AI SolutionsIntelligence, amplified.
Unlock the potential of AI for your organization. Our AI solutions leverage cutting-edge machine learning, deep learning, and generative AI to automate processes, extract insights, and create intelligent experiences. From predictive analytics to conversational AI, we help businesses harness the transformative power of artificial intelligence.
What We Build
Enterprise-grade AI solutions that transform data into actionable intelligence and automate complex processes.
Machine Learning
Custom ML models tailored to your data and business objectives. Predictive analytics that drive decisions.
Machine Learning
Custom ML models tailored to your data and business objectives. Predictive analytics that drive decisions.
Natural Language Processing
Build intelligent chatbots, sentiment analysis, and document processing systems that understand context.
Natural Language Processing
Build intelligent chatbots, sentiment analysis, and document processing systems that understand context.
Computer Vision
Image recognition, object detection, and video analytics powered by deep learning.
Computer Vision
Image recognition, object detection, and video analytics powered by deep learning.
Generative AI
LLM integration, content generation, and AI-powered creative tools using GPT, Claude, and custom models.
Generative AI
LLM integration, content generation, and AI-powered creative tools using GPT, Claude, and custom models.
Predictive Analytics
Detect anomalies, surface actionable insights, and support confident data-driven decisions.
Predictive Analytics
Detect anomalies, surface actionable insights, and support confident data-driven decisions.
AI Infrastructure
MLOps pipelines, model deployment, monitoring, and scaling for production AI systems.
AI Infrastructure
MLOps pipelines, model deployment, monitoring, and scaling for production AI systems.
Machine Learning
Custom ML models tailored to your data and business objectives. Predictive analytics that drive decisions.
AI Solutions We Deliver
Language AI
- Chatbots & Virtual Assistants
- Sentiment Analysis
- Text Summarization
- Language Translation
- Content Generation
- Document Q&A
Vision AI
- Object Detection
- Image Classification
- Facial Recognition
- Video Analytics
- OCR & Document Processing
- Quality Inspection
Decision Intelligence
- Business Insight Discovery
- Customer Behavior Analysis
- Risk Monitoring
- Anomaly Detection
- KPI Intelligence
- Operational Decision Support
Enterprise AI Operations
- Workflow Automation
- Knowledge Copilots
- Document Intelligence
- Human-in-the-loop Review
- Model Monitoring
- Responsible AI Guardrails
AI Across Industries
We apply AI to solve complex challenges across diverse industries.
Process Automation
Automate repetitive tasks with intelligent workflows that learn and improve over time.
Customer Intelligence
Understand customer behavior, predict churn, and personalize experiences at scale.
Healthcare AI
Medical image analysis, patient risk prediction, and clinical decision support systems.
Financial AI
Fraud detection, algorithmic trading, risk assessment, and regulatory compliance.
Retail & E-commerce
Product recommendations, customer journey personalization, and intelligent merchandising.
Manufacturing
Quality control, assistant-led operations, and AI-supported production visibility.
AI Solutions for Real Business Problems: What Works and What Doesn't
Artificial intelligence is no longer exclusive to Big Tech. With the right implementation and integration, companies of every size, from mid-market businesses to large enterprises, can extract measurable efficiency gains from AI. At Makrops, we build production-ready solutions across generative AI, large language models (LLMs), machine learning, and computer vision, focused on deployment, not demos.
Which Business Problems Are Well-Suited for AI?
Document processing and data extraction, customer service automation, demand forecasting, quality control, recommendation systems, and anomaly detection are among the highest-ROI AI application areas. That said, not every problem needs AI: some situations are better solved with simpler rule-based automation or a structured analytics dashboard. We help you distinguish between the two before any code is written.
LLM Integration and Custom Model Development
Integrating large language models like GPT, Claude, and Gemini into existing workflows, when configured properly, delivers dramatic productivity gains. Using RAG (Retrieval-Augmented Generation) architecture, we connect company-internal knowledge bases to LLMs, building assistants that produce accurate, contextually grounded responses. For scenarios requiring custom fine-tuning, we develop models trained on domain-specific data.
The most critical success factor in AI projects is data quality and integration realism. Makrops begins every AI engagement with a technical audit of existing systems and data pipelines. Models validated in staging are deployed to production with monitoring and retraining pipelines in place, because an AI system that degrades silently is worse than no AI at all.
Frequently Asked Questions About AI Solutions
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Enterprise AI solutions, LLM integrations and RAG architecture
Key insights on enterprise Large Language Models, RAG vector architectures, autonomous AI agents, data privacy, and ROI metrics.
Which enterprise AI use cases deliver the highest ROI in 2026?
Top-performing AI use cases: 1) RAG knowledge assistants querying internal docs, 2) Automated customer support copilots deflecting 50%+ of tickets, 3) Autonomous AI Agents executing multi-step business workflows, 4) Automated document parsing for legal/finance, 5) E-commerce semantic search engines.
What is RAG (Retrieval-Augmented Generation) and why is it essential?
RAG connects LLMs directly to your private enterprise databases and documents. Instead of hallucinating from public data, the model retrieves verified internal facts in real time, eliminating factual errors while preserving confidentiality.
Is our proprietary company data protected from public AI training?
Yes. We use enterprise API agreements (Azure OpenAI, AWS Bedrock, GCP Vertex) that contractually prohibit using client data for model training. For strict security, we deploy open-source models (Llama, Mistral) in air-gapped on-premise setups.
What is the typical development timeline for an enterprise LLM/RAG project?
Technical feasibility & PoC: 2–4 weeks. Production-ready MVP: 6–8 weeks. Full enterprise-scale deployment with CRM/ERP integrations: 10–16 weeks.
What are typical enterprise AI budget benchmarks in 2026?
Discovery & PoC validation: $5,000–$20,000. Production RAG knowledge assistants: $20,000–$80,000. Multi-agent autonomous workflow systems: $80,000–$250,000+.
Which LLMs and AI infrastructure frameworks do you support?
We build model-agnostic architectures across OpenAI (GPT-4o, o1), Anthropic (Claude 3.5), Google (Gemini 1.5), Meta (Llama 3), and Mistral, orchestrating with LangChain, LangGraph, LlamaIndex, Pinecone, Qdrant, Weaviate, and pgvector.
What is an AI Agent and how does it automate enterprise workflows?
An AI Agent is an autonomous system capable of breaking down high-level business goals, formulating action plans, querying databases, executing APIs, and validating results with human-in-the-loop oversight.
RAG vs Fine-Tuning: Which is right for our organization?
RAG is faster, cheaper, and always up-to-date for internal Q&A and knowledge retrieval. Fine-tuning is reserved for teaching models unique stylistic tone, specialized syntax, or proprietary code formats.
Do our internal documents need to be pre-cleaned before kickoff?
No. We build automated data ingestion, chunking, and embedding pipelines to normalize legacy PDFs, Word files, and SQL databases into vector indexes.
How is GDPR, KVKK, and Data Residency compliance guaranteed?
We apply automated PII redaction and deploy across European (Frankfurt), US, or on-premise regional clusters with end-to-end encryption.
How do you evaluate and benchmark AI output accuracy?
We deploy automated evaluation frameworks (Ragas, TruLens) and human-in-the-loop test suites to track hallucination rates, latency, and token economics.
Can the AI integrate into our existing CRM, ERP, and ticketing tools?
Yes. We build bidirectional integrations with Salesforce, HubSpot, SAP, Zendesk, Jira, and custom REST/GraphQL systems.
What are typical monthly model API token costs?
Internal enterprise assistants typically incur $50 to $500/month in API tokens. For high-volume applications, self-hosted open-source models keep operational costs predictable.
Do you develop Voice AI and contact center automation?
Yes. We build real-time voice agents combining Whisper, ElevenLabs, and low-latency LLM streaming for automated telephony customer service.
Do clients receive full IP and source code ownership for AI pipelines?
Yes. All prompt templates, orchestration code, RAG embeddings, vector configurations, and API bridges are 100% transferred upon delivery.
How do we launch an enterprise AI project with Makrops?
Schedule an AI discovery call at calendar.makrops.com/info/gorusme to evaluate your use cases with our AI research and engineering squad.