Editorial Policy itinai.com
At itinai.com, we take editorial integrity seriously. Our mission is to create trustworthy, useful, and verifiable content in the field of artificial intelligence, innovation, and product development.
Every article published on itinai.com undergoes human review and aligns with the principles below.
Our Editorial Principles
- Accuracy – We fact-check our content and update it when necessary.
- Transparency – We disclose the source, author, and publishing intent.
- Experience-first – Our content is written or reviewed by practitioners and domain experts.
- Human in the loop – No article is published without human editorial oversight.
- Clarity – We prioritize plain, accessible language and practical insight.
- Accountability – Errors are corrected. Feedback is encouraged and valued.
Submit a Correction or Suggest an Update
We welcome suggestions to improve our content.
If you’ve spotted a factual error, an outdated reference, or wish to propose an edit:
📬 Email: editor@itinai.com
All valid correction requests are reviewed within 72 hours.
In most cases, you will receive a reply from our editorial team.
Submit a News Item or Contribute Content
Want to submit a story, research highlight, or industry insight?
We accept contributions in the following formats:
- Short AI news (100–300 words)
- Research summary (with link to paper)
- Opinion/editorial piece
- Product case study (original only)
📥 Send your pitch to: editor@itinai.com
💡 Guest authorship is available — we credit all contributors.
Editor-in-Chief assistant
Editorial Review Process
Every piece of content published on itinai.com follows a structured editorial workflow:
- Drafting – Written by in-house authors or external contributors.
- Expert Review – Reviewed by a domain specialist (AI, product, healthcare, or law).
- Editor-in-Chief Review – Final oversight by Vladimir Dyachkov, Ph.D.
- Fact-Checking – Sources verified manually and/or via LLM-assisted tools.
- Markup – Structured data (
Article,Person,WebPage) is applied. - Publishing – With author attribution and publishing date.
- Monitoring – Regularly re-evaluated for accuracy and relevancy.
Note: If AI tools assist in drafting or summarizing, this is clearly disclosed.
User & Company Feedback, Corrections
We actively encourage users, companies, and institutions to report factual errors or request content updates.
How we handle it:
- Submissions are received
- An editor reviews the case manually within 72 hours.
- Verified changes are fact-checked again, optionally using AI models for cross-verification (e.g., citation match, entity comparison).
- If the correction significantly changes the context or outcome, we:
- Add a “Corrected on” notice to the article
- Publish a separate editorial blog post explaining the change in our Editor’s Blog
We do not silently alter content unless it’s a typo or formatting issue.
Propose a Story or Suggest an Edit
We believe in collaborative knowledge. Anyone can contribute insights or highlight gaps.
📬 To contribute:
- Factual correction – Use our correction request form
- Submit a news item – Email your pitch to editor@itinai.com
- Contribute a piece – See our Contributor Guidelines
We welcome:
- Original insights
- AI research summaries
- Localization use cases
- Startup/product case studies
Every submission is reviewed by humans. We may edit for clarity or add editorial context.
Get Involved
Follow us, contribute insights, or propose partnerships. We welcome collaboration from researchers, writers, and product leaders passionate about building ethical, usable AI.
Contact and Transparency
- Email: editor@itinai.com
- Telegram: @itinai
- LinkedIn: itinai.com company page
You can also explore:
Editorial Picks
Monetization for Food Truck Operators Using AI
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How to Monetize a Small Audience on Social Media
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AI Document Migration Assistant
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How Alibaba’s Qwen3.8-Max Solves Scaling Limits in Multimodal AI
Alibaba’s Qwen team released a preview of Qwen3.8‑Max‑Preview on July 19 2026, describing it as a 2.4‑trillion‑parameter multimodal model that handles text, images, video and documents. The preview is live and can be bought through Alibaba’s Token Plan subscription…
Liquid AI’s LFM2.5-8B-A1B Shrinks On‑Device AI, Boosts Speed
Liquid AI’s LFM2.5-8B-A1B addresses the main pain points for developers who need powerful language models on limited hardware. The model packs 8.3 billion parameters but only activates 1.5 billion per token, which keeps compute and memory usage low…
AI-Powered Grant Writing Assistant
AI-Powered Grant Writing Assistant The clock is always ticking for nonprofits. A vital program might hinge on securing funding, yet grant writing often feels like a full-time job on top of the actual work of making…
Slow AI Audio? Stable Audio 3 Boosts Speed & Quality
Stable Audio 3 addresses common pain points for creators who need high‑quality, controllable audio without heavy compute or complex workflows. The release provides three open‑weight latent diffusion models—small, medium, and large—built around a new SAME autoencoder…
Multimodal Agent Struggles? Muse Spark 1.1 Solves on Meta API
Today many teams run their own LLM stack with open weights or pay‑per‑token APIs that charge high rates for reasoning. Muse Spark 1.1 changes the equation by offering a hosted, multimodal reasoning model through the Meta…
Stanford’s TRACE Fixes Agent Failures by Creating Synthetic RL Worlds
Agentic large language models often repeat the same mistakes because they lack specific skills that a task requires, such as checking a precondition before calling a tool or keeping track of multiple steps in a request.…
Logistics Coordinator – Answering queries related to shipping policies, warehouse rules, or routing processes.
Professional Summary As a Logistics Coordinator, I specialize in addressing queries related to shipping policies, warehouse rules, and routing processes. My role involves ensuring smooth operations and providing accurate information to clients and internal teams. Leveraging…
LLMs in CX: The Promise and the Potential Pains
Generative AI, such as Large Language Models (LLMs), presents significant opportunities and risks in the customer experience (CX) space. LLMs offer improved customer experience, cost savings, and increased efficiency, but challenges include accuracy, context retention, quality…
Need the Best LLM for Each Task? Sakana Fugu Does It
Many teams struggle with complex AI pipelines that require juggling multiple models, managing different APIs, and staying compliant when a provider changes access or policies. This adds overhead, increases risk of vendor lock‑in, and makes it…
Lost GPU Neocloud 2026: CoreWeave, Nebius, Lambda, Crusoe & Groq
The neocloud market now includes five distinct players, each with its own pricing, power footprint, hardware roadmap, contract style and third‑party ratings. For buyers the main pain points are opaque pricing, unclear capacity guarantees, and uncertainty…
Pros and Cons of Embracing Natural Language Processing (NLP) in Your Business
This Machine Learning Glossary aims to briefly introduce the most important Machine Learning terms – both for the commercially and…
Fast Multilingual Transcription Using NVIDIA’s Nemotron 3.5 ASR
NVIDIA’s Nemotron 3.5 ASR gives developers a single 600‑million‑parameter model that can transcribe 40 language locales in real time, removing the need to maintain separate checkpoints for each language or to swap models during runtime. The…
Boost Urban Function Prediction with city2graph, OSMnx&PyG GNNs
When working with point‑of‑interest (POI) datasets, analysts often need to quantify how crowded each location is, understand its proximity to infrastructure like streets, and explore the underlying spatial relationships through graph models. The typical workflow involves…
