AI development & custom models
AI development and custom small language models for your business
We build the intelligence layer: domain-specific small language models, retrieval pipelines and private AI deployments that are faster, cheaper and more accurate on your work than a general-purpose model.
What we deliver
Models and pipelines tuned to your domain
General AI models know a little about everything. Your business needs a model that knows your products, documents, terminology and rules.
Custom small language models
Compact models trained or fine-tuned for your domain that run fast and privately at a fraction of large-model cost.
Fine-tuning
Adapt open-weight models to your tone, formats and tasks using curated, versioned training data.
Retrieval-augmented generation
RAG pipelines with chunking, embeddings, hybrid search and re-ranking so answers are grounded in your sources.
Private AI deployment
Host models in your own cloud or on-premises infrastructure with access controls and audit logs.
Evaluation & benchmarking
Objective test sets that compare models on accuracy, latency, cost and safety before you commit.
AI strategy & readiness
Assess your data, identify high-value use cases and build a practical roadmap with budget ranges.
Why BlueMind Tech
Why small language models?
For focused business tasks — classifying tickets, drafting responses, extracting fields, answering policy questions — a well-trained small model often matches or beats a giant general model.
Small models are cheaper to run, respond faster, and can be hosted entirely inside your environment. That means predictable costs and data that never leaves your control.
- Lower and more predictable inference costs
- Faster responses for real-time user experiences
- Private hosting for sensitive or regulated data
- Behavior tuned to your terminology and formats
- No lock-in: you own the weights and training data
- Measured against your own evaluation set
How we work
How we build custom AI
- 01
Define the task
We specify inputs, outputs, quality thresholds and constraints such as latency, cost and privacy.
- 02
Prepare the data
We collect, clean and label examples from your systems and build a held-out evaluation set.
- 03
Train & compare
We fine-tune candidate models and benchmark them against baselines on your evaluation set.
- 04
Deploy & monitor
We deploy the winning model behind an API with monitoring, versioning and a retraining plan.
Related services
Pairs well with
AI Software Development
Production applications with AI at the core — copilots, agents, search and automation.
Learn moreSpec-Driven Development
Executable specifications that align stakeholders, engineers and AI coding agents.
Learn moreTest-Driven Development
Red-green-refactor engineering, test automation and legacy code under test.
Learn moreFrequently asked questions
What is a small language model (SLM)?
A small language model is a compact AI language model, typically with a few billion parameters or fewer, that can be trained for specific tasks. SLMs run faster and cheaper than large models and can be hosted privately.
Do we need a lot of data to build a custom model?
Often less than expected. Many tasks can be tuned with a few hundred to a few thousand quality examples. We assess your data during discovery and recommend RAG, fine-tuning or both.
Should we use RAG or fine-tuning?
RAG is best when answers must come from documents that change often. Fine-tuning is best for consistent behavior, format and domain language. Many production systems combine both, and we test which approach performs best on your data.
Who owns the model?
You do. When we fine-tune an open-weight model for you, the resulting weights, training data and evaluation sets are delivered to you.
Find out if a custom model fits your use case
Share the task and a few examples. We will tell you whether an SLM, RAG or an off-the-shelf model is the smarter choice.