What is Ascentic, and what does the company do?

Ascentic is a Sri Lanka-based IT company founded in 2017 by two Swedes. We specialize in helping Swedish businesses find top IT talent while building a thriving workplace for tech professionals in Sri Lanka.

How do we know which AI features are worth building?

Ascentic helps you identify where AI saves your users time or adds business value, and where its limits are, then tests the idea with a working prototype before you invest further.

Is it safe to use AI with sensitive or regulated data?

Yes. Ascentic designs AI features with GDPR in mind, controls what data reaches the model, and has experience in regulated domains such as healthcare and financial services.

Can Ascentic build AI features like chatbots or assistants into our existing product?

Ascentic develops and integrates AI features such as chatbots, assistants and agentic features into new and existing products, and builds custom models when off-the-shelf AI isn’t enough.

AI features, built and integrated into your product

Ascentic develops intelligent features like agents, chatbots and assistants for SaaS and software platforms. We build AI on your data and workflows to automate tasks and improve user experiences, developing custom models where needed.

Let’s discuss your AI ideas

A smartphone on a white platform, connected by glowing lines to a floating AI model

What it is

AI feature development and integration adds capabilities such as conversational assistance, intelligent search and task automation to software products. It connects AI models with your application, data and business logic to help users interact with and make more use of their data.

What we offer

Everything you need to build reliable AI features into your product

AI feature development and integration
We build chatbots, assistants and AI agents into your product, connecting models with your data to deliver smarter userflows.
Custom model development
We develop and train models on your data to solve specific challenges, from forecasting to classification and anomaly detection.
Data engineering for AI
We build data pipelines that collect, clean and structure your data, creating a reliable foundation for AI features and model training.
AI quality assurance and testing
We test AI features for accuracy, reliability and safety, using automation to monitor quality as your product evolves.

Case studies

Recent examples of how we help our customers build, ship and scale

Qvalia
From consultancy to AI-powered SaaS
How Ascentic helped Swedish fintech Qvalia transform from a transaction management consultancy into an AI-powered SaaS platform - automating invoicing, AP/AR, and order management for 10,000+ companies across 35+ countries.
- FinTech
- Python
- AngularJs
- Node Js
- React Native
- AWS
- Amazon Aurora
- AWS Sage Maker
- Amazon Bedrock
Read case study : From consultancy to AI-powered SaaS
Hubert
Revolutionizing High Volume Recruitments using AI
Ascentic worked with Hubert on a cutting-edge platform designed to streamline and optimize high volume recruitment through the power of artificial intelligence.
- Recruitment
- React
- Google Cloud Platform
- Tailwind CSS
- Node Js
Read case study : Revolutionizing High Volume Recruitments using AI
FinTech client
A Taxonomical Classification of FinTech Data through Deep Learning based NLP
FinTech has been at the forefront of using machine learning to streamline business processes. A recent case study involved developing a product classification system for one of our FinTech clients.
- FinTech
- AWS Sage Maker
- Node Js
- Seaborn
- Hugging Face
- PyTorch
Read case study : A Taxonomical Classification of FinTech Data through Deep Learning based NLP

How it works

Step 1
Discover
We identify where AI features like assistants or agents would save your users time and add real business value.
Step 2
Prototype
We build a working version with your real data to test the feature quickly, before investing further.
Step 3
Integrate
We integrate it into your product and existing systems, secure, scalable and with the right guardrails.
Step 4
Improve
We monitor usage and accuracy, and keep improving the feature and its models over time.