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Custom AI Web Application – Full Stack AI Engineer

Full web application with a built-in AI engine. Authentication, database, admin panel, API integrations, and AI as a core feature — not an add-on.

Author: Paweł Wiszniewski · Updated:

Key takeaways

Full web application with a built-in AI engine. Authentication, database, admin panel, API integrations, and AI as a core feature — not an add-on.

  • ›SaaS or internal tool with AI as a core feature
  • ›Custom AI software you own outright
  • ›Full test coverage: unit, integration, and E2E tests with Playwright

Price: from €8,000

SERVICE DETAILS

I build custom AI software — fully-featured web applications where AI is integral to the product, not a bolted-on chatbot. Typical project: React/Next.js App Router (frontend), FastAPI or Node.js with Express (backend), PostgreSQL or Supabase (database), LLM integration via LangChain or direct API. I deliver authentication (NextAuth/Auth0), admin panel, role and permission system, external API integrations, and full unit and integration test coverage. Full stack AI engineer — projects from Warsaw to Berlin and Amsterdam.

› INVESTMENT:

from €8,000
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Paweł Wiszniewski · my approach

What I do differently

I build custom AI apps as one engineer responsible for the whole thing — from database to interface and AI layer — so responsibility never gets passed between teams. One example: for a general contractor I built an ERP system with Saldeo API integration, a RAG-based AI quoting engine and Slack budget alerts. On projects like this, what matters most to me is that AI solves a specific problem in the process rather than being a showpiece, and that your team can take over the code.

Case study: Konkret APP

My take and a real-world example

My take

An opinion that may surprise you from someone who builds custom apps: buying a ready tool is often better. A custom app only makes sense when the process or data is specific enough that off-the-shelf solutions won't work.

From the market

MIT NANDA's 2025 report found that deployments based on purchased tools and partnerships reached production about 67% of the time, versus roughly a third for projects built entirely in-house. That's why I compare build versus buy at the start of every project and say so plainly when building doesn't pay off.

Sources: Fortune: MIT NANDA report on GenAI pilots (2025)

Who this is for

  • Companies whose processes and data don't fit off-the-shelf AI tools.
  • Startups past MVP validation that need a full product.
  • Organizations building internal AI tools for several departments.
  • Companies that want to own the code and control their data instead of paying for SaaS.

What you get

  • App architecture: frontend, backend, database and AI layer.
  • AI features at the core: chat, document analysis, generation and agents.
  • Authorization, user roles, an admin panel and API integrations.
  • Automated tests, monitoring and AI answer quality evaluations.
  • Code handover, documentation and onboarding of your team.

How results are measured

  • Adoption by target users.
  • Time to complete key tasks in the app versus the previous process.
  • Quality and stability of AI features in monitoring.
  • Production bugs and time to fix them.

Key Benefits

SaaS or internal tool with AI as a core feature — not a chatbot bolted onto existing software, but AI integrated into every layer of the product.

Custom AI software you own outright — code in GitHub, no vendor lock-in, free to develop with any engineering team.

Full test coverage: unit, integration, and E2E tests with Playwright — you can add new features without fear of regression.

Admin panel, user management, roles, and permissions — ready for your operations team from day one after launch.

Technical documentation and ADRs (Architecture Decision Records) — your next developer understands design decisions without a week of onboarding.

The Process

1

Requirements and architecture

2-3 day discovery workshop: user story mapping, technology stack selection, database design, and AI system architecture. I deliver a technical specification before the first line of code.

2

Backend, database, and AI engine

Building the foundation: API (REST or GraphQL), database schema, authentication, and AI pipeline. This phase takes 3-5 weeks and is the most critical for performance and security.

3

Frontend and end-to-end integration

React/Next.js with UI components (Radix UI or Shadcn), API connection, error handling, and loading states. I iterate based on feedback on a live staging environment via Vercel Preview Deployments.

4

Testing, hardening, and launch

Automated testing, security audit (OWASP Top 10), monitoring setup (Sentry, Datadog), and production launch. I deliver an operational runbook and a code handover session.

Custom AI web app vs off-the-shelf SaaS vs no-code builder

CriterionCustom AI web appOff-the-shelf SaaSNo-code builder
Fit to your businessBuilt around your exact workflowGeneric, you adapt to itLimited templates
AI as core featureIntegrated into every layerAdd-on or absentShallow integration
OwnershipFull — code in your GitHubNone, vendor-hostedLocked to the platform
Cost over timeHigher build, no per-seat feesRecurring subscriptionsSubscription plus limits
Test coverageUnit, integration, E2EVendor-managed, opaqueMinimal
Customization ceilingUnlimitedVendor-definedHits limits quickly
Best forDifferentiated AI productsStandard, solved problemsQuick internal prototypes

Common mistakes

  • Adding an AI chat to an app without a clear job for it to do.
  • Tying the whole app to one model provider without an abstraction layer.
  • No monitoring of AI answer quality after launch.
  • A project without documentation or tests that can't be handed to another team.

When this service is not worth it

  • An existing tool covers most of your needs and the differences aren't critical.
  • You have no budget to maintain and develop the app after launch.
  • Requirements aren't known yet — an MVP is a better start.

Frequently Asked Questions

What does collaboration look like on a project?

I work in 2-week sprints with daily updates via Slack and weekly live demos. You have access to the repository, a Linear board, and the staging environment throughout the entire project.

Do you offer post-launch maintenance?

Yes — a maintenance retainer (bug fixes, dependency updates, monitoring) from €600/month. One-off feature additions beyond the application contract are also possible.

Do you work with an existing codebase?

Yes, if the codebase is in reasonable technical shape. I start with a code audit before quoting the project. Refactoring and extending an existing application are priced separately.

What is 'custom AI software' and 'full stack AI engineer'?

Custom AI software is an application built specifically for your business, not an off-the-shelf SaaS with your logo. Full stack AI engineer — I work remotely with clients across Europe and am available for meetings in the Central European time zone (CET).

Can the app run on my infrastructure?

Yes. I can deploy the app to your cloud (AWS, GCP, Azure), Vercel or company servers. For sensitive data, the AI layer can use models running locally or in a private cloud.

Do I own the code and data?

Yes. The code, repository, database and cloud accounts are yours. You can keep developing the app with me, your own team or another contractor.

How much does a custom AI web app cost?

Projects start from €8,000. The price depends on the number of modules, integrations, user roles and security requirements.

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BIAŁYSTOK, PL
+48 732 022 086pawel.wiszniewski95@gmail.com