Amaryllis Solutions
AI AGENTS & BUSINESS AUTOMATION

AI agents.
Real-world work.

We build AI agents for business workflows, connect them to your systems and prepare legacy applications for AI.

AI AGENTS / INTEGRATION / MODERNIZATION

AI agentsProcess automationAI integrationLegacy → AI-ready
01 / EXPERTISE

From a business need
to AI that works.

Automate workflows. Connect AI to data and tools. Modernize the systems your business depends on.

Supporting engineering expertise.NET development ↗AI test automation ↗
02 / DELIVERY

From a first use case
to controlled automation.

Start with a focused pilot. Evaluate quality, costs and human involvement before expanding automation.

01

Select the workflow

Identify an owner, the current cost of work and criteria for a completed task.

02

Prepare integrations

Review data, APIs and permissions. Define allowed actions and exception handling.

03

Evaluate the pilot

Test against a separate evaluation set. Measure quality, time and manual corrections.

04

Launch and evolve

Add logs, limits and monitoring. Expand the scope based on evaluated results.

03 / TECHNOLOGY

Tools that fit the task.
Architecture built to evolve.

Technology choices reflect your infrastructure, workload and the capabilities of your team.

Backend & cloud

C#.NETASP.NET CoreAzureSQL ServerEntity FrameworkDocker

Quality & AI

PlaywrightSeleniumPythonOpenAI APIClaude APIpytest

Integration & delivery

RESTGraphQLAzure DevOpsGitHub ActionsGit
Illustration of development, testing and AI in a connected technology ecosystem
HUMAN EXPERTISE. AI CAPABILITY.

AI with engineering
accountability.

Useful automation starts with a defined task. We scope data access, evaluate response quality and include human review for consequential actions.

  • Scoped permissions and actions
  • Evaluation against your scenarios
  • Logging and failure handling
Explore this service ↗
04 / INSIGHTS

Before the first
technical conversation

A practical project brief covering goals, integrations, quality and constraints. Make your first discussion more productive.

Read the guide ↗
PROJECT BRIEF / 01

Better questions.
Better projects.

01Goals and users
02Systems and integrations
03Acceptance criteria
05 / FAQ

Questions before we start

Where should an AI agent implementation start?

With one repeatable workflow, an outcome owner and representative examples. Define data sources, permitted actions and acceptance criteria before development.

How is AI integration different from agent development?

Integration connects the model to data, an application and tools. An agent uses those connections to execute a workflow. An AI feature, such as document classification, does not necessarily require an agent.

Do we need to rewrite the whole system for AI?

Not necessarily. A limited API, data improvements and an isolated workflow are often sufficient. The choice depends on the system’s condition and the new use case.

How do we assess automation ROI?

Compare the process cost before and after implementation, including integration, API usage, maintenance, human checks and error correction. Time saved does not automatically translate into lower expenditure.

How much does a custom AI agent cost?

Cost depends on workflow complexity, integrations, data quality and control requirements. Discovery allows a pilot and production implementation to be estimated separately.

LET'S BUILD SOMETHING THAT WORKS.

Let's work through
your challenge.

A new product, a complex integration or a system ready for its next chapter. Let's start with a conversation.

contact@amaryllis.by ↗
42-317 Kalvariyskaya St.,
Minsk, 220073, Belarus

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