What is Novaneli?
Novaneli is the AI implementation and software development practice of Kamil Nowak.
Novaneli Kamil Nowak / AI implementation + software development
AI implemented in your product, workflow, or internal system
I design and build practical AI integrations, agent workflows, process automations, internal tools, and custom software that connect to existing systems and create measurable operational impact.
Led by Kamil Nowak, a senior software engineer focused on production AI implementation, backend integrations, and automation for real business processes.
AI implementation LLM integrations Agent workflows Process automation Custom software
map workflowconnect systemsship automation
Problem to solution
AI creates value when it is built into the real process
Common failure points
- AI demos isolated from the product, data, and daily workflow
- Manual work still happening between tools because integrations are missing
- LLM outputs without evaluation, review paths, or business rules
- Automation that breaks on exceptions and edge cases
- Custom software gaps that block adoption after the prototype
Novaneli approach
- Start from the business process, users, systems, and measurable outcome
- Design the AI integration around existing tools, APIs, data, and constraints
- Build the missing software layer: backend services, queues, interfaces, and controls
- Add evaluation, guardrails, observability, and human review where needed
- Ship in small increments that save time, improve the process, or unlock a product capability
About
I implement AI inside real software and business processes.
Senior software engineer building production AI integrations, LLM features, agent workflows, internal tools, and backend systems.
Novaneli is the professional brand of Kamil Nowak. I help companies move from AI ideas and prototypes to working implementations connected to their products, data, users, tools, and business constraints.
Who is Kamil Nowak?
Kamil Nowak is a software engineer specializing in practical AI implementation: LLM integrations, AI agents, process automation, internal tools, and backend systems.
What problems does Novaneli solve?
Novaneli helps companies implement AI in existing products and workflows through integrations, automations, internal tools, and maintainable custom software.
Services
AI implementation and software development for existing products, workflows, and tools.
I help teams answer what AI should do in their current system, how it should connect to data and tools, and what software needs to be built around it.
AI feature implementation
Add LLM-powered extraction, classification, search, decision support, or copilots to an existing product.
System integrations
Connect models and automations to CRMs, back-office apps, document stores, APIs, databases, and review flows.
Agent workflows
Build controlled automations with tools, state, approvals, retries, logs, and clear failure handling.
Internal tools
Create operator consoles, dashboards, review queues, admin panels, and custom app features around AI processes.
LLM Integrations
AI features connected to your product, database, APIs, documents, and business rules.
Retrieval, extraction, classification, routing, copilots, guardrails, evaluation, monitoring, and fallbacks.
Process Automations
Automations that remove manual steps from operational workflows instead of living as separate demos.
Data intake, validation, enrichment, notifications, approvals, handoffs, exception paths, and audit logs.
Agent Workflows
Tool-using agents for repeatable processes where state, permissions, and review points matter.
Planning, tool calls, retries, human-in-the-loop decisions, execution logs, and controlled rollout.
Internal Tools
Interfaces that let teams operate, review, correct, and measure AI-assisted workflows.
Dashboards, queues, admin panels, review screens, metrics, exports, and role-based controls.
Custom Software Development
The backend and product code needed to make the AI implementation usable in production.
APIs, services, schemas, queues, integrations, deployment paths, observability, and maintainable product features.
Public projects
Public work I can talk about.
Most client work is private, but these projects show how I think about domain knowledge, data structure, product quality, and maintainable software.
Vetnote.pl
Public projectA product for veterinary documentation and everyday work with structured medical information.
Shows: domain workflows, document capture, review-friendly UX, and production product thinking.
ProductVeterinaryDocumentationWorkflow
Open project vetformat.org
Open sourceAn open-source initiative around structured veterinary data formats and domain-specific interoperability.
Shows: data formats, open standards, domain modeling, and interoperability constraints.
Open SourceData FormatVeterinaryInteroperability
Open project paremac.com
Open sourceAn open-source tooling project with a backend, automation, and practical data-processing angle.
Shows: backend services, developer tooling, automation paths, and maintainable execution.
Open SourceBackendAutomationDeveloper Tools
Open project Confidential work
The most relevant work is often private.
Some of the implementations I have built run in production and touch sensitive data, internal processes, or client-specific systems. They cannot be shown publicly, but they shape how I approach new work.
References are available where client agreements allow it.
- LLM integrations in existing products and internal systems
- Agent automations connected to real operational workflows
- Internal tools, review queues, data pipelines, and observability
- References available on request
Process
A practical path from business problem to production AI implementation.
Before writing code, the process, systems, data, risks, and success metric need to be understood.
01
Diagnose the process
Clarify the workflow, users, bottlenecks, decisions, and what measurable improvement should look like.
02
Map systems and data
Identify existing tools, APIs, documents, databases, permissions, edge cases, and integration points.
03
Prototype the integration
Build the smallest useful AI workflow and test it against realistic cases, failures, and quality targets.
04
Build the production software
Turn the validated path into services, product features, internal tools, queues, and operational controls.
05
Measure and iterate
Improve the implementation with real users, monitoring, evaluation data, and changing business requirements.
Want to implement AI in an existing product, workflow, or internal system?
Send a short description of the technical problem, the systems or tools involved, and the business result you want. I'll tell you how I would approach the implementation.