For AI models to be consistently accurate and high-performing, the pipeline has to start with a well-curated dataset. Our data preparation services ensure you start strong with data that is:
A well-designed and expertly executed data preparation pays off beyond model accuracy and grounded confidence in your data. It also improves training efficiency, speed, and cost:
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Data preparation is where most AI challenges can be solved. Instinctools AI engineers seize this opportunity by shaping siloed, chaotic data into a powerful fuel for an ML model.
Our data scientists begin with exploratory data analysis (EDA) to understand patterns in your raw data and catch obvious errors right away. This early diagnostic measure lays the groundwork for a steady, rework-free course for the rest of the pipeline.
We establish a foundation your model can learn from, whether it’s consolidating siloed data from internal and external sources or web-scraping public datasets, if in-house data is scarce.
Our AI engineers combine automation and targeted human input where it matters most, balancing data annotation speed and precision. To maximize the relevance of the model output to your business, we align the labeling taxonomy and annotation rules with your domain.
Even the most carefully collected and labeled datasets can hide quality issues that trip up the model’s performance and accuracy. Our AI engineers address them up front.
“Every record should contribute to the model’s learning” is our imperative for data preparation for AI/ML. Once your data is labeled, de-biased, and cleaned, we focus on tailoring the training dataset to a specific task or process the ML model will support.
Quality over quantity or vice versa? Our data scientists know that both are equally vital for efficient ML model training. If cleaning leaves your dataset thin, we expand it with realistic variations of the approved samples.
Secure top-tier data preparation for your AI initiative
Preparing data for generative AI systems requires additional steps to enable models to better grasp context and retrieve information efficiently. Our team goes further than baseline preparation.
Delegate the hassle of GenAI data prep
How does one avert an ML model from bringing irrelevant context that only burns tokens away? At *instinctools, we start early and cut off data-related issues contributing to excessive compute resource consumption by right-sizing data chunks, pre-filtering clean data, introducing user role-aware metatags, and more.
These data preparation measures benefit any AI-driven solution, but their impact on the cost-efficiency ratio stands out the most when multi-agent systems are in play.
nothing extra
A better AI system without breaking your budget is a click away
Not all imperfect data is a defect. For conversational AI chatbots, real-world noise, such as typos, colloquial abbreviations, slang, grammar slips, and syntax errors, is what trains models to understand humans better, improving intent recognition accuracy and response precision. Otherwise, the solution will degrade user experience with frequent “I don’t understand” responses.
If your dataset is low on incorrect and inconsistent entries, our data scientists deliberately inject them to enable your AI assistants to handle flawed queries just as accurately and swiftly as word-perfect ones.
Here’s what our AI engineers see businesses get wrong and how we help get it right.
ML models can process unstructured data, but it’s counterproductive for AI development. We put a premium on data preparation to save you from the “garbage in, garbage out” pattern, avoiding underperforming, brittle models.
While dataset size matters for efficient model training, volume without relevance wastes cycles. Our data scientists prioritize more use-case-relevant data.
Format consistency doesn’t fix semantic skew. We ensure fairness long before model training starts by spotlighting bias detection at the data exploration stage.
PoCs need statistically representative, properly prepared data samples; otherwise, it’s a proof of failure. Rather than cutting corners on data preparation, we help businesses fast-track the initial stages with rapid prototyping and other AI-driven SDLC practices.
Addressing issues in the later development stages will cost you x10-100 times more. By doubling down on data preparation from the get-go, we save you from expensive and time-consuming reworks down the line.
Data preparation is an ongoing process. As the ML model evolves or new data becomes available, you need to revisit and refine your data preparation steps. Our AI engineers take this burden off your plate.
Say “yes” to tried-and-true data preparation services
For PoCs that don’t require niche proprietary datasets, we apply safe shortcuts, drawn from years in the field, to move faster without compromising quality.
Instinctools delivered everything on time and was very flexible towards changes in scope during the project work. The team was easy to work with and had a quick response time.
The expectations for the quality of the initial product were very high. I think *instinctools did a great job ensuring those expectations are met. We met the developers we were going to be working with and it quickly became apparent that they are very qualified and were able to deliver the vision that we had from our side for the product. They clearly told us what they were going to do, and if there were questions or problems along the way, they clarified them really quickly thanks to transparent communication.
We had a tight delivery deadline and *instinctools has been able to find another developer and assign him to our project from one day to another. And we’ve been able to successfully deliver this project. When the partner is good, things are just getting done. And that was the case with *instinctools.
The quality has been good. It’s been on the expected level: things come on time, we have a good visibility on the things that *instinctools developers are doing and performing for us, communication is good. Wherever we see that we need some more exra resources, we have found *instinctools to be a good partner in helping us out on those areas.
I’ve been impressed by the available skillset, the flexibility to ramp up resources quickly, and the scalability to extend development teams on short notice. I look forward to continue collaboration with *instinctools and their contribution to our projects.
People at *instinctools are quite tech heads, which I like. They have used very advanced libraries, advanced techniques, advanced coding paradigms. So the advantage is that we get reusable code, that we get well-testable code, we get well-maintained code.
The *instinctools team exhibits the flexibility and professionality required for young companies. You can rely on their tested structures and processes that integrate nicely with your internal workflows. Being able to grow your team quickly with experienced professionals that start delivering value immediately and without a long interview process is a huge help. And personally, you will be working with a team of kind and interesting people.
The team is dependable when it comes to managing time and finances, consistently staying within the designated budget. We’re pleased with *instinctools. Their business analysts are exceptional. They serve as the spokespeople between technology and business, representing both sides effectively.
Instinctools is good at understanding the technical issues – once an issue is outlined, they do not need repeated explanation. They also do not simply accept a proposed solution, but they think about it and propose a better solution. I was really impressed by the custom interface they built for us – we outlined the requirements, and they implemented them in a user-friendly way that makes the interface a pleasure to use.
Instinctools does deliver on time and budget. The company proactively asks how they can support our efforts and provide ideas how to help us with very good candidates with expertise that either we requested or that instinctools identified to be missing.
The team demonstrated effective project management, timely delivery, and responsiveness to our needs. They established open communication to facilitate ongoing dialogue and held regular sprint meetings to keep stakeholders informed and engaged throughout the development process.
Besides ensuring your data undergoes labeling, cleaning, bias prevention, and is relevant to a specific use case, we also break it down into easily consumable pieces, generate embeddings, summarize, timestamp, and index the data to make it fully optimized for AI agents.
AI-ready data is traditionally clean, bias-free, and use-case-relevant. However, for conversational AI chatbots and assistants, prepared data must also include noise, inconsistencies, and errors. This “mess” is essential for the model to learn to process user queries with conversational abbreviations, typos, syntax errors, and grammar mistakes with the same precision as perfectly structured queries.
The five core steps of data preparation include: data exploration, collection, labeling, cleaning, and use case alignment. You may also need data augmentation if your prepared dataset turns out too small to successfully train an ML model.
