Data Engineering involves designing, building, and maintaining the infrastructure for processing data at scale.
Data Engineering focuses on creating and managing the systems and processes needed to collect, store, transform, and analyze large volumes of data efficiently. This includes developing data pipelines, ensuring data quality, and providing the foundation for data-driven decision-making.
Innverse helps private and public data engineering leaders make
better, more affordable, and more accessible for millions of people around the world.
Unlock the value of your data with data engineering software
Machine learning projects often involve uncertainty, technical complexity, and significant execution risks. Without the right in-house AI expertise, it can be challenging to successfully plan, develop, and scale a custom AI solution that delivers real business value.
We have delivered more than 100 custom AI solutions across 20 countries worldwide and developed the national AI strategy for the Government of Estonia. With proven experience in AI development services and enterprise AI implementation, our team has the expertise to confidently support and execute your AI project end-to-end.
Our customers appreciate that we provide more than just regular big
data analytics
services
Besides being top-notch specialists in custom AI solutions and machine learning development, we offer a full spectrum of AI consulting and business strategy services. Our team of expert business consultants works closely with you to understand your unique business challenges, align AI initiatives with your strategic goals, and ensure the final AI solution meets both technical requirements and stakeholder expectations.
By combining enterprise AI implementation, predictive analytics, and end-to-end AI development services, we help organizations maximize ROI, streamline operations, and scale AI-driven solutions effectively. From data engineering and computer vision to natural language processing, our solutions are tailored to deliver tangible business outcomes and long-term competitive advantage.
Data engineering services
At Innverse, we deliver end-to-end data engineering services, building scalable data platforms both on-premise and in the cloud for enterprises like Elisa and Banglalink, as well as fast-growing startups such as Hepta. We design and implement high-performance data pipelines and modern data infrastructure tailored to diverse data types, analytical workloads, and complex business workflows.
By combining advanced data architecture, cloud data engineering, and big data technologies, we ensure efficient data storage, transformation, and processing. Our strong expertise in analytical systems—distinct from traditional operational environments—enables us to deploy optimized solutions that support real-time analytics, business intelligence, and data-driven decision-making, helping organizations achieve their goals with agility and precision.
We provide data engineering solution
Data labeling
Data Labelling involves adding tags or annotations for machine learning.
Data architecture
Data architecture designs the structure and flow of information systems.
AI strategy
AI strategy outlines plans for implementing artificial intelligence initiatives effectively.
Piloting
Piloting involves testing small-scale implementations to assess feasibility and effectiveness.
Scaling
Scaling refers to expanding and optimising systems or operations for growth.
NLOps
Natural Language Processing, involves analysing and understanding language data.
Data collection
Data collection entails gathering information for analysis, often from diverse sources.
App development
App development involves creating software applications for various platforms and devices.
It means that we are with you from the start to the finish of the final
solution
From validating ideas on the business side to creating a strategy that is based on them. Making sure
everything is ready from the data side from quality, quantity, engineering and scalability.
We set up all the necessary MLOps infrastructure for initial pilots and scale successful pilots.
Of course, we develop the actual AI models producing the desired output and the supporting
applications to exploit the output of those models.
AI for business: Reinvent what's possible
AI is rapidly accelerating into a global mega-trend, transforming industries, reshaping businesses, and redefining how we live and work. Organizations that invest in a strong data and AI foundation are positioned to lead this new era of digital innovation, enabling them to reinvent processes, enhance decision-making, and achieve unprecedented levels of performance, efficiency, and scalability
At Accenture, companies are guided from AI interest to actionable strategies that deliver measurable business value. Through responsible AI adoption and clear business-use cases, organizations receive end-to-end support—preparing their data, teams, and workflows for AI-driven transformation. With a secure, cloud-first digital core, businesses can unlock continuous reinvention, improved resilience, and sustainable growth powered by advanced analytics, automation, and enterprise AI solutions.
Artificial Intelligence
A new era of generative AI for everyone
In today’s fast-evolving digital landscape, generative AI is reshaping how businesses innovate, operate, and grow. At INNVERSE, we believe we are entering a new era where generative AI is no longer limited to tech giants or research labs—it is becoming an accessible, practical, and transformative resource for everyone.
We provide the architectural design for a data platform depending on your
required use cases, leaving the design flexible enough to support changing requirements and new use
cases in the future.
We design the platform from both the hardware and software viewpoints, considering the performance the
underlying hardware can provide and the workloads that the software has to support. The technological
landscape is in constant development as new hardware, cloud services and technologies open the doors for
performance increases and completely new use-cases.
Data platform design involves architecting robust infrastructures to collect, process, and analyse data
efficiently. It encompasses aspects like database design, data integration, and scalability, ensuring
that the platform meets business needs and supports advanced analytics, machine learning, and real-time
processing capabilities for informed decision-making.
Data platform engineering
Our experienced data engineers are familiar with the technologies widely used in
the cloud and on-premise data architectures.
We can realize a data architecture from scratch or develop additional capabilities to an existing data
platform, such as data storage layer developments (data lakes and warehouses), data pipelining (ETL jobs
or complex batch processing of data) or analytical components (BI tools and AI model deployment).
Data platform engineering involves designing and building scalable and reliable
infrastructures to manage and process large volumes of data. It encompasses tasks such as data
architecture, pipeline development, and infrastructure management, ensuring optimal performance and
reliability for data-driven applications and analytics initiatives across the organisation.
Data integration
Businesses often collect data in various distinct locations and technologies, such
as on-premise relational databases, CRM tools, analytics tools, object storage and so on.
This may work well for operational tasks, but to develop analytics tools and AI models to generate
insight from these data, it’s often necessary to integrate said data to a common platform where
analytical and operational workloads can be kept separate and the data from various sources used
together.
We work with our partners to understand the nature of said data, develop an integration strategy and
realize this in either a new architecture or in an existing one.
Data integration involves combining data from different sources to provide a
unified view. It ensures consistency, accuracy, and accessibility of data for analysis and
decision-making processes within an organisation.
Data storage layer design
Different data and different use cases require different storage technologies. Whether it’s structured
data that should be kept in a data warehouse in columnar format or unstructured data like images, video
and audio, which is better kept in a data lake.
We design the appropriate storage system with fast interconnects that enable the analytics tools to
access the data efficiently, providing the required performance.
Data storage layer design entails architecting a robust and scalable infrastructure to store and manage
data effectively. It involves selecting appropriate storage technologies, designing data schemas, and
implementing strategies for data partitioning, replication, and backup to ensure data reliability,
availability, and performance for diverse application requirements.
Data pipeline development
Data pipelines are used for ETL jobs, and batch processing of data in analytics and machine learning
workloads.
Good data pipelines are performant, robust and lend themselves well to monitoring and extending when
requirements change.
Data pipeline development involves designing and implementing automated workflows to ingest, process,
and move data from source to destination systems. It encompasses tasks such as data extraction,
transformation, and loading (ETL), orchestrating data flow, and monitoring pipeline performance to
ensure timely and accurate data delivery for downstream analysis and applications.
Big data
The term itself is loosely defined, but quite clear from the perspective of the challenge – handling big
data requires different, and far more complex, tools from small data.
At Innverse, we have extensive experience working with diverse types of data and understand when the use
of a more complex toolset is justified, ensuring it delivers value that outweighs the additional development
and maintenance costs.
And if you really need it, we can help you make the right choices and build the system that helps you
solve your problems.
Big data refers to large volumes of structured and unstructured data that cannot be processed using traditional methods. It involves capturing, storing, and analysing data to uncover patterns, trends, and insights that can inform decision-making and strategy in various domains.
Our projects
Visualising and analysing big datasets
A conversational interface with its own voice, Annika takes calls from
clients, listens to what they have to say, and directs them to the best course of action.
This is done using multilingual speech recognition to translate speech to words, transformer based
NLP models to understand the content of a customer’s sentence and non-autoregressive Transformer
based text to speech models that provide Annika with her signature voice.
Building a job search and career development platform requires quite a bit of
data collection - user profiles, job descriptions, cover letters, etc. We designed a system that
takes user provided documents - CV-s, cover letters, job and education descriptions, etc. - as input
and, using transformer based language models, extracts the relevant information that fits the data
model of the platform.
Big data analytics solution for discovering patterns
The machine learning system learns from expert auditors and helps to detect
potential tax fraud. This solution is deployed at the Estonian Tax and Customs Board of Estonia.
System for optimising business operations through data-driven
Our system helps power line utilities to inspect their power grids with the
help of drones to capture data and a specialized inspection platform called uBird to analyze it.
The Innverse team brings extensive experience in machine learning development, AI implementation, and custom AI solutions, enabling us to hit the ground running on your project. With a team of highly qualified specialists, we don’t waste time figuring out how to work efficiently — we already know the best practices, tools, and workflows to deliver results quickly and effectively.
We carefully allocate the right experts to your project based on their specific skill sets, whether it’s for one day or six months, ensuring that your AI initiative progresses smoothly and efficiently. By leveraging our deep expertise in AI project execution, predictive analytics, and enterprise AI solutions, we can significantly shorten your learning curve and accelerate the delivery of high-impact, business-ready AI solutions.
Unlocking the potential of AI
Since our inception, we’ve successfully built a reputation of trust, reliability and
of
delivering exceptional services. We are progressively diversifying into new markets with our
battle-hardened methodologies. Every day, we work to empower our customers to get the maximum
out of
technology.
We challenge, we innovate, and we continue to deepen our knowledge and expertise to
realize the best value for our customers. We do this through a culture that cultivates a
relationship-based approach to helping people and businesses be successful.
Waseem Ahmad
Frequently ask question
Discovering the right process to be enhanced with AI may be an unusual task for business
people.
People should come to us when they have a business problem where they intuitively feel that the
solution could be hidden in data and that it can not be solved by writing a couple of simple
rules. If this is the case, AI might be the solution.
To actually define an AI use case, business leaders will need the help of an AI team, who judge
if and how to proceed with the problem enhancement; the problem owner who knows the most about
the issue; and technical specialists who understand how the problem described by the problem
owner can be interacted with in the technical world.
Data collection is most often on the client side as it is connected to
the peculiar business problem to be solved we help as much as we can, especially if it is a data
source we have worked with before.
Labeling can be on the client side if very specific knowledge is required for labeling or it can
be outsourced to a labeling company or us. Hybrid solutions are also available, and in all
cases, we provide proper labeling training and guidelines tailored to the computer vision task.
If you imagine the process made up of iterations of the cycle: data
collection, labeling, model development + training, testing & evaluation and deployment into the
final solution, usually the first 2 steps take 20-50% of the time (the smaller the project /
standard problem, the higher the percentage) while the split between the latter 3 is really
dependent of the novelty of the problem being solved, the required performance level and the
complexity of deployment. The AI project range is from 3 months to multi-year collaborations.
During the project evaluation, we provide a feasibility assessment, and
for problems in which we have experience, we can provide a more precise prediction and discuss
the forecasted minimum level of performance.
How can we help you?
Are you ready to push boundaries and explore new
frontiers of innovation?
Let's Talk
Why contact us?
Validate your situation with our business advisors and IT executives
Understand business results, not just technical implications
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