Custom LLM Development and RAG Integration
Add useful AI capabilities to software you already use. We build document search, grounded answers, summarization, classification and extraction into existing products and workflows. Sometimes that calls for retrieval-augmented generation (RAG). Sometimes a simpler integration does the job. We test the approach against real examples before treating a demonstration as a working system.
Companies we've collaborated with
LLMs Built on Your Data and Workflows
Your team may need to find a policy across hundreds of documents, pull details out of incoming files or help a user ask a question about information already in your product. A language model can make those interactions easier, provided the right source material is available and the output can be checked.
We identify the source systems, users and permissions first. Then we decide whether to retrieve relevant information, structure a narrower task or connect the feature to other software. Model choice and infrastructure follow those requirements.
Answers grounded in your information
A RAG system retrieves relevant passages from approved sources before generating an answer. That can make internal knowledge easier to use and give people a source to check. It still depends on document quality, retrieval design and tests built from the questions users ask. Our RAG explainer covers the approach in more detail.
Useful features inside existing software
Not every project needs a chat interface. A product may need to summarize a long record, categorize incoming requests, extract fields from documents or help users search by meaning. We design the interaction for the task and integrate it with the application people already use.
Evaluation before launch
We assemble representative examples, including ambiguous questions and cases the system should decline. We check answer quality, source selection, failure behavior and operating cost. When a change improves one result but hurts another, the evaluation set shows it.
When a custom model isn’t necessary
“Custom LLM” doesn’t automatically mean training a language model from scratch. Many teams can use an existing model with structured prompts and retrieval over their own information. Fine-tuning may be useful for a particular output format or repeated task, but we first establish what the existing model cannot do reliably. Some jobs are better handled by conventional software or a rules-based workflow.
If the system needs to take actions across tools rather than answer or transform information, see AI agent development. If you’re still deciding where AI would help, start with AI consulting and development.
What we build
Tools selected for the job
The model, retrieval method and hosting arrangement depend on the data, permissions, response time and operating budget. We can use managed model APIs or other approaches when the requirements call for them. The durable part is the ability to evaluate outputs and revise the system as its source information and users change.
Data handling and privacy
Before connecting a model to company information, we identify what data it would receive, who can retrieve it, which provider or environment processes it and what controls are required. We document those choices in the project scope and test permission boundaries during integration. Any specific retention, hosting or training restrictions need to be agreed against the actual provider and deployment, rather than assumed from the phrase “private AI.”
How an engagement works
We begin with a workflow and examples of the questions or documents the system must handle. We inspect the available sources and agree on what a useful, checkable answer looks like. Next we build a focused version, evaluate it against real cases and integrate it into the application. After launch, we can monitor failures, cost and changes in the underlying information.
Proof it works in production
We built and operate CodeRaven, our AI code-review system. It reviewed 1,164 pull requests over 148 days with $161.20 in total model spend. Insyght Studio is another AI product we built: it turns live performance data into plain-language insights and helps small businesses produce SEO content and social posts.
Our recommendation engine for learning software is a separate machine-learning example. We built a hybrid model using Python and scikit-learn to match educational resources with user needs. It predates modern LLMs, so it isn’t a RAG example.
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Frequently Asked Questions
What is RAG development?
Retrieval-augmented generation connects a language model to selected source material so it can use relevant passages when answering a question. We design the retrieval, source presentation and evaluation around the information people need to find. It does not guarantee a correct answer, so tests and a way to check sources still matter.
Do we need to train our own model?
Usually not. We first test whether an existing model, clear instructions and retrieval from your information can handle the task. We consider fine-tuning when a specific output requirement justifies it. Training a new general-purpose model is a very different commitment.
How do you handle our data securely?
We map the information the feature needs, the people who can access it and the systems that will process it before selecting an architecture. Provider terms, retention, hosting and access controls are reviewed for the actual deployment and captured in the project scope. We test the agreed boundaries as part of integration.
What does a custom LLM project cost?
The scope depends on the data sources, integration work, permission requirements and level of evaluation needed. We review the workflow and provide a written scope before development. A narrow feature and a company-wide knowledge system involve different work.
What commonly goes wrong in an LLM project?
Poor source data, weak retrieval, missing test cases and unclear ownership can make a promising demo unreliable in use. We test with real questions, record where answers fail and plan for monitoring and updates after launch.
When is a custom LLM build the wrong answer?
When a standard tool already meets the need, when a straightforward rules-based workflow is more reliable or when the source information cannot support the answers people expect. We’ll identify those cases before recommending a custom build.
