We Build Production Multi-Agent Swarms and High-Recall RAG Systems
We design, fine-tune, and deploy deterministic multi-agent swarms and hybrid vector search infrastructure using LangGraph, vLLM, and Qdrant for engineering teams in regulated enterprise environments.
PROVED IN PRODUCTION BY LEADING ENTERPRISE ENGINEERING TEAMS
Production-Tested Solution Blueprints
Audited architectural blueprints engineered for latency, precision, and privacy in enterprise environments.
Enterprise RAG & Hybrid Vector Retrieval
pgvector + Qdrant hybrid search with Cohere reranking for ultra-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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Proprietary Enterprise LLM Performance Benchmark
Audit dataset compiled from 4.2M enterprise requests over 90 days across 12 production clusters.
| Architecture Strategy | Model Split | Median Latency (p50 / p95) | Verification Status |
|---|---|---|---|
| Single Frontier Model (Baseline) | 100% Direct Frontier LLM | 1,120ms / 2,450ms | Baseline Standard |
| Naïve Vector RAG (No Rerank) | 100% Vector Search + LLM | 840ms / 1,680ms | Un-optimized |
| Distilled Swarm Router (Esaholic) | 68% Fine-Tuned SLM / 32% Frontier | 340ms / 580ms | ✓ SLA Verified |
24 Pillar AI Engineering Services
Comprehensive AI architecture, autonomous swarm development, model quantization, and enterprise governance.
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.→
Engineering & Talent
- Web App Development High-performance web applications.→
- Mobile App Development iOS & Android AI app development.→
- Cloud & AI Infrastructure AWS, Azure & GCP cloud architecture.→
- Enterprise IT Consulting Modernization & architecture advisory.→
- IT Staff Augmentation Vetted AI engineers & ML architects.→
SOC 2 Type II, ISO 27001 & EU AI Act Guardrails
Every deployment includes deterministic guardrail validation, zero-data-retention options, and full lineage tracing.
SOC 2 Type II & ISO 27001
Security StandardAudited cloud infrastructure controls, end-to-end TLS 1.3 encryption at rest and in transit, and continuous access logging.
EU AI Act & ISO 42001
AI GovernanceAutomated AI Risk Classification matrices, model cards, lineage documentation, and bias testing frameworks.
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
A 4-step engineering pipeline designed to de-risk AI integration and guarantee latency SLAs.
Technical Audit & Feasibility
Days 1–345-minute technical audit under NDA evaluating data pipelines, schema requirements, context window limits, and security posture.
14-Day Proof of Concept (PoC)
Days 4–14Rapid prototype build on isolated test bench to benchmark dense/sparse recall rates, multi-agent state transitions, and p95 latency targets.
Hardening & Guardrails
Weeks 3–5Implementing Pydantic state persistence, fallback model routing, prompt injection defenses, and zero-data-retention compliance guardrails.
Production VPC Deployment
Weeks 6+Zero-downtime containerized deployment to private AWS/Azure/GCP VPC or bare-metal GPU clusters 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.
Dr. Marcus Vance
Principal MLOps Engineer
Specialist in distributed LLM training, vLLM serving, and GPU cluster optimization.
Elena Rostova
Lead Autonomous Agent Architect
Expert in multi-agent swarm orchestration, MCP servers, and state graph design.
Predictable Project & Retainer Investment
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 is your typical engagement lifecycle? +
Engagements begin with a 45-minute technical audit under NDA. We then build a 14-day fixed-scope proof of concept to validate accuracy and latency SLAs before production deployment.
How do you handle data privacy and security? +
We implement Zero Data Retention (ZDR) agreements, self-hosted open-source models, or private VPC deployments. Your proprietary data never trains external vendor LLMs.
Do you offer post-deployment maintenance? +
Yes, we provide 24/7 SLA monitoring, automated model drift detection, and continuous vector database indexing under dedicated retainer agreements.
Can you deploy open-source LLMs on our private infrastructure? +
Yes, we specialise in deploying Llama 3, DeepSeek, and Mistral models on AWS Bedrock, GCP Vertex AI, or bare-metal GPU clusters with vLLM.
How do you control and optimize LLM token costs? +
We build intelligent routing pipelines that route simple requests to distilled small language models and high-complexity requests to frontier LLMs, cutting spend by 40% to 60%.
What frameworks do you use for autonomous multi-agent swarms? +
We build production agent swarms using LangGraph, Model Context Protocol (MCP), and PydanticAI for deterministic state transitions.
Are your deployments compliant with the EU AI Act and ISO 42001? +
Yes, we build automated model cards, risk classification matrices, prompt injection defenses, and compliance audit trails.
How long does a typical enterprise project take? +
Proof-of-Concept builds ship in 2 weeks. Full enterprise production deployments typically range from 6 to 12 weeks.
Ready to Scale Your Enterprise AI Infrastructure?
Schedule a 30-minute technical architecture review with our principal AI engineers. No sales reps, only code and systems.