AI Development
Applied AI for internal operations — AI agents versus chatbots, document processing and extraction, and knowledge assistants — written for teams evaluating where AI genuinely fits a workflow versus where it's a solution looking for a problem.
Guides in this topic
- AI Agents vs. Chatbots: What's the Real DifferenceChatbots, LLM chat assistants, and AI agents explained with a clear technical line, a comparison table, and a decision framework for your use case.
- AI Knowledge Assistants for Internal Operations: A Practical GuideA practical guide to internal AI knowledge assistants: how RAG grounds answers in your documents, rollout strategy, governance, and fit criteria.
- Automating Document Processing and Data ExtractionA practical guide to automating invoice, contract, and form processing — extraction methods, accuracy trade-offs, architecture, and ROI.
- OpenAI vs. Anthropic: Choosing a Foundation Model Provider for Your ProjectA practical comparison of OpenAI and Anthropic for applied AI projects — what actually differs, and how to choose based on your workflow.
Related services, industries & solutions
- AI AgentsBounded AI agents built around a fixed tool list, permission tiers, human approval gates, and full decision-trace logging for auditable automation.
- AI DevelopmentProduction-oriented AI systems, assistants, RAG platforms and intelligent automation for Nordic businesses.
- Business Process AutomationRule-based workflow automation for approvals, document routing, and system integrations, engineered without AI where AI isn't the real problem.
- AI Agents (Architecture Pattern)The AI agent architecture pattern: tool-calling schemas, permission tiers, bounded orchestration, and trace-level evaluation for scoped LLM action-taking.
- Anthropic (Claude)How North Tech Labs integrates Anthropic's Claude models into business systems — real capabilities, genuine limitations, and the controls we build around them.
- OpenAIHow North Tech Labs integrates OpenAI's models into business systems — real capabilities, genuine limitations, and the controls we build around them.
- Retrieval-Augmented Generation (RAG)How North Tech Labs designs RAG systems — chunking, embeddings, retrieval evaluation, and answers grounded and cited in internal documents.
- AI Knowledge AssistantA representative reference architecture for a retrieval-augmented Q&A system answering employee questions from internal documents, with citations.
- Document Processing PlatformA reference architecture for extracting obligations, dates, and risk flags from contracts, with human review before any extraction is trusted.
- AI Operations PlatformA representative reference architecture combining workflows, document processing, and AI-assisted automation with human review at every consequential step.
Frequently asked questions
Should we start with an AI agent or a simpler chatbot/automation?
That's exactly the decision the AI agents vs. chatbots comparison is scoped to answer — it depends on whether the task requires multi-step reasoning and tool use, or a bounded, scripted interaction. Most teams overestimate which one they need.
Do these guides cover building an AI product, or using AI internally?
Internal and operational applied AI specifically — document processing, internal knowledge retrieval, workflow assistance. Building and selling an AI-powered product to customers is a different discussion with different tradeoffs, not covered here.
How do we know if a workflow is actually a good fit for AI?
Each guide opens with a direct-answer framing of exactly this — the pattern is a task with judgment-based, non-deterministic steps and a tolerance for human review, not a fully deterministic process that a simpler rule-based system already handles well.
Have a question about ai development?
These guides cover the general pattern — the fastest way to get a specific answer for your situation is to ask us directly.