Stack46 builds practical AI and machine-learning features into production software — LLM-powered tools, semantic search, recommendation and automation — engineered in Python on AWS, Google Cloud and Azure. Real features shipped into real products, not proof-of-concept demos.
Book a Free Scoping CallLLM & GenAI feature integration
Semantic search & retrieval (RAG)
Recommendation & personalisation engines
Workflow & document automation
Data pipelines & model deployment (MLOps)
AI features embedded in web & mobile apps
We pin down the real use case and whether AI is genuinely the right tool — no AI for AI's sake.
A working proof against your real data to validate quality before full build.
The feature engineered into your product with evaluation, guardrails and monitoring.
Shipped to production on scalable cloud infrastructure, with ongoing tuning.
AI work usually starts Time & Materials while scope is proven, then moves to Fixed-Price or Retainer.
Practical, product-focused AI: LLM-powered assistants and chat, semantic search and RAG, recommendation engines, and document or workflow automation — all built into real applications rather than standalone demos.
Both, depending on the job. Most product features are built fastest and most reliably on established models and APIs; we build or fine-tune custom models only when the use case genuinely needs it.
Yes. Stack46 integrates AI features into existing web and mobile apps, connecting to your current data and backend rather than requiring a rebuild.
We ground models in your own data using retrieval (RAG), add evaluation and guardrails, and keep a human in the loop where it matters — so outputs stay accurate and trustworthy in production.
Yes. Stack46 builds on your own cloud infrastructure across AWS, Google Cloud or Azure, with data handling designed for privacy and, where required, no data used to train third-party models.
AI work usually begins on a Time & Materials basis so scope is proven against real data first, then moves to a Fixed-Price build or Retainer once the approach is validated.
