AI & Machine Learning Development
Models that run in production, not notebooks
DanixSoft builds machine learning systems that run in production: it first checks whether a model will beat a simpler rule-based approach, prototypes on your data, and then hardens the model into a monitored service.
Not every problem needs machine learning. We start by checking whether a model will genuinely beat a simpler rule-based approach, prototype quickly on your data, and only then harden it into a monitored production service.
What's included?
Everything you need to ship a production-grade solution.
LLM Integration
Production integration of OpenAI, Anthropic, and Gemini models with prompt design, cost control, and guardrails.
Custom Model Training
Classifiers, regressors, and recommenders with scikit-learn and PyTorch, or fine-tuned foundation models where justified.
Retrieval-Augmented Generation (RAG)
AI that answers from your own documents and databases, with citations back to the source.
Computer Vision
Object detection, image classification, and OCR pipelines.
Conversational AI
Context-aware assistants with human hand-off and full conversation logging.
Predictive Analytics
Forecasting, churn prediction, and anomaly detection on your business data.
How does a project run?
Transparent, milestone-driven delivery from day one.
Use-Case Validation
We assess whether ML solves your problem better than a deterministic approach and agree how success will be measured.
Data Assessment
Audit data quality, identify gaps, and define what labelled data or API access is needed.
Two-Week Prototype
A working proof of concept on your data. If it does not hold up, we tell you before you invest further.
Production Hardening
Input validation, output filtering, rate limiting, cost monitoring, and fallbacks for when the model is wrong.
Monitoring & Retraining
Performance tracking, drift detection, and a retraining plan as your data grows.
Which technologies do we use?
Battle-tested tools we rely on to deliver.
How much does it cost?
MVP / Launch: from ~$3k
Typically 4–8 weeks
LLM integrations usually fit the MVP tier. Custom model training and production ML systems typically sit in Product / Growth or above, depending on the data.
Typical starting points, not quotes. Final pricing depends on scope and is fixed in writing after a discovery call.
See all pricing tiersFrequently Asked Questions
Common questions about this service.
How much does AI integration cost?
A focused LLM integration with a custom UI and backend usually fits our MVP tier (from ~$3k) and takes 2–4 weeks. Custom model training depends heavily on data and typically lands in the Product / Growth tier or above.
Will AI actually work for my use case?
Sometimes it will not, and we would rather tell you early. That is why we validate the use case and build a two-week prototype on your data before any larger commitment.
What happens to my data when using OpenAI or Claude?
Their API tiers do not train on your data by default. For highly sensitive data we can deploy open-weight models in your own cloud account so data never leaves your infrastructure.
Where to Next
Services teams usually pair with this one.
Further reading: AI & ML articles on the DanixSoft blog
Industries we build this for
Next step
Tell us what you are building.
A free 30-minute discovery call, then a written scope and a fixed-price quote — before you commit to anything.