Custom LLM Development
RAG systems, domain-tuned models, and LLM-powered features built around your data, with evaluation pipelines that prove they work.
Companies we've collaborated with
LLMs Built on Your Data and Workflows
Custom LLMs and RAG Systems Built on Your Data
SLIDEFACTORY designs, builds, and operates custom LLM applications for businesses that need AI answering from their own knowledge, accurately, securely, and in production.
Unlock the Power of Language with Our Custom LLMs
Data-Driven Enhancements
Our custom LLM solutions are designed to leverage your existing data to enhance language models. By integrating RAGS systems, we ensure that your LLMs are continually updated with the most relevant and accurate information, improving their effectiveness across various applications.
Advanced AI Algorithms
We employ state-of-the-art AI algorithms to develop and refine large language models. These algorithms process and understand complex language data, ensuring high accuracy and reliability in tasks such as customer service, content generation, and data analysis.
Integration with AI Agents
Our custom LLMs seamlessly integrate with AI agents, providing robust solutions for tasks that require advanced language understanding and interaction. This integration enhances the capabilities of your AI-driven applications, delivering superior performance and user experience.
Continuous Learning and Adaptation
Our LLMs are designed to continuously learn and adapt from new data. By leveraging RAGS systems, we ensure that your models are constantly updated with the latest information, maintaining high performance and relevance over time.
What we build
Our stack, honestly
We build on Anthropic and OpenAI APIs where managed models fit, and open-weight models where data residency or cost demands it. Retrieval runs on vector search tuned to your corpus; orchestration uses LangGraph-style graphs rather than fragile prompt chains: we’ve written about why that architecture survives production when demos don’t. Every system ships with an evaluation harness, monitoring, and cost controls.
How an engagement works
- Requirement analysis: we map the workflow, the data sources, and the accuracy bar the system must clear.
- Design and build: retrieval, model selection, and orchestration designed against your constraints, not a template.
- Evaluation and hardening: test sets built from real cases, measured before launch and continuously after.
- Integration and operation: deployed into your systems with monitoring, cost tracking, and a team that maintains what it built.
Proof it works in production
We built a custom recommendation engine for STAR at Home (a production ML system serving real users) and we operate CodeRaven, our own AI code-review platform. The team that ships and runs its own AI products is the team building yours. Tell us what you’re working on.
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Frequently Asked Questions
What is RAG development?
Retrieval-augmented generation (RAG) connects a language model to your own documents and data, so answers come from your knowledge base with citations instead of the model’s memory. It is the standard architecture for accurate, auditable business LLM applications.
Do we need to train our own model?
Usually not. Most business problems are solved with RAG and structured prompting on top of frontier or open-weight models. Fine-tuning enters when tone, format, or latency requirements demand it. We recommend the cheapest architecture that clears your accuracy bar.
How do you handle our data securely?
Data stays inside your access-control model: retrieval respects existing permissions, we use API providers with no-training guarantees or open-weight models hosted in your environment, and nothing about your business enters a public training set.
What does a custom LLM project cost?
Custom LLM development means building language-model applications around your data and your workflows, retrieval-augmented generation (RAG) systems that answer from your knowledge base, fine-tuned and prompt-engineered models that speak your domain language, and evaluation pipelines that prove the system is right before your customers see it.
This is also where classic NLP lives now: document classification, entity extraction, and semantic search are all tasks modern LLM systems handle better than the single-purpose NLP tools they replaced.
What actually breaks in LLM projects?
Four things, in our experience building and operating them. Retrieval quality: RAG systems fail on messy source data far more often than on model choice, which is why data cleanup is scoped honestly instead of assumed away. Evaluation: without a test set that reflects real questions, you can’t tell a regression from an improvement; we build evals before we tune anything. Cost drift: token spend that looked trivial in the demo compounds at production volume, so we instrument it from day one (our own code-review platform’s full five-month model bill was $161.20, and that number is engineered, not lucky). And ownership: if you can’t run, retrain, and modify the system after we leave, we haven’t finished. We hand over infrastructure, prompts, evals, and documentation, not a black box.
When is a custom LLM build the wrong answer?
Often enough that it deserves its own heading. If an off-the-shelf assistant with good prompts covers the workflow, we’ll recommend that. If your data volume is too small for retrieval to beat a well-written system prompt, we’ll say that too. And if the real problem is an agent that takes actions rather than a model that answers questions, that’s a different build with different risks. The scoping conversation exists to route you honestly, including away from us.
