Knowledge / Software & AI

Software & AI Development — Knowledge Base

Practical guides to building custom AI and LLM systems: what they cost, whether to build or buy, how RAG and fine-tuning differ, on-premises versus cloud deployment, document processing, and how to choose an AI development partner. Written by Haink, which builds production AI software and supplies the GPU infrastructure it runs on.

Software & AI Development Services →

Looking for the services themselves? See LLM applications & RAG, document intelligence, AI & machine learning, and more on the Software & AI overview.

Guides

• How Much Does It Cost to Build a Custom AI or LLM Solution? — Cost drivers, typical ranges by project type, and build-vs-run economics including on-premises GPU.
• Build vs Buy: Custom AI/LLM Application or Off-the-Shelf? — A framework for when to build, when to buy, and the hybrid layer most companies need.
• RAG vs Fine-Tuning: Which Should You Use? — What each does, when to use which, and why most production systems start with RAG.
• How to Build a Production-Grade RAG System — The full pipeline — ingestion to evaluation — and the mistakes that keep RAG demos from production.
• On-Premises vs Cloud LLM Deployment for Enterprises — Privacy, cost at scale, latency and control — and when each approach wins.
• How AI Document Processing Works (IDP Explained) — OCR, layout models and LLMs combined — accuracy expectations and handling messy documents.
• How to Choose an AI Development Company — Production track record, evaluation discipline, deployment options and red flags.
• How Long Does an AI Project Take? — Realistic timelines from discovery to production and what speeds projects up or slows them down.
• AI for KYC and Identity Verification: How It Works — How AI automates KYC — document authenticity, face matching, liveness and behavioral analysis — balancing fraud reduction and conversion.
• AI Underwriting and the EU AI Act High-Risk Line — Which underwriting decisions fall under Annex III, why the fraud exclusion depends on system boundaries, and what the deferral to December 2027 changes.
• Agentic Document Processing and Auditability — An agent picks its own sequence; an audit needs the same input to give the same output. Where the line sits and what to log.
• Clinical Document Extraction Under Validation — Reading the record is the easy part; the system doing the reading has to be validated. Why cloud fails on change control, and what air-gapped really requires.
• Due Diligence Document Review: Deal vs Continuous — Two unrelated processes share one name; only the continuous one is an automation problem. With the payback threshold and the error asymmetry.
• Invoice Processing Automation and E-Invoicing Mandates — Mandates are deleting the extraction problem on a published timetable; what survives is matching, exceptions and a permanently mixed estate of formats.
• Document Data Capture Methods: Which to Choose — Template, trainable extraction or a vision model, decided by form variability rather than novelty — with the field-error arithmetic and when a rule beats a model.
• How AI Claims Processing Works (2026 Guide) — Fifteen claims use cases sorted into three groups by cost of error, when a rule beats a model, and why claims handling is absent from Annex III but still needs a documented classification.
• MLOps: Getting Machine Learning Models to Production — Why most ML models never ship, and the engineering — pipelines, CI/CD, monitoring, drift, retraining — that gets them to production.