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Next-Gen Enterprise Solutions

Engineering Autonomous AI Systems & Seamless Cloud Migration for Enterprise Growth

We design and deploy Agentic AI solutions and execute secure, zero-downtime cloud migrations from Microsoft Azure to Amazon Web Services and beyond — enabling innovation, cost efficiency, and scalable digital transformation.

Empowering Enterprises Across

FMCG FinTech Healthcare Manufacturing Retail SaaS
Who We Are

From Fragmented Automation to Intelligent Autonomy

We are a technology consulting firm specializing in agentic AI architecture and cloud modernization for regulated and high-growth sectors.

  • Agentic AI architecture and autonomous business systems
  • Cloud modernization and migration to AWS
  • Industry-specific AI transformation
40-70 Operational Efficiency
Zero Migration Downtime
Our Core Services

Next-Generation Technology Capabilities

Agentic AI Engineering

We design AI systems that do not just respond — they reason, decide, and act. Our autonomous agents integrate seamlessly with your ERP, CRM, and data ecosystems.

Capabilities

  • Multi-agent orchestration
  • Workflow automation with reasoning loops
  • Knowledge retrieval with vector databases
  • Secure enterprise-grade LLM deployment
  • Autonomous reporting and decision assistance
  • AI copilots for operations, finance, HR, compliance
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Cloud Migration & Modernization

Structured migration from Microsoft Azure environments, on-premise Windows Server infrastructure, and legacy SQL ecosystems to Amazon Web Services.

Framework Phases

  • Discovery: Infrastructure audit, dependency mapping
  • Strategy: Rehost, replatform, refactor
  • Execution: Containerization, CI/CD, Terraform
  • Optimization: Cost governance, auto-scaling
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Industries

Industry-Specific Transformations

Tailored Agentic AI & Cloud Architecture for your sector.

Intelligent Demand Forecasting & Orchestration

Challenge: Unpredictable demand across regions leads to overproduction, stockouts, and inefficient supply chain routing.

Solution: We engineer a multi-agent AI system that unifies Retail POS data, Distributor inventory levels, Weather API streams, and Social sentiment indices.

  • Ingestion: AWS Kinesis Data Streams for POS & Weather data
  • Storage & processing: AWS Glue ETL to Amazon S3 Data Lake
  • AI Orchestration: Amazon Bedrock Agent orchestrating SageMaker models
  • ERP Sync: Bi-directional sync with SAP/Dynamics via Amazon API Gateway
30%
Improved forecasting accuracy
Reduced warehousing cost

System Architecture

graph TD A[POS Data] -->|Kinesis| B(AWS Glue) C[Weather/Social] -->|API Gateway| B B --> D[(Amazon S3 Data Lake)] D --- E[SageMaker Models] E --> F{"Agentic AI
Bedrock"} F -->|Decisions| G[ERP/Dynamics] F -->|Alerts| H[Supply Chain App]

Cloud Migration Highlight

Migrated legacy Microsoft Dynamics ERP hosted on Azure VMs to AWS ECS + RDS with 25% infrastructure cost reduction.

🔬 PoC Highlight: Multi-Agent Supply Chain Orchestration

1. Objective

Prove that autonomous AI agents can resolve predicted stockouts by dynamically negotiating with tier-2 suppliers before human intervention is required.

2. Agentic Stack

Coordinator Agent: LangChain Router.
Data Agent: AWS Athena SQL-tool.
Action Agent: API mutator (SAP Ariba payload generator).

3. Reasoning Loop (ReAct)

Agent detects weather disruption in transit lane → Queries DB for affected SKUs → Identifies alternative local supplier → Drafts automated PO adjustment → Requests final human approval.

4. Workflow Outcomes

Achieved a 72% reduction in reaction time to supply chain anomalies, converting a 3-day manual review process into a 15-minute autonomous drafting pipeline.

Real-Time Fraud Detection System

Problem: Legacy rule-based fraud systems produce high false positives and fail to catch emerging attack vectors such as synthetic identity fraud.

Solution: A distributed, event-driven Agentic AI framework that monitors transaction streams, learns behavioral anomalies, and updates fraud scoring dynamically in real-time latency.

  • Streaming: Amazon Managed Streaming for Apache Kafka (MSK)
  • Scoring Engine: SageMaker endpoint deployed via AWS Lambda
  • Graph DB: Amazon Neptune for entity resolution
  • Resolution Agent: Auto-blocks threats or escalates via Bedrock LLM
60%
Reduction in false positives
<50ms
Transaction scoring latency

System Architecture

graph LR TX[Transaction] --> MSK[Amazon MSK] MSK --> L[Lambda Hook] L --> NEP[(Neptune Graph)] L --> SM[SageMaker Model] SM --> AG[AI Resolution Agent] AG -->|Block| CORE[Core Banking] AG -->|Escalate| REV[Human Ops Queue]

Cloud Migration Highlight

Migrated Microsoft .NET Core API stack from Azure App Services to AWS EKS for immense scalability during peak trading hours.

Clinical Decision Support Agent

Problem: Clinicians suffer burnout spending hours continuously reviewing disparate patient EHR records across different databases.

Solution: A multimodal AI agent that summarizes longitudinal medical history, extracts patterns from unstructured clinical notes, and suggests evidence-based diagnostic pathways.

  • Compliance: Fully HIPAA-aligned architecture within AWS VPC
  • Data Extraction: Amazon Textract and Comprehend Medical
  • Vector Retrieval (RAG): Amazon OpenSearch Serverless for precise context
  • Generation: Claude 3 Opus via Amazon Bedrock (Private deployment)
Reduced clinician workload

System Architecture

graph TD EHR[EHR System] -->|VPC Link| API[API Gateway] API --> T[Amazon Textract] API --> C_MED[Comprehend Medical] T --> VEC[(OpenSearch DB)] C_MED --> VEC VEC --> RAG[RAG Workflow] RAG --> LLM["Claude 3 Agent on Bedrock"] LLM --> DASH[Clinical Dashboard]

Cloud Migration Highlight

Migrated on-prem Microsoft SQL Server EHR database to AWS RDS with HIPAA-aligned config and automated backup strategy.

🔬 PoC Highlight: Autonomous Pre-Authorization Agent

1. Objective

Eliminate the friction in insurance prior-authorization by deploying an AI agent capable of reading clinician notes and mapping them directly to payer policy requirements.

2. Agentic Stack

OCR Agent: Amazon Textract + Comprehend Medical.
Policy RAG: Pinecone Vector DB.
Decision Agent: Bedrock Claude 3 Sonnet.

3. Reasoning Loop (ReAct)

Agent digests faxed clinical notes → Queries Vector DB for specific payer's CPT code rules → Extracts matching patient vitals/history → Formats a complete structured HL7/FHIR payload for payer submission.

4. Workflow Outcomes

Dropped prior-authorization rejection rates by 40% due to missing documentation. Accelerated patient treatment timeline by an average of 3.5 days.

Predictive Maintenance Cyber-Physical System

Challenge: Unplanned equipment failures cause costly assembly line halts and supply chain ripple effects.

Solution: Edge-to-cloud Agentic AI that actively monitors IIoT sensor telemetry (vibration, acoustics, temperature) to predict failure risks before they compound into downtime.

  • Ingestion: AWS IoT Core with secure MQTT brokering
  • Edge Compute: AWS IoT Greengrass for local inference
  • Time-series: Amazon Timestream for optimized metric storage
  • Autonomous Actions: AI Agent that schedules maintenance in SAP
35%
Downtime reduction
20%
Lower maintenance cost

System Architecture

graph LR MC[Industrial Machines] -->|MQTT| IoT[AWS IoT Core] IoT --> TS[(Amazon Timestream)] IoT --> GG["IoT Greengrass
Edge ML"] TS --- SM["SageMaker
Anomaly Det."] SM --> AG[Maintenance Agent] AG -->|Create Ticket| SAP[SAP Plant Maint.] GG --> AG

Cloud Migration Highlight

Migrated legacy Windows Server SCADA systems to AWS IoT Core + S3 Data Lake, reducing technical debt.

🔬 PoC Highlight: Self-Healing SCADA Infrastructure

1. Objective

Prove that Agentic AI can not only predict anomalies in robotic arm motors but also autonomously re-route production logic to bypass the failing hardware gracefully.

2. Agentic Stack

Edge Agent: AWS IoT Greengrass Lambda.
Time-Series Agent: Amazon Timestream query tool.
PLC Controller: Modbus/TCP API mutator.

3. Reasoning Loop (ReAct)

Vibration sensor detects degrading bearing → Edge Agent confirms anomaly against historical baseline → Triggers "Soft Degradation" workflow → Reprograms adjacent robotic cell to absorb 30% load → Alerts maintenance crew with exact part ID.

4. Workflow Outcomes

Demonstrated zero unplanned downtime during a simulated motor failure. Saved $120k in avoided scrapped materials during the 30-day PoC timeframe.

AI-Powered Dynamic Pricing Engine

Challenge: Static pricing models fail to capture margin opportunities in highly competitive, fast-moving retail scenarios.

Solution: An autonomous AI agent engine that continually evaluates competitor pricing fluctuations, historical demand elasticity, and current supply limits to dynamically tune prices.

  • Data Integration: Serverless scraping tasks via AWS Fargate
  • Real-time Rules: Amazon ElastiCache (Redis) for rule evaluation
  • Decision Logic: Multi-agent negotiation (Margin vs Volume)
  • Execution: GraphQL API mutators syncing to Shopify/Magento

System Architecture

graph TD COMP["Competitor Sync
AWS Fargate"] --> K[Kinesis Stream] INV[ERP Inventory] --> K K --> RED[(ElastiCache Redis)] RED --- AG[Pricing Engine Agent] AG -->|Approve Change| API[API Gateway] API --> ECOM[E-Commerce Storefront]

Cloud Migration Highlight

Migrated Magento stack from Azure VM to AWS auto-scaling environment improving Black Friday load handling by 4x.

🔬 PoC Highlight: Multi-Agent Competitor Defense

1. Objective

Develop an autonomous pricing defense mechanism that responds to aggressive flash-sales from competitors without triggering a margin-destroying race to the bottom.

2. Agentic Stack

Scraping Agent: Apify Actor / Puppeteer.
Elasticity Agent: SageMaker pricing model.
Orchestrator: AWS Step Functions + Bedrock.

3. Reasoning Loop (ReAct)

Agent spots competitor price drop → Calculates new margin cross-elasticity → Reasons: "If we drop price, we lose 15% margin; instead, bundle with overstocked item X" → Automatically pushes bundle promotion to Shopify via GraphQL.

4. Workflow Outcomes

Maintained a 22% higher gross margin during competitor promotional periods compared to legacy rule-based price-matching engines.

Enterprise AI Support Copilot

Challenge: L1 and L2 support queues get bottlenecked, leading to high resolution times and decreasing Net Promoter Scores (NPS).

Solution: A sophisticated autonomous AI agent seamlessly integrated into ticketing workflows to auto-resolve tier-1 issues, orchestrate troubleshooting, and arm human agents.

  • Event Ingestion: Amazon EventBridge listening to Zendesk/Jira webhooks
  • RAG Backend: AWS Bedrock Knowledge Bases over Confluence docs
  • Automated Action: LangChain tooling triggers remote server actions
  • Supervised Handoff: Smooth escalation mapping to human queue

System Architecture

graph LR USER[User Query] --> ZEN[Zendesk] ZEN -->|Webhook| EB[EventBridge] EB --> L[AWS Lambda] L --- KB[(Knowledge Base)] KB --- AG[Copilot Agentic Loop] AG -->|API Action| SRV[User Server Reset] AG -->|Reply/Resolve| ZEN AG -->|Escalate| HMN[L3 Human Support]

Cloud Migration Highlight

Migrated multi-tenant platform core from Azure SQL to Amazon Aurora PostgreSQL, achieving 40% improved DB performance.

🔬 PoC Highlight: AI "Ghost Developer" for DevOps

1. Objective

Deploy an Agentic AI capable of autonomously triaging, debugging, and drafting Git Pull Requests for low-level infrastructure (Terraform) alerts.

2. Agentic Stack

Log Agent: Datadog/CloudWatch API tool.
Code Agent: GitHub/GitLab API tool.
Reasoning Engine: Claude 3.5 Sonnet.

3. Reasoning Loop (ReAct)

PagerDuty alert fires (Memory leak in ECS) → Log Agent queries CloudWatch for traceback → Reasoning Engine identifies faulty deployment config → Code Agent branches repo, patches Terraform limits, and opens a PR for human review.

4. Workflow Outcomes

Achieved a Mean Time to Draft (MTTD) of 4 minutes for infrastructure fixes. Reduced senior DevOps interrupt-driven context switching by 35%.

Unrivaled Expertise

Architecting Enterprise Resilience

Focus20 is not a generic IT agency. We are a premier boutique engineering "seal team" trusted by Fortune 500 institutions. Our enterprise clients deploy us when complex, mission-critical Azure to AWS migrations stall out, or when they need to securely weave state-of-the-art Agentic AI (LLMs, RAG, ReAct) natively into their backend systems. We engineer fault-tolerant, high-concurrency systems built exclusively for vast scale.

Cloud & Infrastructure Modernization

We execute zero-downtime, highly structured migrations ensuring robust security, sub-millisecond latency, and optimal FinOps cost structures.

  • Azure to AWS Specialization: seamless transition of AKS to Amazon EKS, and SQL Server to Amazon Aurora PostgreSQL.
  • Event-Driven Microservices: Refactoring monolithic legacy apps into decoupled serverless architectures (AWS Lambda, EventBridge, SQS/SNS).
  • Multi-Region High Availability (HA): Designing active-active routing (Route53) and cross-region disaster recovery (DR) with RPO/RTO < 5 minutes.

Autonomous "Agentic" Engineering

Going beyond conversational chatbots to deploy read/write, multi-step Reasoning & Acting (ReAct) agents that execute complex business logic securely.

  • Terabyte-Scale RAG: Vector database routing (Pinecone/AWS OpenSearch) with hybrid semantic/keyword search for instant proprietary data retrieval.
  • Multi-Agent Orchestration: LangGraph and AWS Step Functions coordinating specialized AI agents (Auditor, Coder, Researcher).
  • Tool Calling & API Mutators: Securely granting LLMs the ability to read from SAP/Salesforce and execute approved database mutations.

Zero-Trust Security & Compliance

Enterprise-grade Data Loss Prevention (DLP) and strict Identity & Access Management (IAM) built natively into every environment.

  • LLM Prompt Protection: Guardrails and deterministic firewalls against prompt injections, jailbreaks, and sensitive PII leakage.
  • Zero-Trust Architecture: Micro-segmentation, AWS PrivateLink, and rigorous RBAC integration with Microsoft Entra ID / Okta.
  • Compliance Automation: Continuous auditing pipelines for SOC2 Type II, HIPAA (HealthLake), and PCI-DSS environments.

Performance & Outcome-Driven

We do not charge for "effort." We engage via fixed-bid, milestone-based, and performance-linked models tied directly to your ARR and margin improvements.

  • FinOps Optimization: Guaranteed 30-40% reduction in cloud spend through spot-instance orchestration and right-sizing.
  • Velocity Acceleration: Slashing CI/CD deployment times from hours to automated, zero-touch minutes.
  • Quantifiable ROI: Autonomous pipelines driving direct top-line revenue recovery (e.g., Focus20 Recova OS).
For Investors

Explore Our Strategic Vision

Get a high-level overview of the Focus20 thesis, our proprietary Agentic infrastructure, and the massive enterprise market opportunity we are capturing.

Request Full Pitch Deck
01

The Bottleneck

Why human-in-the-loop enterprise support cannot scale linearly with ARR.

02

Our Solution

Zero-touch, read/write autonomous agents running on secured ReAct loops.

03

Cloud Migration

Our "Trojan Horse" land-and-expand strategy via Azure to AWS migrations.

04

The Team

Engineers and architects who have built hyperscale systems at elite firms.

Insights & Research

Deep-Dive Case Studies

Explore our highly-detailed technical playbooks covering architecture, Agentic design patterns, and deployment strategies.

Core Architecture

Building Resilient Agentic Workflows

A comprehensive design pattern guide for creating self-correcting autonomous LLM agents using AWS Bedrock, LangChain, and ReAct loops.

Read Full Case Study →
Cloud Modernization

The Azure to AWS Migration Playbook

A step-by-step technical breakdown of migrating a multi-tenant enterprise .NET monolith to a highly scalable Amazon EKS architecture.

Read Playbook
Free Resources

Download Our Enterprise Playbooks

Actionable blueprints for migrating legacy systems and deploying Agentic AI architectures.

The Agentic AI Playbook

A comprehensive 24-page guide on designing, deploying, and securing autonomous Reasoning & Acting (ReAct) frameworks within enterprise SaaS and FinTech environments.

  • LangChain & LlamaIndex routing paradigms
  • Vector DB optimization for Terabyte-scale RAG
  • Defending against Prompt Injection and DPI
Download PDF (6.2 MB)

AWS Migration Blueprint

The exact technical blueprint we use to execute zero-downtime, secure migrations from legacy on-premise infrastructure and Microsoft Azure to Amazon Web Services.

  • Active Directory to AWS IAM federation mapping
  • Azure AKS to Amazon EKS container shifting
  • Microservice cost-optimization models
Download PDF (8.4 MB)

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