AI Solutions
LLM Training Models
We fine tune and train custom large language models for teams with proprietary data, domain specific tasks, or workflows a general model cannot handle. We own the data preparation, the training, the evaluation, and the deployment, so the result is measured rather than guessed, and it is yours to keep.
What we offer
From raw data to a model you can rely on
Training a useful model is less about a single clever step and more about getting four things right: the data, the method, the measurement, and the way it ships. We do all four, with accurate engineering rather than a demo that falls over on real inputs.
Custom fine tuning on your data
When a task is narrow and repeated, a fine tuned model beats a large general model with a long prompt. We fine tune open models with LoRA and QLoRA adapters, which train a small set of weights on top of a frozen base. That keeps training affordable, lets us version adapters per task, and produces a model that is cheaper to run and more consistent on your work. We keep the training data and the adapter so the model can be retrained as your process changes.
Retrieval augmented generation
Most business questions are answered from your own documents, not from what a model memorized. We build RAG systems end to end: chunking your content sensibly, generating embeddings, storing them in a vector database such as pgvector, Qdrant, or Pinecone, and writing the retrieval logic that selects the right context at query time. Answers stay grounded in your sources, and we can cite where each answer came from.
Evaluation and benchmarking
A model you cannot measure is a model you cannot trust. We build evaluation sets from your real cases and score changes against them with frameworks like Ragas for retrieval quality and task specific graders for everything else. Before anything ships, you see accuracy, failure modes, latency, and cost side by side, so a change is an informed decision rather than a hope.
Deployment and integration
We deploy the model where it fits: a hosted API, a managed endpoint, or your own GPU infrastructure when data residency matters. Then we wire it into the systems your team already uses, behind your auth, with logging and rate limits in place. You get an integration you can audit, not a black box bolted to the side of your stack.
Fine tuning or retrieval
We pick the method that fits the problem
Fine tuning changes how a model behaves. It is the right tool when a task has a consistent shape and you want shorter prompts, lower cost, and steady tone or format. With LoRA and QLoRA we train small adapters rather than the whole model, so the work is affordable and easy to version.
Retrieval changes what a model knows at the moment of the answer. It is the right tool when facts change often or must be cited, such as policies, product data, or documentation. Often the best system uses both: a fine tuned model for behavior and retrieval for fresh, grounded facts.
We do not start from a favorite technique. We start from your task, your data, and your constraints on cost, latency, and privacy, then choose the approach that the evaluation numbers support.
Process
Data prep, train, evaluate, deploy, iterate
A clear loop from your raw material to a model running in production, with a measurement step that decides whether anything ships.
Data preparation
We gather, clean, and structure your source material, remove duplicates and noise, and build a labeled dataset and a held out evaluation set. Most of the quality of a trained model is decided here, so this is where we spend real care.
Train
We select a base model sized to the task and fine tune it with LoRA or QLoRA, or stand up the retrieval pipeline if RAG is the better fit. We track runs and hyperparameters so every result is reproducible.
Evaluate
We score the model against the held out set and your real cases, comparing it to the baseline on accuracy, latency, and cost. We look at where it fails, not just the average, and we tune until the numbers justify shipping.
Deploy
We package the model and serve it behind a stable API with auth, logging, and guardrails. We integrate it into your workflows and put monitoring in place so you can see how it behaves in production.
Iterate
Real usage surfaces new cases. We feed them back into the evaluation set, retrain or adjust retrieval, and ship improvements on a cadence that suits you. Because you own the data and the pipeline, this loop keeps running with or without us.
Use cases
Where a trained model earns its keep
A few of the problems where a model grounded in your own data and tuned to your task does real work, not a party trick.
Customer service automation
A model grounded in your help center, past tickets, and policies drafts accurate replies, routes the hard cases to a person, and stays consistent with how your team actually answers. It reduces handle time without inventing answers, because retrieval keeps it tied to your real content.
Internal knowledge bases
Teams lose hours hunting through wikis, drives, and chat history. We build an assistant that answers from your internal documents with citations, respects access permissions, and tells the user when it does not know rather than guessing.
Domain specific code assistants
A general code assistant does not know your internal libraries, conventions, or service boundaries. We fine tune and ground a model on your codebase and standards so suggestions fit how your team writes software, not a generic average of the public internet.
Document extraction pipelines
Contracts, invoices, permits, and forms carry structured data trapped in unstructured files. We build extraction pipelines that pull the fields you need, validate them against business rules, attach a confidence score, and send the uncertain cases to a review queue instead of failing silently.
Let's talk about your data
Tell us the task you want a model to handle and what data you have. We will tell you honestly whether fine tuning, retrieval, or a mix is the right call, what it would take, and what it would cost to run. You own the result either way.