Agentic AI & Intelligent Automation for Real Business Systems
We design and build AI-powered systems that work reliably in production. Our approach combines LLM-based intelligence with deterministic logic, strict validation, and operational safeguards.
Production-ready AI systems - not experiments
Start with a free consultationPlumPix AI development core capabilities
LLM-powered
systems
Structured generation, validation, normalization, and decision support using large language models with strong guardrails.
1Agentic AI & workflow
automation
AI-driven agents that orchestrate background jobs, assist users, and automate internal workflows inside real systems.
2AI-ready data
pipelines
Reliable ingestion, normalization, and validation pipelines designed to support AI-powered processing and analytics.
3Key principles
Production-grade
LLM integration
Structured outputs
with strict validation
Deterministic
fallback logic
Asynchronous
background workflows
Monitoring, observability,
and auditability
Predictable AI
cost control
AI services we deliver
AI-powered data
normalization pipelines
Transform unstructured or inconsistent data into reliable, structured formats suitable for search, analytics, and automation.
1Intelligent search
& query understanding
Process complex free-text queries using AI-assisted intent extraction combined with deterministic matching and relevance scoring.
2LLM-assisted
decision support
Copilots and admin tools that help users analyze data, trigger workflows, and make informed decisions — with guardrails and validation in place.
3Background AI
workflows & automation
Queue-based, asynchronous AI processing designed for scale, reliability, and controlled execution.
4AI integration into
existing systems
Safe and predictable LLM integration within APIs, backoffice tools, data pipelines, and internal platforms.
5Our approach to production AI
Strict schemas & output validation
All AI-generated outputs are validated against predefined contracts to ensure consistency and correctness.
Deterministic fallback mechanisms
Non-AI logic guarantees stable system behavior when AI services are unavailable or return low-confidence results.
Asynchronous execution
AI processing runs in background jobs to avoid blocking critical user flows.
Cost & rate control
Controlled request rates, batching, and monitoring keep AI usage predictable and cost-efficient.
Full audit & traceability
AI inputs, outputs, and intermediate states are logged and stored for transparency and future analysis.
We design AI systems so they enhance reliability — not compromise it. AI is never a single point of failure.
Real AI case studies
Open Data Aggregation Service for Entrepreneurs, Legal Entities & Tenders
An open data aggregation service that consolidates multiple public registries into a unified, searchable platform. The system normalizes heterogeneous datasets, preserves data provenance and update history, and enables reliable cross-linking between entrepreneurs, legal entities, and related tenders.
Admin Panels & Backoffice Suite for Internal Operations
A permission-driven admin and backoffice suite built for high-risk internal operations, where control, safety, and auditability are critical. The system enforces business rules server-side, supports role-based workflows, and provides clear visibility into actions, statuses, and execution history.
Smart Search System with AI-assisted Query Understanding
A smart search system built for real-world free-text queries, combining deterministic relevance modeling with AI-assisted query understanding. The solution handles typos, fuzzy matches, and mixed-language input while maintaining predictable behavior, stable relevance, and high performance at scale.
Retail Scraping & Ingestion System for E-commerce Data Collection
The system is designed for reliable extraction and ingestion of e-commerce product and promotional data. It supports multiple pagination strategies, operates through a managed proxy pool, and ensures stable data collection at scale. The goal is to provide consistent, observable, and fault-tolerant retail data pipelines.
AI-powered SKU Normalization Pipeline for E-commerce Operations
The platform is designed to automatically normalize and standardize product data at scale. It transforms raw, inconsistent SKU titles and attributes into structured, high-quality product information, ensuring consistency across catalogs. The goal is to improve data accuracy, search relevance, and operational efficiency for e-commerce systems.
Build AI you can rely on
Want to integrate AI into real, production systems and not just run experiments? We can help. Let’s discuss your use case.
Iryna
Client Manager
Our AI development process
Discovery & feasibility
We analyze data quality, workflows, constraints, and failure scenarios to determine where AI adds real value — and where it doesn’t.
Architecture & guardrails
AI is designed as part of a larger system: schemas, validation rules, fallbacks, queues, and monitoring are defined upfront.
Implementation & integration
AI components are integrated into existing services, APIs, and workflows with strict output contracts and error handling.
Stabilization & monitoring
We introduce observability, auditing, and cost controls to ensure predictable behavior in production.
Our tech stack includes
React
Next.js
Astro
TypeScript
JavaScript
Vue
Redux
Zustand
Tailwind CSS
Sass/SCSS
Bootstrap
Material UI
WebGL
Three.js
Framer Motion
GSAP
Alpine.js
Laravel
php
Symfony
Node.js
Express.js
Python
FastAPI
Django
REST APIs
GraphQL
Blade / HTML
Laravel Horizon
MySQL
PostgreSQL
Redis
Firestore
MongoDB
Docker
CI/CD
Git
Vercel
Netlify
AWS
GCP
Azure
WordPress
WooCommerce
Magento
OpenAI API
Auth