AI Development
GPT, LangChain, Custom Models
Build intelligent applications using the latest AI technologies including large language models, custom model training, and AI orchestration frameworks.
- LLM integration (GPT-4, Claude)
- Custom model fine-tuning
- RAG implementation
- Prompt engineering
- AI application architecture
- Cutting-edge AI capabilities
- Customized to your domain
- Production-ready solutions
- Continuous improvement
- Chatbots
- Content generation
- Search
- Analysis
Common questions
Which model should we use?
Whichever is cheapest for the accuracy the task needs, and that is rarely the largest one. We benchmark candidates against your actual cases rather than public leaderboards, because leaderboard performance tells you very little about your documents. We also design so the model can be swapped - this field moves quickly and being locked to one provider is a real risk.
What is RAG, and do we need it?
Retrieval-augmented generation means finding the relevant passages from your own material and giving them to the model with the question. You need it whenever answers must come from your content rather than the model's general knowledge - internal documentation, policies, product data. It is usually cheaper and more accurate than fine-tuning, and unlike fine-tuning it can cite where an answer came from.
How do you stop it inventing answers?
Constrain it to retrieved context, return citations so a user can verify, and make refusal an acceptable outcome. A system that says it does not know is more valuable than one that always answers, particularly where being confidently wrong carries a cost. We test specifically for the confidently-wrong case, not just accuracy on questions with clear answers.
How much does this cost to run?
It depends on volume, model and prompt size, and it is an ongoing operating cost rather than a one-off build expense - which is the part that surprises people. We estimate cost per request during discovery and design to reduce it: caching, a smaller model for the simple steps, and retrieval rather than very large contexts.