Maßgeschneiderte KI-Entwicklung & Implementierung
Wir lösen komplexe technische Probleme mit maschinellem Lernen, zugeschnitten auf Ihre spezifischen Daten. Wir entwerfen, entwickeln und betreiben sichere KI-Systeme.
Was wir liefern
Produktionsreife KI-Systeme
Generative KI-Entwicklung & Agenten
Messbarer Geschäftswert
Souveräne Intelligenz
Wie wir mit Ihrem Unternehmen zusammenarbeiten
Maßgeschneiderte KI-Entwicklung
Individuelle KI-Lösungen, die speziell für Ihre geschäftlichen Herausforderungen entwickelt werden, gebaut von erfahrenen Entwicklern, die sich nahtlos in Ihr Team integrieren.
Wir übernehmen den gesamten technischen Lebenszyklus: Entwicklung: Programmierung von Lösungen, Entwurf von Algorithmen und Definition von APIs. Implementierung: Bereitstellung in der Produktion, Integration in Ihren Stack und Sicherstellung des ROI.
Vielen Unternehmensteams fehlt die tiefgreifende ML-Erfahrung -- genau hier kommen wir ins Spiel. Unsere Aufgabe ist es, diese Lücke zu schließen, die ersten Lösungen zu entwickeln und Unternehmen dabei zu unterstützen, eigene starke Kompetenzen aufzubauen.
Warum maßgeschneiderte KI-Lösungen bessere Ergebnisse liefern
Individuelle Lösungen
Maximierter ROI
Datenschutz & Compliance
Tiefe Integration
Schnellere Ergebnisse für Ihre Kunden
Vorhersehbare Kosten
Reduziertes Risiko
Wettbewerbsvorteil
Technische Expertise, der Sie vertrauen können
Unsere maßgeschneiderten Lösungen werden durch tiefgreifendes technisches Wissen gestützt, dokumentiert in branchenführenden Publikationen
Retrieval Augmented Generation, The Seminal Papers
Principles for architecting reliable and verifiable AI
Retrieval Augmented Generation (RAG) is a standard process for grounding LLM prompts in user-specified content rather than relying only on a model’s training data.
Generative AI with LangChain (2nd edition)
Build production ready LLM applications and advanced agents using Python and LangGraph
A practical guide to leveraging LangChain and LangGraph for GenAI implementation, with real-world examples ranging from customer support to data analysis.
Machine Learning for Time Series
Forecast, predict, and detect anomalies with state-of-the-art machine learning methods
Use Python to forecast, predict, and detect anomalies with state-of-the-art machine learning methods.
Artificial Intelligence with Python Cookbook
Proven recipes for applying AI algorithms and deep learning techniques
Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow and PyTorch.
Bewerten Sie Ihre KI-Bereitschaft
Füllen Sie diese Bewertung aus, um einen kostenlosen Bericht zu erhalten. Völlig unverbindlich.
Wie wir mit Ihnen zusammenarbeiten
We structure our engagements to address practical constraints: budget approval, risk management, and stakeholder confidence.
"We think this could work, but need proof before we can get budget approval."
Validation Phase
Feasibility & Proof of Concept
A self-contained proof-of-concept that validates your AI initiative's feasibility without internal dependencies or large commitments.
Real Example:
One client couldn't use internal data for the PoC due to IT security policies - we built on our infrastructure instead, delivering validation in 4 weeks without any internal IT involvement.
Best for: When you need proof an AI solution will work within your constraints before seeking larger budget approval.
"We want you as the brain of the project - doing architecture and guiding our team."
Advisory & Architecture
Technical Guidance & Oversight
A collaborative approach where we provide technical architecture and ongoing guidance while your team builds, retaining full ownership.
Real Example:
A client's technical team preferred us as 'systems engineers' - we designed the architecture, provided ongoing checks, and guided implementation while they retained full ownership and built internal expertise.
Best for: Organizations wanting to build internal AI capabilities while ensuring technical success and mitigating risk.
"The timeline has to work with the budget. We need something tangible with clear milestones."
Development & Implementation
Build, Code & Deploy
End-to-end delivery. We handle Development (coding, algorithms) and Implementation (production deployment, integration), ensuring the solution creates tangible ROI.
Real Example:
An organization had tried similar automation in 2022 that failed. We managed the Development of new algorithms and the Implementation onto their specific devices.
Best for: Projects requiring both technical build (Development) and operational integration (Implementation).
Engagement Structure
Beyond the specific collaboration model, we define who drives the process. Are we extending your existing capacity, or are we taking full ownership of the delivery?
01. Dedicated Development
"Your Process, Our People."
Best when you have technical leadership in-house but need specialized AI capacity. Our engineers plug into your existing workflows (Jira, Slack) and report directly to your Engineering Manager or Product Owner. You retain full control over the daily priorities.
02. Solution Development
"Our Process, Your Outcome."
Best when you need a specific result (e.g., "Build this MVP") but lack the bandwidth to manage a dev team day-to-day. We provide a full agile pod (PM, Devs, QA) managed by Chelsea AI. We take responsibility for the timeline and delivery; you act as the stakeholder approving milestones.
Not sure which approach fits your situation?
Every organization has different constraints. Let's discuss your preferred collaboration approach.