SW Development ✅ IT Outsourcing ✅ Data Science ✅
Profinit is one of the leading companies in the field of Software Development, Big Data & Data Science, Consulting and IT Outsourcing. IDC Research ranked Profinit as the 3rd largest custom application developer in the Czech Republic.
Key clients: Erste Group, O2, BNP Paribas, KBC Group, Vodafone, Deutsche Telekom, Raiffeisenbank, Allianz, Société Générale, Prague Airport, CEZ Group, VIG (Vienna Insurance Group), Confirmo, Edenred, ING, NN, Darag, M.M. Warburg, My Community Finance, Confirmo, Bottomline, Nord/LB
Profinit's services:
- Custom software development
- IT Outsourcing & Nearshore services
- Application management outsourcing
- BI/DWH design & development
- Big data & data science
- Enterprise integration
Profinit's main clients include banking, insurance, pharmaceutical and telecommunications companies in Central and Western Europe. The company also services a number of other private and public-sector organizations.
Clients use Profinit's services and products in Germany, Austria, Benelux, the UK, and other countries, where Profinit operate in the nearshore mode.
The company develops and maintains vital internet banking systems and other bank applications, insurance policy management systems, B2B and B2C portals, EPM systems, fraud prevention solutions and even a mission-critical software system for an international airport.
For more information visit the following websites:
3 Languages
- English
- German
- Czech
7 Timezones
- GMT
- UTC
- ECT
- EET
- MST
- CST
- EST
headquarters
other locations
Centralized Log Management System Dev for IT Security Co
the project
"We were satisfied with the delivery, which met our requirements for a favorable price solution."
the reviewer
the review
The client submitted this review online.
Please describe your company and your position there.
I'm working as a Security Product owner (Security Monitoring department) in the largest Czech bank.
For what projects/services did your company hire Profinit, and what were your goals?
In order to comply with banking regulations, it was necessary to build one unified repository for various audit logs. Unify the logs into a unified structure. Logs of int/ext applications, infrastructure, network.. Ensuring the storage of the original message, message hash, immutability, anti-readability.
How did you select this vendor and what were the deciding factors?
Six-month tender process was held between several suppliers. The price was crucial.
Describe the scope of work in detail, including the project steps, key deliverables, and technologies used.
- 3 month analysis of our requirements and data with architects.
- 2 months POC - WMWare test environment
- Purchase of physical HW
- Integration with 90 source systems and tests (data loss, recovery, secondary tape backup and recovery, AVAST penetration tests).
- Migration of older logs from the original RDBMS solution
- Commissioning and subsequent prophylaxis after 6 months of operation
How many people from the vendor's team worked with you, and what were their positions?
- 4 developers
- 3 architects (oponent)
- 1 external architect consultant from ORACLE
Can you share any measurable outcomes of the project or general feedback about the deliverables?
We were satisfied with the delivery, which met our requirements for a favorable price solution. There are currently 2 separate Cloudera Hadoop solutions in HA mode. 48 physical servers
Describe their project management style, including communication tools and timeliness.
Both the analysis and the subsequent implementation were conducted properly and in a friendly spirit.
What did you find most impressive or unique about this company?
Quick response to specific bank entry - designing solutions and internal POC for demonstration and cost estimation in a short time.
Are there any areas for improvement or something they could have done differently?
Nothing:-)
Focus
Portfolio
Raiffeisenbank, Erste Group, Société Générale, Allianz, Prague Airport, M.M. Warburg & Co, CEZ Group, My Community Finance, O2, Vodafone, ING, NN, T-Systems, VIG (Vienna Insurance Group), Confirmo, Edenred, Darag, J&T bank, Nord/LB, Bottomline
Nearshore software development for Bottomline
In 2021, the Dutch company was looking for a strategic software development partner in the CEE region. Finally, in January 2022, it found it in Profinit.
Nearshore SW development for M.M. Warburg & Co
Project Brief
Develop custom software capabilities for a leading Germany private bank as part of a long-term nearshore partnership.
Business Needs
The solution needed to meet the following targets:
- Support business through legacy modernisation
- Respond more flexibly to changing requirements
- Engage in long-term collaboration with a nearshore team
- Apply a bespoke tech stack to optimise costs
Project Summary
We applied the Profinit Modernisation Framework to deliver a custom software development solution for our client as part of a wider long-term nearshore partnership.
- Bank’s ability to meet market challenges expedited significantly
- Nearshoring resulted in 65% savings compared to German providers
- Seamless transfer of knowledge and alignment with the client’s IT department
- Fine-tuned and ongoing coordination on SDLC to realise development goals
- Future agreements established on other long-term projects for the client
Migrating data to a new core systems
Profinit prepared the comprehensive plan including the minute-by-minute cutover plan for this first wave of the SOC project. We ensured its progress and the coordination of all 60 affected systems, we prepared and implemented data migrations from ten discarded or modified applications to seven newly running systems.
The data migrations included not only the physical movement of data from source systems to target systems but also the extensive transformation of data demanded by the differences in logic between the original and new environments, changes resulting from the significant simplification of the product portfolio, and the cancellation or introduction of dozens of tariffs, packages, and other services.
Such a large migration required approximately 10 months of preparation. The preparation of the cutover alone took 6 months. The main part of the cutover went on for 55 hours straight, for which the eight-member team from Profinit coordinated approximately 110 staff from 11 other service providers as well as O2 employees. 75,000 rows of code, 4.5 MB of source code, and configurations were written.
“It was the most extensive IT project ever in the history of our company, thanks to which we have simplified the architecture, saved the old systems and, above all, made it possible for our business colleagues to reduce TTM. Although Profinit didn’t provide any of the source or target systems, they successfully managed both the technical and organisational aspects of the data migration, which are always the riskiest parts of a project of this type.”
Michal Dvořák, CIO of O2 Czech Republic
Administration & development of a core system
Profinit’s main tasks were to take over the maintenance and further development of Cardif’s core system without any breakdowns, to merge the development and operations of the Czech, Slovak, and Austrian versions into one shared services center, and to further develop system features in step with the latest requirements of the insurance company’s branches.
The key phase where Profinit took control of the core system had to be carried out as quickly as possible to keep the time frame during which two different providers would be working on the system to a minimum. Profinit executed the first phase of the implementation in 5 weeks.
Shared services centre
Profinit transformed the development and administration of Cardif’s core system into a Prague-based shared services centre. The Czech, Slovak, and Austrian branches of Cardif now use a new version of the core system where each branch has its data and settings and, nevertheless, can have its own requirements for new functionalities and the implementation of new insurance products.
The main benefits of Profinit’s takeover of the administration of Cardif’s core system include:
- Speed at which Profinit took over the core system from the previous service provider.
- Stabilization and setup of administrative and other system development processes.
- Significant acceleration and improved quality of the implementation of new features and products.
- Decreased risks associated with the operations of the core system, verified by a BNP Paribas audit.
- Transparent cost model for branches using the shared services centre.
“The main benefits of cooperation with Profinit are apparent in the acceleration and improved quality of the development of our core system and the assurance of its reliable operation. The system migration project was done over a very short time period, and its results were very positively assessed by the BNP Paribas headquarters,” says Rudolf Šlesinger, Chief Operating Officer at Cardif.
Big data Hadoop platform
Project Brief
Banking security compliant big data platform for use case analyses
Business needs
The solution needed to meet the following specifications:
- Select and build a highly efficient big data processing platform
- Set up tools for solving business cases with data science
- Meet strict requirements on system security and data anonymisation
Project Summary
We implemented an end-to-end big data platform for new business use case analyses, and achieved these results for the bank:
- End-to-end delivery of a big data platform, including tools for data science
- Single sign-on and integration with Active Directory
- Fully compliant, bullet-proof platform security
- A powerful tool for self-service BI/DS analytical departments
Receivables monitoring system for ČSOB bank
Summary
Profinit delivered a strategic solution called REMOS for ČSOB bank. The project consisted of specifying and implementing a system, which enables bank clients to file and deposit invoices in various electronic formats and through diverse channels without the need to physically visit a branch.
Project background
ČSOB made the strategic decision to develop a new software solution called the Receivables Monitoring System—abbreviated as REMOS. Subsequently, Profinit secured the system specifications and implementation of the REMOS project.
Thanks to REMOS, clients (even new ones) can now immediately withdraw loan finances the day after handing in their receivables. The money can be accessed as a revolving line of credit or a current account. From the bank’s perspective, REMOS has fulfilled the defined requests and supports the entire life cycle of the enterprise finance service.
Profinit's role
Profinit acted as a contractor for the turnkey REMOS solution and handled everything from analysis, design, development, internal testing, and acceptance testing support all the way to production installation, pilot run support, and support during normal operations as defined in the SLA. The detailed system specifications take up more than 600 pages of text.
The development of the entire REMOS solution took 15 months and was done in several stages, including the bank’s initial assessment of our ability to actually produce a quality product. The total project volume was almost 2,000 man-days.
„I definitely view the REMOS solution as a successful project that fulfilled the defined expectations and, in many aspects, even exceeded them. A large portion of the credit goes to the Profinit team, who was professional, well-organized, and otherwise great to work with."
Martin Šindelář, Executive Manager IT DEVOPS at ČSOB
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Find more at: https://profinit.eu/wp-content/uploads/2020/01/Profinit_case_study_REMOS_04_2019_EN_online-2.pdf
Scoring of clients for consumer loans offer
Project Brief
Creating a prediction model aimed at identifying clients likely to take out consumer loan products and optimising campaign targeting.
Business needs
The solution needed to meet the following business targets:
- Improve the conversion rate of consumer-loan product offerings
- Generate accurate prediction scores for Equa’s entire client base
- Evaluate added business value for different client segments
Project Summary
We rolled out an advanced propensity-to-buy model to enhance consumer loan uptake for our client Equa bank, delivering the following results:
- Propensity scores computed for all bank clients
- High-accuracy prediction model for future loan applications
- Added business value for almost 70% of the client base as confirmed by Equa bank analysts
- Applicable even for new and inactive clients without previous loan history
Tech stack
- Profinit propensity-to-buy lending solution
- Hadoop
- Apache Spark
- Python
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For more information visit https://bigdataforbanking.com/success-stories
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Data-driven campaign targeting
Project Brief
We increased the conversion rate of consumer credit campaigns using customer behavioural modelling on a big data platform.
Business needs
The solution needed to meet the following specifications:
- Increase the conversion rate of large-scale consumer credit campaigns
- Leverage other channels and increase success rates
- Integrate the solution into CRM and campaign management system processes
- Adhere to strict outreach policy requirements
Project Summary
A campaign offering consumer credit targeted at customers via the advanced use of transactional data produced the following results:
- Within two months, the bank reached approximately 95,000 customers through various channels
- The model identified the most suitable 10% of the customers to be reached by the call centre
- By selecting the right customers, the call centre increased its conversion rate sixfold compared to matching done without using the model
- Across all channels, the data-driven solution was 50% more successful than the existing model
Tech stack
- Profinit AcceptAI (P2L Model)
- Hadoop
- Apache Spark
- Python
"Thanks to Profinit’s AcceptAI, we achieved a sixfold improvement in call centre conversion rates in a credit campaign targeted based on customer behaviour. The advanced propensity model running on the big data platform achieved a 50% better overall result than the existing model and improved success rates across all channels."
Milan Jirkovský
Head of CRM at Raiffeisenbank CZ
Find more at https://bigdataforbanking.com/success-stories/data-driven-campaign-targeting
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Central log monitoring solution
Project Brief
Implementing real-time data streaming to meet regulations and allow fast internal analysis.
Business needs
The solution needed to meet the following specifications:
- Fulfil requirements of new cyber security legislation
- Fast and stable data processing
- Able to analyse data including freshly logged events
- Scalable solution for future data volume increases
Project Summary
We developed and implemented a new system for fast, compliant data processing, and achieved these results for the bank:
- Česká spořitelna now has a central log monitoring system with uniform handling of real-time and batch data
- This solution processes tens of thousands of logged entries per second
- The system is compliant with new cyber security legislation
- It enables analyses of freshly generated data for the security department
For more information visit our website.
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Front-office System Modernisation
Project Brief
Modernise a 15-year-old front-office system to increase business value, optimise the user experience, and reduce error rates and maintenance costs.
Business Needs
The solution needed to meet the following targets:
- Deliver added business value
- Bridge the tech gap from legacy to modern
- Improve automation and overall quality
- Reduce technical debt
Project Summary
We successfully oversaw a complete front-office system modernisation based on greenfield development principles, delivering a new architecture and removing dependencies on existing legacy code.
- Delivery of a modernised front-office system
- Technical debt reduced significantly
- Rollout of new features and safer implementation
- Increased functionality and decreased error rate
- Scalable, well-documented and maintained
- New architecture enabling future integration with other systems
- The client reclaimed governance and know-how over the new system
For more information visit our website.
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Competitor loans consolidation
Project Brief
Applying advanced data analytics and machine learning to detect outside loan payments
Business needs
The solution needed to meet the following specifications:
- Identify clients with competitor loans for targeted marketing campaigns focused on consolidation
- More accurate assessment of customers’ credit risk scoring
- Adding data science tools to bank infrastructure and setting up a big data processing pipeline
- High-performance technology to promptly process clients’ transactions without delays
Project Summary
The solution we designed and implemented has achieved these results for the bank:
- The new solution can detect twice as many competitor loans as the former one.
- A new big data pipeline now processes billions of transactions daily.
- Daily leads for loan consolidation offers and better campaign targeting.
- Information about new loans elsewhere improves credit risk management of debtors.
For more information visit our website.
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Big data management platform
Participating in the development of Big Data Management Platform for Telekom Germany. Storing, integrating and analysing large volumes of data using state of art tools and technologies with the aim to replace selected traditional business intelligence systems.
Turnkey online supplementary pension savings
In 2015, a legislative amendment that introduced supplementary pension savings for minors played an important role, increasing market potential on a one-off basis. Česká spořitelna – penzijní společnost (ČSPS) wanted to take advantage of this opportunity, so in 2015 it launched a tender for a supplier for the Information System for Arranging Supplementary Pension Savings. In the spirit of modern trends, the new system was expected to be a completely electronic way of arranging supplementary pension savings. With this system, ČSPS hoped to jump far ahead of its competition.
Thanks to PROFINIT, these expectations have been met and clients no longer have to go to a branch. They can do the calculations, confirm their identity, and arrange a contract according to their wishes. Thus, a modern system has been created for this pension company, which can be further developed to meet changing market needs.
“Thanks to the complete digitalization of the process of arranging supplementary pension savings, our company has managed to get to the top of its field in online arrangements. PROFINIT is a reliable partner who has helped us greatly in achieving our vision and we believe that we can continue to rely on them in the future,” says Daniel Šarman, CIO at Penzijní společnosti České spořitelny, as he evaluates the results of the project.
The Main Benefits of the New Solution
- Increased sales
- Facilitated a timely response to the new market segment (clients under 18)
- Attracted more clients and external sellers
- Increased conversions from transformed
- Supplementary Pension Insurance
- Significantly decreased administrative and manual labor costs for the bank, thanks to reduced errors and full automation
- Accelerated the client scoring process
- Allowed them to jump ahead of the competition
As the supplier and system integrator, we ensured the complete development cycle of the system from the analysis, development, and project management to the deployment and subsequent system support.
High-speed platform for fraud detection
Project Brief
Implementing a high-speed big data platform for computing anti-fraud predictors.
Business needs
The solution needed to meet the following specifications:
- Have the ability to perform high-speed computations of predictors within a limited time window
- Allow in-house departments to design and adjust the computed predictors
- Easy integration with the surrounding banking systems
- Scalability for future extensions and customization
Project Summary
We implemented a high-speed big data platform for computing anti-fraud predictors, and achieved these results for the bank:
- Česká spořitelna has a new high-speed solution which fulfills the requirements of the limited-time window
- The solution is scalable – it can process larger data volumes and conduct faster computations in the future
- The bank’s in-house departments can customize the computations
- The big data platform integrates fully with all required banking systems
Offer acceptance increase through machine learning
Project background
The UK fintech company My Community Finance, the most prominent lender in the UK credit union sector, was looking for a strategic partner in machine learning and data analytics.
The first step to achieving the ultimate optimised underwriting process with a personalised offer was to build a behavioural model to predict the probability of acceptance of each client quote with the given parameters.
Profinit accepted a request for proposal (RFP) by My Community Finance in the form of a contest to get the best prediction results from an anonymised dataset. Our data science team successfully tackled the challenge and delivered the best model out of all the competing vendors within two weeks.
Challenge
The model needs to process hundreds of client features from the underwriting process and external risk to credit-bureau data.
Furthermore, the computational time is critical as each offer needs to be shown to the customer within a window of a few seconds when other competing offers are generated through web aggregator comparison services such as Experian.
Solution: Machine learning
Profinit designed and implemented the model for assessing each individual client quote. The behavioural model enhances the underwriting process by optimising offers for unsecured loan products using machine learning.
The end-to-end implementation consists of a real-time data processing pipeline running entirely on the AWS cloud and MLOps environment, enabling failover model retraining with a single click.
The solution provides stable, highly accurate predictions (85% AUC) and makes decisions in less than 100 milliseconds. The number of loan offers accepted increased by 30% as a result of using the solution for the individual offer for each customer.
DevOps: Automated deployment on the cloud
Project background & challenge
Our client, My Community Finance, the UK’s largest provider of consumer loans in the credit union sector, was transforming its cloud IT infrastructure to catch up with rapid business growth.
The new microservice architecture was chosen to better match very agile and flexible IT needs.
The current deployment setup and infrastructure components were set up for traditional incremental releases unsuitable for continuous deployment. The Profinit DevOps team enhanced the existing infrastructure and recreate a new deployment pipeline.
DevOps Solution
The first action was to upgrade major infrastructure pieces to the up-to-date versions.
Profinit’s primary approach was to create an infrastructure-as-code (IaC) solution automating the whole pipeline as much as possible using Jenkins and Amazon Elastic Kubernetes Service. Documentation on how to use and operate the system was created as a matter of course as well as guidelines for new service developers.
The pipeline now enables the deployment of new microservices with one click.
Reviews
the project
Centralized Log Management System Dev for IT Security Co
"We were satisfied with the delivery, which met our requirements for a favorable price solution."
the reviewer
the review
The client submitted this review online.
Please describe your company and your position there.
I'm working as a Security Product owner (Security Monitoring department) in the largest Czech bank.
For what projects/services did your company hire Profinit, and what were your goals?
In order to comply with banking regulations, it was necessary to build one unified repository for various audit logs. Unify the logs into a unified structure. Logs of int/ext applications, infrastructure, network.. Ensuring the storage of the original message, message hash, immutability, anti-readability.
How did you select this vendor and what were the deciding factors?
Six-month tender process was held between several suppliers. The price was crucial.
Describe the scope of work in detail, including the project steps, key deliverables, and technologies used.
- 3 month analysis of our requirements and data with architects.
- 2 months POC - WMWare test environment
- Purchase of physical HW
- Integration with 90 source systems and tests (data loss, recovery, secondary tape backup and recovery, AVAST penetration tests).
- Migration of older logs from the original RDBMS solution
- Commissioning and subsequent prophylaxis after 6 months of operation
How many people from the vendor's team worked with you, and what were their positions?
- 4 developers
- 3 architects (oponent)
- 1 external architect consultant from ORACLE
Can you share any measurable outcomes of the project or general feedback about the deliverables?
We were satisfied with the delivery, which met our requirements for a favorable price solution. There are currently 2 separate Cloudera Hadoop solutions in HA mode. 48 physical servers
Describe their project management style, including communication tools and timeliness.
Both the analysis and the subsequent implementation were conducted properly and in a friendly spirit.
What did you find most impressive or unique about this company?
Quick response to specific bank entry - designing solutions and internal POC for demonstration and cost estimation in a short time.
Are there any areas for improvement or something they could have done differently?
Nothing:-)
the project
Hadoop Consulting & Development for Banking Institution
"I was impressed by their culture of self-managed teams."
the reviewer
the review
The client submitted this review online.
Please describe your company and your position there.
Ceska sporitelna, largest bank in Czech republic, 4,5 mio clients. My position is Tribe lead responsible for data management and analytics.
For what projects/services did your company hire Profinit?
Antifraud of payment transactions operating in real time ... so called OLIN ... on-line monitoring ... solution is prepared based on big data technologies (Cloudera Hadoop). Profinit delivered consulting, expert and development services on Hadoop technologies ... Spark, Hive, Python.
How did you select this vendor and what were the deciding factors?
We decided for Profinit due to demostrated experties in Hadoop technologies and positive experience with Profinit services in another projects within Ceska sporitelna (e.g. application development). Profinit was able to propose team of experts (5 people) with expected background and experties.
Describe the project in detail and walk through the stages of the project.
Aim of project was prepare data integration for source of payment transactions, collect all relevant data in Hadoop based data mart and building analytical model up on those data to identify fraud or suspicious transactions. In next phase were those models integrated into on-line transaction monitoring front-end application. Profinit helped us to set up overall governance and analytical model lifecycle management.
How many resources from the vendor's team worked with you, and what were their positions?
5 FTE (architect, big data expert)
Can you share any outcomes from the project that demonstrate progress or success?
Thanks to projects deliveries we realized significant decrease of fraud transactions and improve clients NPS ratio.
How effective was the workflow between your team and theirs?
Absolutely effective. Profinit dispose with culture of self managed teams led by senior consultants or architects.
What did you find most impressive or unique about this company?
I was impressed by their culture of self-managed teams. Optimal mix of senior and junior people. High level of effectivity (we realized cost savings in projects in comparison to original estimation).
Are there any areas for improvement or something they could have done differently?
No idea in this moment
the project
Custom Software Development for Private Bank
"They have good response times, delivering bug fixes within a day."
the reviewer
the review
A Clutch analyst personally interviewed this client over the phone. Below is an edited transcript.
Introduce your business and what you do there.
I'm an IT manager at a private bank. We're one of the largest private banks in Germany.
What challenge were you trying to address with Profinit?
When I started with the company in 2017, I was asked to set up software development. Usually, we use standardized software solutions. However, our head of IT wanted to develop customized software solutions in special cases to assist our back office because sometimes the solutions we needed weren't available in the market.
We didn't want to hire software developers, so the idea was to look for a partner who could do the development for us. The work had to be carried out in our Java environment.
What was the scope of their involvement?
Profinit provides us with a team of developers who do custom software development for us. They work in a Java environment. Initially, we had them do a pilot project; we gave them requirements to deliver a software solution.
Currently, they help us with projects when there's a requirement on our business side. First, we give Profinit basic information. Then, we have a kickoff workshop for each project, which usually includes them coming to Hamburg for 1–2 days. Our business and IT departments participate in the workshops, and the Profinit team asks for detailed requirements.
From there, they create a solution design and provide a specifications document with cost and effort estimations. If the project is approved, they move on to development. So far, we've done a couple of projects together.
What is the team composition?
We have a fixed team of 3–4 developers. Depending on our demands and requirements, extending the team to up to 10 people is possible. The minimum team size is 2–3 people from Profinit. There's always a senior developer heading the team who is our main point of contact.
How did you come to work with Profinit?
I checked the market and got in contact with many providers. Profinit was looking for clients in the German market, and they were already in contact with our head of IT, who put them forward as a potential contact. We had very good conversations, so we invited them to come to Hamburg to have a kickoff workshop. We were happy with the results, so we decided to work with them.
How much have you invested with them?
We've spent between €800,000–€1 million (approximately $815,000–$1.1 million USD).
What is the status of this engagement?
We started working together in January 2018, and the engagement is ongoing.
What evidence can you share that demonstrates the impact of the engagement?
Profinit delivers high-quality work. We're really satisfied with the software they've developed so far; it's state-of-the-art, scalable, and very stable. Moreover, they have good response times, delivering bug fixes within a day. Our business side has also given good feedback. We plan to continue our cooperation with Profinit.
How did Profinit perform from a project management standpoint?
The team delivers within time and budget. We usually do weekly status meetings. Additionally, we appreciate that we have a fixed team because we don't have to explain everything repeatedly. Typically, one person from the business department is involved, and someone from our IT department coordinates. However, we've established such a good relationship with Profinit that sometimes our IT department doesn't need to get involved, and our business department talks directly with the Profinit team.
What did you find most impressive about them?
We appreciate that Profinit aims for a long-term partnership with us because this is also our objective. We needed a provider we could rely on and trust, and we've established that relationship with the team over the years. Moreover, they deliver what they promise. For example, they make very realistic effort estimations because they consider that there will be many change requests during the project.
Are there any areas they could improve?
I don't think there's anything they need to change; we're very satisfied. At first, we had a brilliant main contact, but it was clear that Profinit couldn't guarantee that they would be working for us exclusively after the starting phase. There was a reallocation, and we got a new contact, but our business department wasn't satisfied because this person didn't have the same competence level and didn't have the best English. I talked to Profinit and explained the issue. The team understood and introduced a new contact within 3–4 weeks. Since then, the situation has improved, so we're satisfied again.
Profinit's deliverables successfully met the requirements and budget and satisfied the client — they rendered two different Cloudera Hadoop Solutions and 48 physical servers. The team perfectly handled the project, especially analysis and implementation. Overall, they were responsive and friendly.