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During the development phase, the reseller builds the AI solution according to the approved design while following THiNK coding standards and best practices. Development should follow an iterative approach, allowing continuous testing and feedback throughout i...

Quality Assurance (QA) ensures that the solution meets functional, technical, security, and performance requirements before deployment. Testing should be performed continuously throughout development rather than only at the end of the project. Types of Testing...

Deployment is the process of moving the completed solution into the client's production environment where it becomes available to end users. Deployments should follow standardized procedures to minimize operational risks and service disruptions. Pre-Deployment...

Project closure formally concludes the implementation process and confirms that all contractual and technical obligations have been fulfilled. The objective is to ensure a smooth transition from implementation to operational support. Closure Activities The res...

The THiNK Reseller Program recognizes outstanding partner performance through a structured incentive and rewards framework. The objective is to encourage high-quality project delivery, continuous improvement, and long-term engagement within the THiNK ecosystem...

Every reseller should establish a local development environment capable of supporting AI application development and deployment. A standard development environment includes: Visual Studio Code (VS Code): Primary integrated development environment (IDE) for wr...

FastAPI FastAPI is the recommended backend framework for developing RESTful APIs that power THiNK AI solutions. It is lightweight, high-performance, and well suited for AI applications requiring rapid response times. FastAPI is commonly used to: Build chatbot...

The THiNK Reseller Program operates through standardized processes that ensure effective communication, consistent service delivery, partner accountability, and continuous improvement. These operational guidelines enable certified resellers to deliver high-qua...

Agentic RAG systems combine: ●       Reasoning models  ●       Retrieval systems ●       Tool use (increasingly via MCP) ●       Memory (short-term, long-term, and now tiered/benchmarked) ●       Workflow orchestration ●       Governance controls ●       A har...

Control > Convenience: Explicit systems outperform hidden automation. Context > Prompting: Prompt engineering helps. Context engineering scales,  it is now treated as the discipline of designing everything a model sees on every inference call, not just the ins...

Agentic systems are AI systems that can: ●       Maintain internal state ●       Plan and decompose tasks ●       Use tools ●       Retrieve knowledge ●       Reflect and self-correct ●       Operate under constraints ●       Escalate to humans when needed Con...

The diagram presents AI as a progression of interconnected layers, from traditional rule-based systems to increasingly autonomous and goal-driven AI. Here is a paragraph explaining each layer: 1. Classical AI Classical AI represents the foundational approache...

The AI Factory Stack illustrates the key components required to transform a general-purpose Large Language Model (LLM) into a reliable, production-ready AI system. Each layer adds a specific capability, moving from the core intelligence of the model to ...

The Modern AI Ecosystem – Tools diagram presents the modern AI landscape as a collection of interconnected layers and capabilities. Rather than being built around a single model or platform, modern AI systems combine specialized tools for intelligence, dat...

NEW IN 2026  Harness engineering emerged through 2026 as a distinct discipline, formalized in surveys covering 100+ papers and 23 production systems. The core claim: at fixed model capability, agent-computer interface design materially changes benchmark outcom...

Six Harness Components ●       State and persistence — durable memory and workflow checkpoints across long-running runs. ●       Security and governance — permissioning, sandboxing, credential scoping. ●       Orchestration and tool routing — how planner/execu...

Large Action Models (LAMs) Models designed to reason about actions, workflows, and consequences. Used for: ●       Browser automation ●       Enterprise workflows ●       Multi-step operations ●       Transaction execution Model Specialization Role Bes...

Layer Recommended Options LLMs LLaMA 4, Qwen 3, Mixtral, DeepSeek Fast Inference Groq, vLLM, Ollama Agent Framework LlamaIndex Workflow Engine LlamaIndex Workflows API Layer FastAPI Vector DB Qdrant, Chroma Graph ...