Elasticbrains Alternatives
Profile Overview
- Minimum Budget
- > €5,000
- Hour Rate
- €100 - €149
- Company Size
- 2-10
- Founding Year
- 2015
- Case Studies
- 3
- Spoken Languages
- Deutsch (German), English (English)
About Elasticbrains
Elasticbrains, based in Munich, has been developing software and platforms for startups, mid-sized businesses, and large enterprises since 2015—from booking systems and marketplaces to portals. In 2019, we began developing our own AI models, and AI has been our primary focus since 2024. AI works within the platform as an agent, assistant, or trained model, taking work off your team’s plate.
Our specialties:
- AI platform development (18+ AI projects in production)
- n8n automation (17+ n8n implementations)
- Multi-agent systems
- RAG knowledge bases
- Voice AI & document AI
- GDPR-compliant architectures hosted in the EU
Our AI work builds on 100+ successfully delivered digital projects and 10,000+ person-days of experience since August 2015. We’ve published 32 client references at elasticbrains.de/references.
How we work:
- Initial conversation (30 or 45 minutes, free, by video or phone): You tell us about the problem, we ask questions, and explain how we’d solve it. If off-the-shelf software is all you need, we’ll tell you.
- A fixed-price first step: a workshop or a feasibility sprint. During the sprint, we test how well it works using your real data. You’ll see the accuracy rate and costs before making a major investment.
- Build: We integrate the system into your existing workflows. If a standard model isn’t good enough, we train a custom one.
- Launch and operation: The system runs wherever you choose. We train your team to manage it independently.
Your point of contact is Daniel Neumann, from the initial conversation through launch and operation. We regularly show you a working version, and after every step, you decide whether to continue.
Fixed-price starting options (excluding VAT):
- Team check, €650: Half a day, onsite or remote. We look at how your team plans, coordinates, and hands off work, then identify where things are getting stuck.
- AI workshop, “Find the Right Use Cases,” €1,750: Four hours with your team, followed by a solution vision and a proposal for the next step.
- AI feasibility sprint, from €4,999: We test a use case with your real data and report on accuracy and costs.
Operations and data protection: The system runs on your servers or in a data center in the EU. We agree on maintenance and response times upfront. Together, we decide what data the AI model can access; a local filter can remove personal data before text reaches a language model.
Our core team in Munich works closely with clients and is supported by our own nearshore talent pool, TalentLake, with 400+ qualified developers. This lets us scale flexibly while maintaining full GDPR compliance and high quality standards.
Industry focus: Pharma, finance & collections, education, recruiting, legal tech, automotive, sales training, hospitality, events & ticketing, and construction.
Reviews
Feedbax reviews
Services
- Artificial Intelligence 60%
- Software Development 25%
- Cloud Solutions 5%
- App Development 5%
- UX / UI Design 5%
Frameworks
Artificial Intelligence
- Machine Learning 20%
- Image Processing 20%
- Pattern Analysis 20%
- Speech & Text Processing 15%
- Deep Learning 15%
- Chatbot 10%
Mobile OS
- Hybrid & Cross Platform 50%
- Android 25%
- iOS 25%
Programming & Scripting
- JavaScript 30%
- TypeScript 20%
- JSON 15%
- REST API 15%
- Swift 10%
- Kotlin 10%
No Code Development
- Bubble 85%
- Thunkable 15%
Portfolio
Downloads
Elasticbrains Firmenpräsentation PDFProjects

Custom Image Model for a Collector Catalog
Case Study by Elasticbrains
Description
A specialized image model matches photographed collectibles to entries in a large catalog, where off-the-shelf AI models fall short. At the customer’s request, we’re not naming them.
Challenge
The objects in the catalog look almost identical. Off-the-shelf models treat everything as similar and, in critical cases, score the wrong match higher than the right one. But automated matching requires a signal you can trust.
Solution
- The pretrained image model stays as is. Only a small add-on learns the subtle differences.
- Labels came from existing metadata, so no one had to tag images by hand.
- Training takes seconds. It runs on standard CPUs on-premises. The images never leave the organization.
- We averaged each improvement across multiple test runs. We tested and ruled out using more training data, higher resolution, and a language model to rerank results.
Results
- 53 vs. 30 correct matches on the first try per 100 photos: a custom model compared with a standard API, measured in a 2026 customer project.
- Matching also improves for objects never seen during training. The model learned the distinction, not the examples.
- No GPU cluster required; runs on-premises.
Contact the service provider and receive an appropriate offer in a timely manner.





























