AI Application Layer Engineering

The infrastructure between AI models and real business outcomes

We design and build the application layer that makes AI operational — integrating intelligence directly into workflows, data pipelines, and decision systems for organizations that need it to actually work.

85%reduction in monitoring time
Weeksnot months, to production
100%codebase ownership
Security & Surveillance
Live
CAM 1
CAM 2
Person 94%
CAM 3
CAM 4
Motion detected · Zone 3 · 2s ago
Technical depth

The right techniquefor the right problem.

We don't apply AI generically. Every system we build uses the approach that fits the problem — chosen for precision, not trend.

RAG + Vector Search
Your knowledge base changes and needs to stay current inside the AI
Fine-tuning
A general model isn't precise enough for your domain or terminology
Semantic Search
Users need to find things by meaning and context, not just keywords
LLM Orchestration · Production pattern
1"color:#475569"># Agent with "color:#93c5fd">memory + tool use
2"color:#93c5fd">agent = "color:#c4b5fd">build_agent(
3 "color:#93c5fd">llm="color:#c4b5fd">ChatOpenAI(model="gpt-4o"),
4 "color:#93c5fd">tools=[search_tool, db_query_tool, alert_tool],
5 "color:#93c5fd">memory="color:#c4b5fd">ConversationSummaryMemory(),
6 "color:#93c5fd">system_prompt="color:#c4b5fd">load_system_prompt("ops-">agent"),
7)
8"color:#93c5fd">result = "color:#93c5fd">agent.invoke({"input": user_query})
Models
GPT-4o
Claude
Llama 3
Mistral
Custom fine-tuned
Vision
YOLO v8
OpenCV
Custom pipelines
Orchestration
LangChain
Custom agent frameworks
RAG pipelines
Backend
Django
FastAPI
Node.js
Data
PostgreSQL
Redis
pgvector
Pinecone
Infrastructure
Docker
AWS
DigitalOcean
CI/CD
Applied AI

What AI integration looks like in business automation

Before
Calendar App
Email
Spreadsheet
Booking Tool

Missed leads · Disconnected data · No visibility

After — AI integrated
All Data SourcesSetosys AI LayerAuto CRMWebsite + BookingAuto Follow-upZero Missed Leads
One platform replacing six disconnected tools

See your industry here?

Tell us about your system
How we work

Systematic. Owned. Operational.

Every engagement follows a disciplined process — designed to deliver working systems, not status updates.

01

Understand the System

We start by mapping your existing environment — data sources, workflows, infrastructure, and the specific outcomes that matter. No assumptions, no generic frameworks.

  • Stakeholder sessions to align on real objectives
  • Existing architecture and data landscape review
  • Opportunity identification with clear prioritization
02

Engineer the Layer

We design and build the AI application layer — integrating intelligence directly into your workflows, APIs, and data paths. Iterative, observable, and built to last.

  • Architecture designed for your stack and scale
  • AI model integration with production-grade reliability
  • Full-stack development with continuous quality assurance
03

Deploy, Operate, Evolve

We deploy to your infrastructure, hand over full ownership, and support as you scale. The system is yours — we make sure it stays excellent.

  • Deployment to your cloud environment with full credentials
  • Monitoring, observability, and performance baselines
  • Ongoing support and capability expansion as you grow

Every engagement ends with a complete handover — source code, infrastructure, credentials. We don't create dependencies.

Systems We've Shipped

Production platforms built for real environments, real complexity, and real outcomes.

Security Infrastructure

Enterprise AI surveillance platform replacing legacy NVR systems

AI-Powered Video Management System

The Challenge

A security-focused enterprise needed to move beyond passive recording — integrating AI detection across cameras from Axis, Sony, and other hardware vendors, each with different APIs and data formats.

What We Built

We built a custom NVR/VMS platform with hardware-agnostic camera integration, native support for vendor AI where available, and custom YOLO-based detection pipelines where it wasn't. Full-stack, containerized, and deployed on client infrastructure.

Outcomes

  • Hardware-agnostic integration across major IP camera vendors
  • Real-time object detection and recognition in production
  • Custom ML pipeline where vendor AI was unavailable
  • Fully client-owned — code, infrastructure, credentials
Data Operations

Multi-source ML platform for forecasting, detection, and process optimization

Data Intelligence Platform

The Challenge

An organization with fragmented data sources needed a unified platform to run machine learning models across their operations — without rebuilding their existing data infrastructure.

What We Built

We designed and built a platform with connectors for diverse data sources, ML model execution pipelines, and interactive visualization. Forecasting, anomaly detection, and process optimization running in a single system.

Outcomes

  • Unified ingestion from multiple disparate data sources
  • Forecasting and optimization models in production use
  • Interactive dashboards for operational decision-making
  • Modular architecture allowing new models without rebuilding
Intelligent Platforms

AI business operating system — CRM, website, assistant in one platform

AI Business Management Platform

The Challenge

Small service businesses were running on disconnected tools — separate booking systems, no CRM, no web presence — with no technical capacity to integrate them.

What We Built

We built a platform where a business's AI assistant knows everything about their operation, manages client relationships, runs their booking and website widgets, and handles day-to-day communication — all from a single system.

Outcomes

  • AI assistant with full business context and memory
  • Integrated CRM with client history and communication
  • Website builder with booking, chatbot, and lead capture
  • Single platform replacing 4–6 disconnected tools
About Setosys

We build AI-powered systems that operate in the real world — not in demos.

AI Models
Application Layer
Business Operations

Where Setosys operates

We build platforms, not prototypes.

Every system we've shipped runs in production. The AI surveillance platform handles live camera feeds. The data intelligence platform runs forecasting models on real operational data. The business OS manages real client relationships. We don't experiment on your budget.

The application layer is where AI gets hard.

Anyone can call an API. The difficult work is integrating AI reliably into existing workflows, data environments, and operational systems — with the security, observability, and maintainability that production demands. That's where we operate.

You own everything.

Every engagement ends with a complete handover — source code, infrastructure, credentials, documentation. We don't hold anything back and we don't create dependencies. Our value is in what we build, not in keeping you reliant on us.

Start a conversation

Tell us about your system.

Our AI assistant will understand your challenge and connect you with our team. If it sounds like a fit, we'll talk.

If you'd prefer email: info@setosys.com