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ServicesEnterprise AI, Machine Learning & Autonomous Agents
GenAI & Predictive Models Practice

Enterprise AI, Machine Learning & Autonomous Agents

Custom AI model training, fine-tuned LLMs, computer vision inspection, and multi-agent workflows integrated into enterprise core operations.

LLM Fine-Tuning & Agent WorkflowsReal-Time Computer Vision InspectionPredictive Anomaly DetectionModel Context Protocol (MCP) Integration
99.2%
Model Accuracy
Grounded RAG & Validation
<80ms
Inference Speed
GPU Edge Model Acceleration
12,000+
Pre-Built AI Tasks
Catalog of Governed Agent Workflows
<0.1%
Hallucination Rate
Guardrail & Citations Policy
40% Cost Saved
Compute Efficiency
Quantized Model Optimization
Core Practice Pillars

Key Practice Capabilities & Deliverables

Comprehensive deliverables engineered by senior software architects, DevOps leads, and security engineers.

Custom LLM Fine-Tuning & Domain RAG

Private LLM Fine-Tuning Pipelines
Vector Database Indexing (pgvector/Pinecone)
Contextual RAG Retrieval Systems
Prompt Governance & PII Masking

Autonomous Multi-Agent Workflow Engineering

Agentic State Machine Workflows
Model Context Protocol (MCP) Tool Tools
Autonomy Level Governance (A0-A4)
Human-in-the-Loop Approval Queues

Computer Vision & Edge Defect Inspection

YOLOv8 & OpenCV Inspection Models
Edge Hardware Deployment (NVIDIA Jetson)
Defect Classification & Bounding Boxes
Real-Time Camera Stream Processing

Predictive Analytics & Anomaly Detection

Time-Series Anomaly Detection Models
Equipment Remaining Useful Life (RUL)
Customer Lifetime Value (LTV) Forecasts
Automated Retraining Pipelines
Agile Delivery

5-Stage Agile Lifecycle & Quality Governance

01

Data Feasibility & Audit

Evaluating data quality, data labeling availability, privacy constraints, and AI ROI feasibility.

02

Model Selection & Benchmark

Benchmarking foundation models (OpenAI, Claude, Llama) vs fine-tuned domain models for accuracy and cost.

03

Pipeline & Agent Engineering

Building data ETL pipelines, RAG vector indexes, and agent orchestration state graphs.

04

Evaluation & Guardrail Audit

Testing against adversarial prompt attacks, hallucination benchmarks, and PII leakage filters.

05

Production MLOps Deployment

Deploying to GPU clusters with real-time drift monitoring, latency tracking, and continuous retraining.

Technical Architecture

Core Technology Stack & Frameworks

AI / ML Engines
PyTorchTensorFlowHugging FacevLLMOllama
Agent Frameworks
LangChainLangGraphCrewAILlamaIndexModel Context Protocol (MCP)
Vector Stores
PostgreSQL (pgvector)PineconeQdrantChromaDBMilvus
Hardware & Cloud
NVIDIA CUDAAWS SagemakerAzure AIGoogle Vertex AI
Compliance & Security Certifications
EU AI Act GovernanceSOC 2 Type II AI AuditISO 42001 AI Management SystemUAE PDPL PII ProtectionHIPAA Data Privacy

Frequently Asked Questions

Everything you need to know about implementation, compliance, and features.

We use strict Retrieval-Augmented Generation (RAG) with vector similarity thresholds, enforced schema validation, and required inline source document citations.

Ready to Partner with KreupAI Engineers?

Schedule a technical consultation to review your software architecture and project requirements.

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