AI-powered software development
We build web, mobile and cloud systems in Python, JavaScript and PHP, and our AI team turns your data into working models, agents and automation. 16 years, 500+ projects delivered to acceptance.
Our engineers have delivered work for
16
Years in business
500+
Projects delivered
30+
Client countries
100%
Projects delivered to acceptance
How AI agents work
How AI agents work: the six parts of a production agent
An AI agent is not a chatbot with a longer prompt. It is a system: a model for reasoning wrapped in perception, memory, tools, orchestration and monitoring. We build agents for clients and for our own products — this is the architecture behind them.
1. Perception & input
Everything the agent will act on enters here: chat messages, emails, documents, images, API events, database rows. The layer parses, validates and routes — extracts text from PDFs, classifies intent, attaches the user, the account and the permissions — so the model sees clean, structured context instead of raw noise. A thin, strict perception layer is the difference between an agent that handles edge cases and one that hallucinates on them.
2. Reasoning & planning
The LLM is the cognitive engine: it interprets the goal, breaks it into steps, decides which tool to call next and when the task is done. Good agents plan explicitly, re-plan when a step fails and keep a budget on loops and cost. Model choice and prompt design live here — and are evaluated on a test set, not by feel.
3. Memory & knowledge
Short-term memory keeps the current conversation and intermediate results; long-term memory stores facts, preferences and past outcomes. Retrieval (RAG) connects the agent to your documents, CRM and databases through a vector index and structured queries, so answers are grounded in your data and cite their source.
4. Tools & actions
Tools are how the agent changes the world: search, read and write records in CRM/ERP, send an email, create an invoice, run code, call another agent. Each tool has a typed contract, an access scope and an audit log. Anything irreversible — payments, deletions, outbound messages — goes through a confirmation or a human approval.
5. Orchestration & guardrails
The control loop runs the agent: sequences steps, handles retries and timeouts, enforces limits on cost, time and permitted actions, coordinates several specialised agents when one is not enough. Guardrails validate inputs and outputs, filter sensitive data and stop the loop when confidence is low. This is the layer that makes agents boring and reliable.
6. Reflection, evaluation & monitoring
Before it answers, the agent checks its own output against the goal and the rules; after it answers, every run is traced — prompts, tool calls, cost, latency, outcome. An evaluation set replays real cases on every change, and production metrics (task success, escalations, corrections) feed the next iteration. Human feedback closes the loop.
What we build
Product and systems development across the stack — with AI where it moves the numbers, not for its own sake.
AI & machine learning
AI agents, LLM pipelines, forecasting, computer vision and process automation — from prototype to monitored production.
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Web development
Back ends in Python (FastAPI, Django), PHP (Laravel, Symfony) and Node.js; front ends in Vue and React. Portals, SaaS, e-commerce, internal tools.
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Cybersecurity & AI security
Penetration testing, vulnerability assessment, LLM/agent red teaming, DevSecOps and compliance readiness.
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Cloud & DevOps
AWS, GCP and Azure infrastructure, CI/CD, containers, observability and security reviews.
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Mobile development
Cross-platform and native apps that share a back end with your web product.
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Data engineering & analytics
Pipelines, warehouses, dashboards and predictive models on your own data.
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Industries
Sectors we have shipped in
Any industry — we start from your process, not from a vertical template.
Retail & e-commerce
Storefronts, marketplaces, catalogue and master data, Document AI for finance, assistants for staff and customers — for grocery chains and online retailers in the EU and US.
Fintech & banking
Payment and lending platforms, KYC and scoring, ledgers that reconcile, and AI that reads documents and flags risk — built to audit, encrypt and survive regulators.
Logistics & supply chain
TMS/WMS integrations, tracking, route and load planning, driver apps and forecasting — systems that keep moving when a carrier API times out.
Healthcare & medtech
Patient portals, clinic systems, medical imaging pipelines and research tooling — with privacy, traceability and clinical review built in.
Manufacturing & IoT
MES/ERP integrations, telemetry platforms, quality control with computer vision and predictive maintenance — from the sensor to the dashboard.
Education & e-learning
LMS platforms, course marketplaces, assessment engines and AI tutors — for schools, universities and corporate learning.
Real estate & proptech
Listing platforms, CRM for agencies, property management systems and valuation models — with maps, documents and payments in one flow.
Media, gaming & sports analytics
High-volume event platforms, ratings and statistics, community products and content pipelines — our own products live here.
Why clients stay
Four things you can count on
Senior engineers only
You talk to the people who write the code. Average experience on a Glanit team is over eight years.
Delivered to acceptance
500+ projects accepted by the client. We define "done" in writing before we start and measure ourselves against it.
AI with proof
Every model or agent ships with an evaluation set, monitoring and a before/after number the business can check.
Long-term partners
Most of our work comes from clients who have been with us for years and from their referrals.
Technology
The stack we ship with
Languages, frameworks and infrastructure we use in production — chosen per project for the team that will own it, not for fashion.
Languages
- Python
- TypeScript
- JavaScript
- PHP
- Go
- Rust
- Kotlin
- Swift
- Dart
- SQL
Back end
- FastAPI
- Django
- Laravel
- Symfony
- NestJS
- Node.js
- gRPC
- GraphQL
- Celery
- Laravel Octane
Front end & mobile
- Vue 3
- Nuxt
- React
- Next.js
- Inertia
- Tailwind CSS
- Flutter
- React Native
- SwiftUI
- Jetpack Compose
AI & data science
- PyTorch
- scikit-learn
- Transformers
- LangChain
- LlamaIndex
- OpenAI
- Anthropic
- Gemini
- Llama
- Mistral
- pgvector
- Qdrant
- pandas
- Polars
- OpenCV
Data & messaging
- PostgreSQL
- MySQL
- ClickHouse
- Redis
- Elasticsearch
- Kafka
- RabbitMQ
- MongoDB
- MinIO / S3
- Airflow
- dbt
Cloud & DevOps
- AWS
- Google Cloud
- Azure
- Docker
- Kubernetes
- Terraform
- GitHub Actions
- GitLab CI
- Prometheus
- Grafana
- OpenTelemetry
- Cloudflare
Insights
From our engineers
AI & Machine Learning ·
Why Hybrid Search Beat Pure Embeddings in Our Invoice AI Pipeline
Pure dense embeddings failed on alphanumeric serial numbers in our Invoice AI pipeline. Combining PostgreSQL tsvector full-text search with pgvector cosine distance lifted top-1 matching accuracy from 61.4% to 94.8%.
6 min read
AI & Machine Learning ·
Why pgvector Replaced Qdrant in Our LLM Feedback Pipeline
We migrated 5 million vector embeddings from a standalone Qdrant cluster to PostgreSQL 16 with pgvector 0.7. Here is how operational overhead dropped while maintaining sub-50 ms search latencies.
5 min read
AI & Machine Learning ·
How AI agents work: the architecture of a production agent
An agent is a model wrapped in perception, memory, tools, orchestration and monitoring. A practical walk through the six layers, what breaks in each one, and what we do about it.
6 min read
FAQ
Questions we hear before every project
Discuss your project
Python, JavaScript, PHP, mobile, ML, DevOps — open roles and how we hire.