Artificial Intelligence

AI Integration & Machine Learning

We help you ship AI features that users trust — not demos that break in production. From LLM-powered assistants to custom ML models, Spygar integrates intelligence into your existing web apps, mobile products, CRM, ERP, and workflows.

Capabilities

What we build with AI

Whether you need a customer support copilot, automated document processing, demand forecasting, or recommendation engines, we design the architecture, train or fine-tune models, and deploy with monitoring, guardrails, and clear ROI metrics.

LLM & chatbot integration

GPT, Claude, and open-source models wired into your app with RAG, tool calling, and human handoff.

Document & data AI

Extract, classify, and summarize invoices, contracts, forms, and support tickets at scale.

Predictive analytics

Forecast sales, churn, inventory, and maintenance needs with models tuned to your data.

Computer vision

Image classification, OCR, quality inspection, and visual search for retail and operations.

Workflow automation

AI agents that route tasks, draft responses, and trigger actions across CRM, ERP, and email.

MLOps & governance

Versioning, monitoring, drift detection, and privacy controls for production AI systems.

Use cases

Common use cases

Practical AI features we deliver for startups, schools, ecommerce, and enterprise teams.

  • Customer support copilots with your knowledge base
  • Lead scoring and sales forecasting in CRM
  • Intelligent search across products and documents
  • Automated report generation and insights
  • Personalized recommendations in ecommerce
  • School ERP analytics and student performance insights

Technology we use

OpenAI / Azure OpenAI Anthropic Claude Python & FastAPI TensorFlow / PyTorch LangChain & vector DBs AWS / GCP ML services Laravel & Node backends React / Vue dashboards

We pick models and infrastructure based on your budget, latency needs, and data privacy requirements — cloud APIs, private deployments, or hybrid setups.

Process

How we deliver AI projects

Structured phases so you see progress early and avoid open-ended experimentation.

01

Discovery

Define the business problem, data sources, success metrics, and compliance constraints.

02

Prototype

Build a focused POC with real data to validate accuracy, latency, and user experience.

03

Integrate

Embed AI into your product via APIs, admin tools, and secure authentication.

04

Deploy & improve

Monitor usage, retrain on feedback, and expand capabilities in measured phases.

Ready to add AI to your product?

Tell us your use case — we'll recommend the right approach and share a clear scope, timeline, and estimate.