Enterprise AI Engineering for Production-Ready AI Systems
We design, build, and deploy production-ready AI systems for enterprises—from AI agents and RAG platforms to LLM infrastructure, automation, and secure AI solutions.
Engineering blueprints, architectural patterns, and transparent technical evaluation.
Built on the enterprise AI stack
We design and deploy on the platforms and frameworks our clients already run.
Product and company names are trademarks of their respective owners. Their inclusion does not imply partnership, endorsement, or affiliation.
Production-Tested Solution Blueprints
Explore production architectures designed for reliable AI systems, enterprise data retrieval, and scalable AI workloads.
Enterprise RAG & Hybrid Vector Retrieval
pgvector + Qdrant hybrid search with Cohere reranking for high-recall enterprise document retrieval.
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Autonomous LangGraph Swarm Orchestration
Deterministic state machine agent swarms with human-in-the-loop validation and recovery boundaries.
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vLLM Inference & Model Quantization
Private GPU cluster deployment with PagedAttention and AWQ 4-bit model quantization.
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How We Benchmark AI Architectures
We evaluate model routing, retrieval pipelines, and agent state machines through rigorous, reproducible engineering methodologies rather than relying on generic vendor claims.
Measuring time-to-first-token (TTFT), inter-token latency, and GPU saturation under varying batch sizes and concurrent request loads to identify hardware bottlenecks before deployment.
Evaluating dense vs sparse vector search, Reciprocal Rank Fusion (RRF), cross-encoder reranking, and chunking boundaries against domain-specific test corpora to eliminate retrieval errors.
Benchmarking small language model (SLM) triage against frontier LLMs, analyzing prefix caching hit rates, and optimizing cost-performance tradeoffs for high-volume pipelines.
Testing structured Pydantic schema validation, tool execution authorization contracts, hallucination detection layers, and human-in-the-loop escalation paths for mission-critical workflows.
Explore Our Complete Engineering Methodology
Learn how our 4-phase delivery process moves from feasibility audits to production deployment.
Core AI Engineering Services We Offer
From AI agents and generative AI to machine learning, infrastructure, security, and software engineering, we provide end-to-end services for building and scaling production-ready systems.
Machine Learning, Vision, NLP & Data
- Machine Learning Development Custom predictive models & deep learning.→
- MLOps & LLMOps vLLM inference, serving & model routing.→
- Computer Vision Image recognition & document OCR pipelines.→
- Natural Language Processing Named entity extraction & sentiment analysis.→
- AI Data Engineering Vector database ETL & data pipelines.→
Enterprise AI Security & Compliance
Security, privacy, governance, and responsible AI practices are considered throughout the design and deployment of enterprise AI systems.
Security & Infrastructure Controls
Security StandardCloud infrastructure controls, end-to-end TLS 1.3 encryption at rest and in transit, and continuous access logging.
AI Governance & Risk Alignment
AI GovernanceAI risk classification frameworks, model cards, lineage documentation, and bias testing protocols.
Zero Data Retention (ZDR)
Data SovereigntyPrivate VPC deployments on AWS Bedrock, GCP Vertex, or on-premise bare metal GPU nodes ensuring customer data never trains vendor models.
Deterministic Schema Guardrails
Execution SafetyPydantic & Zod schema boundaries preventing prompt injection, hallucinated fields, and unhandled agent exceptions.
Production Deployment Methodology
From technical discovery and architecture to testing, deployment, and ongoing optimization, we build AI systems around measurable production requirements.
Technical AI Audit & Feasibility
Days 1–345-minute technical audit under NDA evaluating data pipelines, schema requirements, context window limits, and security posture.
Architecture & Engineering
Days 4–14System design, model selection, hybrid retrieval architecture, and state graph specification.
Testing & Optimization
Weeks 3–5Implementing schema guardrails, fallback model routing, latency tuning, and security evaluations.
Production Deployment & Monitoring
Weeks 6+Zero-downtime containerized VPC deployment with 24/7 SLA telemetry monitoring.
Architects Behind the Infrastructure
Umar Abbas
Principal AI Architect
Ex-FAANG Machine Learning Infrastructure Lead. Specialist in LangGraph swarm orchestration and vLLM inference optimization.
Ahmad Sultan
Principal MLOps & Infrastructure Engineer
Specialist in distributed systems, high-throughput model serving, and scalable cloud infrastructure.
Danish Mustafa
Lead Autonomous Agent Architect & Applied AI Engineer
Expert in multi-agent swarm orchestration, MCP servers, and LangGraph workflow design.
Amir Iqbal
Director of AI Security & Governance
Oversees prompt injection defenses, red-teaming audits, and ISO 42001 compliance frameworks.
Predictable Project & Retainer Investment
Choose an engagement model based on your project scope, engineering requirements, and long-term AI roadmap.
Fixed-Scope Architecture SOW
Milestone-based delivery with strict SLA guarantees.
Dedicated AI Engineering Pod
Full-time senior AI engineers integrated into your sprint workflow.
Fractional AI CTO & Advisory
Weekly executive strategy, PR reviews, and compliance oversight.
Frequently Asked Engineering Questions
What AI engineering services does Esaholic provide? +
Esaholic provides end-to-end AI engineering including autonomous AI agents, enterprise RAG systems, LLM fine-tuning, vLLM inference infrastructure, MLOps, AI security guardrails, and custom software integration.
Can Esaholic build production-ready AI agents? +
Yes, we architect deterministic, stateful multi-agent systems using frameworks like LangGraph and PydanticAI with human-in-the-loop controls and schema guardrails.
Do you develop enterprise RAG systems? +
Yes, we build high-recall RAG pipelines using hybrid vector search (Qdrant, pgvector), document parsing, and Cohere reranking for enterprise data retrieval.
Can you integrate AI with our existing software and data? +
Yes, we connect AI models and agents to existing enterprise APIs, databases, ERPs, CRMs, and custom software using Model Context Protocol (MCP) and secure microservices.
Can you deploy AI in a private cloud or VPC? +
Yes, we deploy self-hosted open-weights models and private LLM infrastructure directly into your AWS, Azure, GCP VPC, or on-premise GPU clusters with Zero Data Retention.
How do you secure enterprise AI systems? +
We implement deterministic output validation, NeMo / Pydantic schema guardrails, prompt injection defenses, role-based access control, and comprehensive telemetry logging.
How long does an AI development project take? +
A proof of concept (PoC) typically takes 2 weeks, while full enterprise production deployments range from 6 to 12 weeks depending on scope and integration requirements.
How do you measure AI system performance? +
We evaluate AI systems across p50/p95 response latency, retrieval precision/recall rates, schema execution reliability, and token cost efficiency using automated benchmarks.
Do you provide ongoing AI infrastructure and support? +
Yes, we offer dedicated retainer agreements providing 24/7 SLA telemetry monitoring, model drift detection, vector index maintenance, and continuous optimization.
How can we start an AI architecture project? +
You can begin by booking an AI Architecture Audit. We conduct a technical review under NDA to assess your data, SLAs, and requirements before defining a fixed-scope deliverable.
Ready to Build or Scale Your Enterprise AI System?
Tell us what you're building, what you're trying to improve, and where you're facing technical challenges. We'll help you define the right architecture and next step.