requirements?

We work to explainability (XAI) and model risk management standards, run ongoing bias and fairness audits, and align with data privacy regulations like India's DPDP - drawing on our fintech and BFSI background to build AI that's compliance-aware from the start.

Q: Do you only build GenAI features, or also fraud detection and automation?

Both. Alongside GenAI/LLM integration, we build ML-powered fraud detection, intelligent document processing, workflow automation, and analytics pipelines - the same ML systems already running in production behind our fintech platforms.

From GenAI / LLM integration to intelligent automation, ML pipelines & analytics, and AI-powered fraud detection - we embed intelligence into your product as core architecture, not a bolt-on. 15+ years of enterprise engineering discipline applied to every model we ship.

Discuss Your AI Project → View Case Studies

AI / ML Platform Architecture

🧠

LLM Orchestration & RAG Pipeline

GenAI

🔎

Vector Search / Embeddings Store

Fast

🤖

Real-time Model Serving Layer

AI/ML

📊

Feature Store & Data Pipelines

Scalable

🛡

Model Guardrails & Governance

Secure

✅

Human-in-the-loop Review Layer

Compliant

Inference latency Sub-50ms p95

10×

Concurrent sessions unlocked with AI

<50ms

AI-augmented inference latency

AI-integrated platforms delivered

ISO

27001 certified security

Our AI capabilities

Four areas where we
go deep on AI.

All case studies →

GenAI / LLM Integration

LLM-powered copilots, retrieval-augmented generation (RAG), and natural language interfaces embedded directly into your product - engineered for accuracy, latency, and cost control.

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Intelligent Automation

Workflow automation, intelligent document processing, and MLOps-driven model serving that replaces manual operational work with reliable, monitored systems - built with the same production rigour as our core Java systems.

Workflow Automation Model Serving Feature Pipelines Intelligent Document Processing

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ML Pipelines & Analytics

High-throughput pricing and analytics engines with AI-driven personalisation and real-time pipelines, processing 100K+ transactions daily at sub-50ms latency.

100K+ tx/day Sub-50ms AI Pricing Event-Driven

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AI-Powered Fraud Detection

ML models for real-time anomaly detection, identity fraud prevention, and intelligent transaction monitoring - deployed alongside our ACH/RPPS payment and Video KYC platforms, not just static, rule-based filters.

Fraud Detection AI Face Matching Anomaly Detection Real-time Scoring

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Have a different AI challenge?

We also work on document intelligence, computer vision, predictive analytics, and AI readiness assessments for legacy platforms. Let's talk.

Talk to our AI team →

Why OKRUTI for AI

Production discipline meets
intelligent systems.

AI that only works in a demo isn't AI - it's a liability. Our team brings the same enterprise engineering rigour we apply to fintech and BFSI systems to every model we ship.

AI as core architecture, not an add-on

We design intelligence into the system from day one - data pipelines, feature stores, and inference layers - rather than bolting a model onto an existing product.

Production-grade MLOps discipline

Versioned models, monitored inference, reproducible pipelines. The same CI/CD rigour we apply to Java microservices, applied to ML.

Domain-aware AI for regulated industries

Our fintech and BFSI background means we design AI systems that are compliance-aware from the start, not retrofitted after an audit.

Human-in-the-loop safety & guardrails

Every AI system we ship includes review checkpoints, guardrails, and monitoring - so intelligent automation never means unsupervised automation.

AI governance & standards we work with

MRM

Model Risk Management

Structured validation before production rollout

XAI

Explainability

Interpretable outputs for regulated decisions

DPDP

Data Privacy

India DPDP & global privacy alignment

ISO 27001

Information Security

Independently certified annually

HITL

Human-in-the-loop

Review checkpoints on high-impact decisions

Bias Audit

Fairness Review

Ongoing model bias & fairness monitoring

FAQ

Common questions about AI engineering.

How is this different from bolting a chatbot onto our product?

We design intelligence into the architecture from day one - data pipelines, feature stores, and inference layers - rather than adding a model on top of an existing system. AI that only works in a demo isn't AI, it's a liability, so everything we ship goes through the same production rigour as our Java microservices.

Can you integrate LLMs into our existing Java or enterprise stack?

Yes. We build on Spring AI, so GenAI and LLM integration - RAG pipelines, vector search, prompt engineering - runs natively alongside your existing Java services, without standing up a separate Python stack to maintain.

What guardrails do you put around AI systems in production?

Every system we ship includes human-in-the-loop review checkpoints, monitoring, and guardrails on high-impact decisions, plus versioned models and reproducible pipelines - so intelligent automation never means unsupervised automation.

How do you handle bias, explainability, and regulatory requirements?

We work to explainability (XAI) and model risk management standards, run ongoing bias and fairness audits, and align with data privacy regulations like India's DPDP - drawing on our fintech and BFSI background to build AI that's compliance-aware from the start.

Do you only build GenAI features, or also fraud detection and automation?

Both. Alongside GenAI/LLM integration, we build ML-powered fraud detection, intelligent document processing, workflow automation, and analytics pipelines - the same ML systems already running in production behind our fintech platforms.

Get in touch

Let's talk about your platform.

Whether it's an LLM copilot or a fraud model - tell us what you're building.

✉️

Email us

📍

Location

Jaipur, India · Global delivery

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Company

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Project details

We reply within 24 hours.

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AI systems built for production, not just prototypes.

Four areas where wego deep on AI.

GenAI / LLM Integration

Intelligent Automation

ML Pipelines & Analytics

AI-Powered Fraud Detection

Have a different AI challenge?

Production discipline meetsintelligent systems.

AI-powered case studies.

Scaling Video KYC for Major Banks & Insurers

Real-time Pricing Engine at 100K+ Transactions/Day

Automated Pricing Orchestration for a Retail Chain

Secure & Scalable ACH/RPPS Payment Platform

Laying the Foundation of API Security for a Financial Platform

Speeding Up Digital Trading with a Microservices & AI Platform

Common questions about AI engineering.

Technologies we useon every AI engagement.

Let's talk about your platform.

AI systems built for
production,
not just prototypes.

Four areas where we
go deep on AI.

Production discipline meets
intelligent systems.

Technologies we use
on every AI engagement.