Neoteric

Sales teams generate valuable information in every customer conversation. The challenge is turning it into something useful. 📊

For a metal trading company, we developed an LLM-powered pipeline for sales meeting analysis.

The solution helps transform conversations into structured insights that can support sales teams and make important information easier to use after the meeting.

It's another example of where LLMs can bring value beyond the typical chatbot interface, by becoming part of an existing business process.

📖 Read the case study:
https://neoteric.eu/portfolio/enhancing-sales-meetings-analysis/

The AI model is often the most visible part of an AI product. It's rarely the whole product. 🤖

Behind a reliable AI solution there may be data pipelines, integrations, permissions, monitoring, evaluation, fallback scenarios, UX decisions, and plenty of regular software engineering.

Users don't need to see any of that. They just need the product to work.
And that's probably a good way to think about AI development: the technology can be complex behind the scenes while the experience stays simple.

AI agents are everywhere in business conversations. But only 11% of companies have actually put them into production. 🤖

The gap between a promising demo and a system people can rely on every day is still significant.

Companies that successfully make that transition tend to focus on a few fundamentals: a clearly defined workflow, controlled access to data and tools, human oversight, and measurable business value.

Because getting an agent to perform a task once is relatively easy. Getting it to perform reliably, safely, and repeatedly is where the real work begins.

📖 See what the 11% are doing differently:
https://neoteric.eu/blog/ai-agents-in-production/

AI is changing the way we work. 💡

Some of the biggest opportunities come from helping teams reduce repetitive tasks, access knowledge faster, and make better-informed decisions.

When AI fits naturally into existing workflows, people gain more time for work that requires experience, creativity, and judgment.

That is where technology can make a real difference. 🚀

AI Agent or AI Workflow? 🤖

It is a question more and more companies are asking as they explore new AI opportunities.

AI Agents offer greater autonomy, but structured workflows are often easier to control, maintain, and integrate with existing processes.

The right choice depends on the business goal, available data, acceptable risk, and the complexity of the task.

📖 Read more:
https://neoteric.eu/blog/ai-agents-vs-ai-workflows-what-makes-more-sense

What can AI tell you before your customers decide to leave? 📉

For one of our clients, predictive analytics helped identify customers at risk of churning and supported earlier, more targeted action.
The results:
✅ More than 20% improvement in retention
✅ 10× return on investment
✅ faster identification of at-risk customers
✅ better-informed business decisions

This case shows how predictive AI can deliver clear, measurable value.

📖 Read the full case study:
https://neoteric.eu/portfolio/how-predictive-models-help-businesses-reducing-churn-by-more-than-20-with-10x-roi

The first weeks of an AI project often shape everything that follows. 🧭

A well-structured discovery workshop helps teams align around business goals, validate ideas, identify high-impact use cases, and understand technical constraints.
It also creates space to define what success should look like before development begins.

A little more clarity at the start can save a great deal of time later. 💡

📖 Read the full article:
https://neoteric.eu/blog/ai-discovery-workshop-deliverables-for-two-weeks

Why do so many promising AI projects never reach production? 🤔

The most common reasons include:
⚠️ Unclear business objectives
⚠️ poor integration planning
⚠️ limited access to quality data
⚠️ no roadmap beyond the proof of concept

A good demo is only the beginning. Turning it into a reliable product requires a clear strategy, the right processes, and close collaboration across teams.

📖 Read the article:
https://neoteric.eu/blog/why-most-ai-proofs-of-concept-never-reach-production

Supply chains rarely stand still. 🌍

New regulations, market changes, and operational disruptions mean risk managers constantly need reliable, up-to-date information.

We partnered with our client to build an AI-powered MVP that helps identify risks faster and brings relevant information together in one place.

The solution supports experts in making more confident decisions without spending hours searching through data. 📊

📖 Read the full case study:
https://neoteric.eu/portfolio/ai-powered-mvp-supply-chain-risk-managers

The most successful AI projects usually have one thing in common. 💡

They begin with a clear understanding of the business problem.

Before discussing models or frameworks, it helps to ask:
🔹 Which problem are we solving?
🔹 Who will use the solution?
🔹 How will we measure success?

Technology matters, but clear objectives come first.

When the foundations are strong, AI has a much better chance of creating lasting business value. 🚀