SUMMARY

Titannis is developing Kara. Kara is a workflow automation platform for legacy enterprises. It runs enterprise workflows as digital tasks, the way a person would — reaching legacy back-office systems that API-dependent vendors can't touch. Banks, insurers, manufacturers, and the Mittelstand are full of these systems, where a standard API integration would be too costly to build in time, technology, and money.
Kara is not an ERP, but operates on top of one. It's an intelligent tool that automates repetitive digital tasks a human would otherwise do — in a no-touch, no-integration way, starting with FinOps: accounting reconciliation, payroll consolidation, and invoice processing. Unlike Sage, Pigment, or SAP, nothing needs to be modified or connected for Kara to work. It's a horizontal platform, technologically capable of taking on any task.

PROBLEM

Kara solves the AI last mile gap, meaning the main blockers for enterprise adoption of AI automation.
Kara is Legacy friendly — no 18-month IT project to roll out — Kara works on legacy systems exactly as they are, with no modernization required first.
Kara requires no API integrations — Kara reads the screen and operates the keyboard/mouse directly, so it deploys against any application in weeks, not months, without touching existing systems — lower OPEX, instantly.
Kara is Sovereignty by design — compliance review becomes a one-line "approved," since data never leaves the premises. Titannis builds small, proprietary AI models (under 10B parameters) that are light enough to run fully on-premises or in a private cloud.

SOLUTION

Kara allows end users to automate their most repetitive tasks by simply recording how they process an activity once. It then is able to translate the displayed workflow into system prompts and re-execute the process simply by using mouse clicks and keyboard strokes to operate the desktop and browser applications required to execute the process.
Kara allows companies to gain significant efficiency gains, as more than 30% of the work done by end users is composed of repetitive workflows. This allows companies to release significant tasks from low value activities and refocus their resources in value adding activities such as business growth.
Additionally the strategy of Titannis is to develop Small Language Models (SLMs), which enables two key gains. Firstly, it allows to keep the data of the companies local and not expose it to frontier models that are usually developed in the United States and China. Additionally by being smaller they require much lower hardware capacity for being processes, which means less stress is put on the construction of data centers which also translates to the reduction of electricity costs and CO2 emissions coming from AI usage.