AI Development
AI Development Company in Stockholm - From Pilot to Production
We work across the full spectrum - from proof of concept to global production deployments - with teams that understand both the models and the business behind them.
Tell us about your need
LLM Solutions
Large language models such as GPT, Claude, and Gemini have fundamentally changed what can be built quickly. But the potential is only realised when the model is configured correctly, connected to the right data sources, and placed in the right context. That is where the difference between a demo and a production-ready solution emerges.
Shapp designs and implements LLM solutions including:
- ChatGPT and OpenAI integration - we integrate GPT and the Assistants API against your internal systems via RAG (Retrieval-Augmented Generation), so the model responds based on your actual data, not generic training.
- Claude integration (Anthropic) - Claude's long context window and strong instruction-following make it an excellent choice for complex document and analysis workflows. We have built production solutions with the Claude API for editorial and legal use cases.
- Fine-tuning and custom models - when off-the-shelf models are not sufficient, we fine-tune existing models on your domain-specific data or build lightweight specialist models for repetitive classification and generation tasks.
- Chatbot development - we build conversational interfaces with memory, context management, and escalation flows to human support. Chatbots are integrated against your CRM and support systems and configured with clear guardrails to minimise hallucination and off-topic responses.
All LLM implementation is handled with security and GDPR compliance in focus. We ensure that sensitive customer data is never sent to external model APIs without the necessary agreements and anonymisation in place.
Automation
Manual workflows represent one of the clearest opportunities for AI to deliver rapid ROI. Tasks that consume hours each week - content writing, metadata tagging, quality assurance, publishing flows - can be automated without sacrificing quality, often with greater consistency than manual handling.
Examples of automation projects we have delivered or can deliver:
- Content production - AI-assisted flows for generating product descriptions, news summaries, SEO metadata, and social posts. Flows are configured with editorial review built into the process, not as an alternative to it.
- Metadata enrichment - automatic tagging, categorisation, and description of video, image, and document archives. Critical for streaming services with large content libraries where discoverability and recommendations are business-critical.
- Quality assurance and testing - AI-driven QA that identifies anomalies in content, format, and data. Reduces manual review time and catches errors that tired eyes easily miss.
- Publishing flows - orchestration of content from creation to distribution with automated approval flows, scheduling, and platform adaptation.
We integrate automation solutions against your existing tools - WordPress, Contentful, Salesforce, Slack, and your internal systems - so that the flows fit how your team actually works. See how other services such as API integrations can amplify your automation flows.
Recommendation Engines
For streaming and content services, the recommendation engine is directly linked to retention. The right content at the right moment keeps viewers engaged, reduces churn, and increases time on platform. Shapp builds recommendation systems tailored to the specific needs of media companies and digital platforms.
We work with:
- Collaborative filtering - models that identify patterns among users with similar behaviours and recommend content that comparable profiles have appreciated.
- Content-based filtering - analysis of metadata, genre, tags, and semantic similarity between content items to make recommendations based on what a specific user has shown interest in.
- Hybrid models - combinations of both methods with real-time signals (current viewing, search behaviour, time of day) to deliver contextually relevant recommendations.
- A/B testing and evaluation - all recommendation models are delivered with an A/B testing framework and clear metrics (CTR, retention, average session length) so you can measure actual business impact.
We build recommendation systems that can be deployed in your existing infrastructure and exposed via API to your front end - without requiring you to replace your data stack.
Our Process
AI projects fail not because the technology is wrong, but because they start without a clear business problem. Our process is designed to ensure that every AI initiative is grounded in reality:
- AI workshop - we begin with a structured workshop session where we map your needs, existing data, and business objectives. We identify use cases with high ROI potential and rank them by feasibility.
- Proof of concept - before committing to full implementation, we build a limited PoC that validates the technology works for your specific case. This saves time and reduces risk.
- Implementation and integration - we build production-ready solutions connected to your existing systems, with a focus on security, scalability, and maintainability.
- Testing and evaluation - we establish metrics, run A/B tests where relevant, and document model behaviour and edge cases.
- Deployment and monitoring - we deploy into your environment, configure logging and monitoring, and ensure you have full visibility into how the model behaves in production.
- Ongoing optimisation - AI models require maintenance as data and behaviours evolve. We offer retainer agreements for continuous tuning and further development. See our pricing page for details.
Frequently asked questions about AI development
Do we need large volumes of proprietary data to build an AI solution?
Not always. Many use cases can be addressed with existing LLMs via prompt engineering and RAG (Retrieval-Augmented Generation), which requires significantly less proprietary training data. We assess your data situation during the workshop and recommend the right approach based on what you actually have.
How long does an AI project take from workshop to production?
A simple PoC can be delivered in two to four weeks. A production-ready chatbot or automation solution typically takes six to twelve weeks depending on integration complexity. Recommendation systems with custom model training can take three to six months. We provide a clear timeline estimate after the discovery phase.
Which AI models and frameworks do you work with?
We work with OpenAI (GPT series, Assistants API), Anthropic (Claude), Google (Gemini, Vertex AI), Hugging Face, and open-source alternatives such as Mistral and Llama. Model selection is always based on your specific requirements, cost profile, and data protection needs - we are not tied to a single vendor.
How do you handle sensitive data and GDPR in AI solutions?
We design AI solutions with data minimisation and security as foundational principles. Sensitive personal data is anonymised or pseudonymised before being processed by external models. We establish the necessary DPA agreements with model providers and document all data flows to support your GDPR compliance.