Data engineering encompasses the development,
deployment, and management of data pipelines and
infrastructure
for processing and analysing large datasets.
Innverse helps private and public data engineering leaders make better, more affordable, and more accessible for millions of people around the world.
There are lots of unknowns and risks when it comes to machine learning. And you might not always have enough talent in-house to successfully execute your AI project.
We delivered more than 100 custom AI solutions in 20 countries worldwide and developed the AI strategy for the Estonian government, so we have enough competence to support you.
Besides being top-notch specialists in machine learning we have a team of expert business consultants ready to support you in achieving your business goals and help to develop the final AI solution that matches your stakeholders’ expectations.
At Innverse, we specialize in developing robust data platforms both on-premise and in the cloud, catering to the needs of large enterprises such as Elisa and Banglalink, as well as innovative startups like Hepta. Leveraging the right technologies tailored to different data types, analytical demands, and business workflows, we deliver solutions that meet the unique requirements of our clients. Our deep understanding of analytical workloads—distinct from traditional operational workloads—enables us to identify and implement the best technologies to efficiently store and process data, empowering businesses to achieve their goals with precision and agility..
Data Labelling involves adding tags or annotations for machine learning.
Data architecture designs the structure and flow of information systems.
AI strategy outlines plans for implementing artificial intelligence initiatives effectively.
Piloting involves testing small-scale implementations to assess feasibility and effectiveness.
Scaling refers to expanding and optimising systems or operations for growth.
Natural Language Processing, involves analysing and understanding language data.
Data collection entails gathering information for analysis, often from diverse sources.
App development involves creating software applications for various platforms and devices.
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 is accelerating into a mega-trend, transforming industries, companies and the way we live and work. Organizations that build a strong data & AI foundation will be better positioned to reinvent, compete and achieve new levels of performance.
Accenture helps companies move from AI interest to action to value, in a responsible way with clear business cases. We help companies get their data, people and processes ready for AI, with a secure, cloud-based digital core that allows for continuous reinvention and greater growth, efficiency and resilience.
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.
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.
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.
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 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.
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.
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.
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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.
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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.
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Read MoreThe Innverse team works with machine learning projects all the time, and has highly qualified specialists on board; thus, we can start implementing the project right away, not spending time learning how to be more efficient.
We already know who is good at what and who to include to efficiently deliver your project. This might mean including a specific person with a particular skill set for 1 day or 6 months.
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.
Why contact us?
Shaping the Future of Technology Your Gateway to Innovation and Excellence
65-E Sir Syed Town, FSD
+92 300 786 2325
info@innverse.io
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