Custom LLM Solutions Built Around Your Data
For companies that need more than a chat window on a public model. We build the system around the model: retrieval over your documents and databases, agents that act in your software, deployment inside your own cloud, and fine-tuning where a test set shows it pays.
Clients include Typeform. 5.0 on Clutch. AWS Partner.
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What Custom Means in a Custom LLM
Almost nobody who asks for a custom LLM needs a new model. OpenAI, Anthropic, Google and the open-weight labs have already paid for the training, and their models handle most business tasks on the day you start. What those models lack is your data, your rules and access to your systems.
So the custom part is the system around the model: what it is allowed to read, which actions it can take, how its answers are checked, and where it runs. That is where the engineering goes, and it is the part you own when the project ends.
What We Build
Each piece, with when it is the right choice.
Retrieval Over Your Data (RAG)
The model answers from your documents, tickets and databases, fetched at question time and cited in the answer. It is the right start for most projects, because the knowledge stays current without retraining anything. It needs strict access rules, so one customer or department never sees another's data.
Agents That Act in Your Systems
The model calls your APIs through tools to look things up and get work done, and a person approves anything that cannot be undone. It fits a job that is a sequence of steps across systems. For a single question and answer, an agent adds cost and ways to fail.
Private and Self-Hosted Deployment
The model runs inside your own cloud account through AWS Bedrock or Azure, or as an open-weight model on your own GPUs. Choose it when data cannot leave your network, or when steady volume makes owned hardware cheaper. At low volume a hosted API costs less.
Fine-Tuning
An existing model trained further on your examples. It pays on a narrow, high-volume task such as classification or extraction, where a small tuned model can cost less and answer faster than a large general one. We try retrieval and prompting first, and fine-tune when the test set shows a gap they did not close.
An Evaluation Set From Your Cases
Real questions and documents from your team, with the answers you would accept, scored automatically on every change. It tells you when the system is good enough to ship, and it catches a prompt or model change that makes answers worse before your users see it.
Tracing and Cost Per Task
Every model call is traced with its inputs, cost and latency, so a wrong answer can be followed back to the retrieval step or the prompt that caused it. You also see what one answered question or one processed document costs.
How We Decide What to Build
The test set comes first. Before choosing an architecture we collect real cases from your team with the answers you would accept. Every option is then scored on the same cases, which turns the RAG or fine-tuning question into a measurement. How we run evaluations is in Agent Evaluation at Scale.
The simplest system that passes. We start with a hosted model, retrieval and careful prompts, and add agents, private hosting or fine-tuning when the scores or your constraints call for them. Each addition has to pay for the cost of running it.
What we have shipped. For Typeform we built a multi-agent assistant on AWS Bedrock AgentCore with six agents, more than ninety backend tools and an evaluation pipeline in CI. For an automotive marketing platform we built a retrieval assistant over dealer inventory, where one dealer can never see another's data. For a financial services company we fine-tuned language models to classify customer intent in a support chatbot that resolves 75% of inquiries without a person.
You own all of it. The code lives in your repository and runs in your cloud account from the first commit. The prompts, the evaluation set, the vector store and any fine-tuned model weights are yours, in the contract.
Models, Frameworks and Infrastructure
We work with what you already run, and recommend whichever model scores best on your cases.
How the Engagement Runs
A small fixed-price step before any build, so you can judge the work before committing to it.
Discovery Call
30 minutes with Carlos, who founded Vindler. Bring the task you want the model to do and a few examples of good and bad answers. You leave knowing which kind of system it is and roughly what it takes.
Scoping Week, $4,500 Fixed
One week that ends in a written build specification: architecture, the first evaluation cases, running cost and a milestone plan. If you already have a prototype, the Production Readiness Review takes its place at the same price.
Build in Milestones
Projects start at $25,000. Working software is demoed every week, in your repository and your cloud account, and each milestone is scored against the evaluation set before the next one starts.
Tell Us What the Model Should Do
Book a 30 minute call. Bring the task and a few examples of answers you would accept, and you leave knowing which kind of system it is.
Describe the Task
What the model should know or do, where your data lives, and any limit on where it can run. We reply within 24 hours.