Artificial Intelligence (AI) is a broad term that refers to machines or software that can perform tasks that normally require human intelligence — like recognizing patterns, making predictions, or understanding language.

A common type of AI is machine learning (ML). This is where computers learn from data — for example, learning what types of customers are most likely to purchase, or which products to recommend — without being explicitly programmed for every rule.

Agentic AI (or AI agents) goes one step further. These are intelligent systems that can reason, plan, and take action across multiple steps. For example, an agent could research a topic, summarize the results, and then send a report — all on its own. It’s like giving the AI a task, not just a question.

You may also hear terms like AGI (Artificial General Intelligence) and ASI (Artificial Superintelligence). These are future-facing concepts:

• AGI would be AI that can think and learn like a human across any topic.

• ASI would go beyond human intelligence entirely.

At Transformly, we focus on practical, business-ready AI — including agentic AI — that helps companies make smarter decisions, save time, and unlock real value today.

Yes — building agentic AI is one of our specialties.

At Transformly, we design and deploy intelligent agents that don’t just predict or analyze — they act. These AI systems can reason through multi-step tasks, use tools like APIs or databases, and carry out workflows autonomously or alongside your team.

For example, we’ve built agents that:

• Score customer service calls in real time

• Help clients complete forms by pulling relevant data

• Answer complex customer questions through chat interfaces, from the perspective of a user persona

• Trigger workflows like follow-ups or recommendations

We combine strong underlying models with smart interfaces like chatbots and dashboards, so your team can interact with the AI naturally — no code required.

The kind of data you need depends on the problem you're trying to solve. For example:

• Customer retention models need historical customer interactions, churn status, and transaction history.

• Inventory forecasting relies on sales data, lead times, seasonal patterns, and supplier information.

• Compliance or call grading models require transcripts or audio of past calls with labeled examples.

How much data is enough:

• For traditional machine learning (which is most AI), a few thousand data points can be sufficient.

• For more complex problems or deep learning, tens or hundreds of thousands of rows may be ideal.

• What's more important than sheer volume is data quality, consistency, and having the right features.

At Transformly, we help clients assess the readiness of their data and, if necessary, assist in building the right pipelines and strategies to make AI implementation feasible—even with limited datasets.

And yes, we even clean data, fill in blank values, analyze/remove outliers, and do all sorts of pre-processing of your data so that the final output is optimized.

Since we build custom AI models, there is no "typical."

However, most projects take 2–4 months, which includes developing a tailored AI strategy, securely accessing your data, ensuring privacy and compliance, cleaning and preprocessing the data, and building the AI models themselves.

Once the core models are in place, we often install a layer of agentic AI—such as interactive chatbots or dashboards—so your team can "talk to the model" directly. This final step transforms static insights into a dynamic, everyday tool your team can actually use.

Once the project is started, we only need 1 hour every other week to give you and your team an update, share insights, and get any questions answered.

In the first few weeks of the project, we may need 3-5 hours (in total) as we get access to the data, set up secure environments, and become familiar with the data and your business.

Yes—our engineering team specializes in API‑driven integrations with CRMs, ERPs, and cloud platforms.