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AI driven Products

On this page we link AI driven products which we think are pretty awesome.

Zeta AlphaThe AI driven platfomr allows AI temas to find research documents by meaning, stay up-to-date on essential information, and organize AI related knowledge discovery work. “AI researchers drowning in information overload? AI to the rescue!”

Zeta Alpha wants to make AI R&D teams more productive, support decision-makers, and keep busy knowledge workers up to date with relevant information, insights, and connections in their field of expertise.

About us

Goldblum was founded 2019 by Ansgar Bittermann in Berlin Germany.

Ansgar is a studied data scientist and brain researcher and has been building data science related start ups and leading companies since 2007.

He experienced how difficult it is for companies to introduce AI. But he also saw that there was little help in the consulting world addressing this issue. Many companies had just a data science background, but noone could offer him “the big picture solution”. Thus he started Goldblum Consulting to help other business leaders to access the world of artificial intelligence with more ease and success and sophisticated help.

AI courses

We are offering three in-house courses for our clients:

Leadership Intense AI (3 hours)

This course is for all executives and leaders who have a busy schedule and want to learn about all important things in AI, so that they are able to follow or lead discussions about this topic. This course is mostly a private 1:1 course where the client learns the necessary skills to be informed.

Course includes:

Introduction into AI, Introduction into Machine Learning and its most famous models, NLP, RPA, Inferential Statistics, AI Project Management, Project Ideas for AI

Leadership AI (10 hours)

This course targets mostly “Head ofs” and expands the above course. It covers all above topics, but dives into more details.

AI Project Manager (40 hours)

This course targets project managers with experience in PMI/PRINCE2 and/or SCRUM, who want to expand their knowledge towards AI project management. In the 40 hours an exemplary project (or a real live project within the client’s company) will be used to learn the specific skills of an AI project manager.

Leadership education

It is a conundrum many leaders face. They have to decide on budgets and artificial intelligence, but they have the feeling that they do not know enough to make a sound decision. And it is not appropriate to ask. It is expected that “they know”.

Do you feel the same?

Goldblum helps discreetly

We are specialized to empower leaders and c-level executives in the realm of artificial intelligence. Our CEO Ansgar Bittermann personally helps leaders to learn more about artificial intelligence. The curriculum is always personalized towards the client as everyone has a different background and faces different challenges in his or her company.

  • Exemplary topics of the leadership education are:
  • Defining AI
  • Foundation of Machine Learning, NLP, RPA (introduction into models, algorithms, examples)
  • Inferential Statistics
  • AI project management (including process optimization and introduction into Lean Six Sigma)
  • AI Readiness (including operational, transformational and foundational readiness)
  • How to create value with AI within the business, for your customers and/or employees
  • What are trends in AI? Which AI problem areas are typical for my industry?

The leadership education is conducted via phone or Zoom during Covid.

AI Project Management

We have deep knowledge in AI project management. Many people think that they can just use SCRUM or Prince2 or PMP or nothing for AI projects, but a rising fail rate of AI projects (recently up to 70%) shows, that AI is special.

We have first hand knowledge of running AI projects for many years and we are using a mix of waterfall and agile project frameworks plus process and process optimization methods within the realm of Lean Six Sigma. Our project managers all have black belts in Lean Six Sigma.

A successful A.I.project, run with Lean Six Sigma, is conducted in five steps. Each phase will end with a distinct set of documents which will be handed down to the next phase. Actual coding or what most people consider “the actual A.I. project” just happens in the fourth phase.

The five steps are:

  • 1. Define
  • 2. Measure
  • 3. Analyse
  • 4. Improve
  • 5. Control

Skipping Step one, two and three will mostly lead to costly re-work and extended project periods. Successful discussions with technical data science outsourcing partners can only start once phase one, two and three have been finished within your company.

We will help you set up, plan and execute your AI projects. We are your one-stop solution for AI projects.

Besides running the project, we also help with vendor selection, technical set up, recruiting, staffing and assessment.

We would love to help you succeed in the world of AI.

AI Readiness, Digital Transformation and Enterprise Architecture

We help you become AI ready

Everyone talks about A.I., but when you want to do it, it seems harder than you might think. Over time many people realized that a company has to fulfill certain criteria to be able to do A.I. projects. Thus the term A.I. readiness was coined. Capgemini defines A.I. Readiness as

“Extent to which a country and its institutions & businesses have the ability to reap the benefits of A.I.”

As we learned before, the benefits of A.I. are defined as improving the Voice of the Customer (Product, Services, Customer Satisfaction), the Voice of the Business (Strategy, lower cost, higher revenue, increased stock prices or profits) or the Voice of the Employee (Employee satisfaction). That means that A.I. Readiness  is the

“Extent to which a business has the ability to improve the VOC, VOE or VOB  by using A.I.”

According to Intel, AI readiness is being achieved on three dimensions.

  • Transformational
  • Operational
  • Foundational

It is important to dissect A.I. readiness to these independent dimensions, because many companies just focus on one dimension and wonder later why their A.I. initiatives are not working.

Goldblum Consulting is supporting you on ALL THREE DIMENSIONS.

Transformational readiness includes strategic leadership, educated leadership, a clarity of potential business cases and the acceptance of your organization to evolve. Even the most spirited leader will fail, if the employees themselves boycott change. Thus a big part of A.I. readiness is change management. The further the business is originally away from A.I., the more attention we have to put into change management.

Operational readiness includes all levels of management. On a governing level it covers governance, compliance, risk but also cyber security. On an operational level it builds up human resource readiness and needs to address the issue of assessing existing skills and expertise and how to fill the existing gaps. Furthermore it includes project management readiness. This includes the adaption of existing management frameworks to yield to specific software development & A.I. issues. Additionally it includes a specific focus on data management, data flow and data resource management.

Foundational readiness is the groundwork for A.I., but should only be tackled as an output of the two above dimensions. It consists of the infrastructure platform, cloud resources, data sources and software packages. Questions in this dimension might be:

  • Is my existing data center set up for increased network bandwidth needed by A.I.?
  • Do I know enough about the offerings cloud providers have? Do I know exactly how expensive cloud services would be? Do I understand the extend of services e.g. AWS offers and how much training my employees need to work with it?
  • Is critical data available? Do you have it labelled correctly? Is it up to date? Can you retrieve it in real time or does your database has to calculate a SQL request for 20 minutes?
  • Do you have people in your organization who have worked with AI software packages? Do they know which software is out there? Have they worked with e.g. TensorFlow and know how to connect it to your existing software landscape?

As many parts of AI readiness are part of works within digital transformation or enterprise architecture, these areas always overlap. Especially as enterprise architecture 4.0 develops more into the business realm.

Talk to us, if we can help you into becoming AI ready or you have problems regarding digital transformation or enterprise architecture.

Service: Recruitment & Assessment


There are several roles for AI teams. We have described them in an article “Roles in AI”.

  • Project Manager specialized in AI (most important)
  • Data Analyst
  • Data Engineer
  • Applied Machine Learning Engineer
  • Data Scientist

We provide any of these roles for a single project or help you recruit them. Our CEO and other senior partners mostly fill the role of the Project Manager AI as seniority and experience in all project and process management framework is essential for a successful project. You can get more background information on AI project management in these articles.


Normal pre-screening has only a validity of 18 % and personal interviews alone range from 5-25 %, while normal structured interviews allow only validity of 30-40%. That means that in best case scenario, the decision whom to hire will be probably wrong in 60 – 70%.

In order to overcome this roadblock, psychology departments around the world have researched supporting measures to increase this number.

As this article is focusing on small and medium companies, I am limiting myself to solutions which can actually be used by small and medium companies for a reasonable price.

Thus our recruitment includes specific scientific assessments. You can also book the assessment without recruitment. We also just support your HR department with the assessment.

The solutions which should be added to your hiring process are part of the psychological diagnostics psychometrical assessments. Here, important variables of the human mind, important for fitting into a company, are researched and metrized. Metrizing means making non-countable things countable. For example the ability to be open for change or the ability to bear a lot of stress. These important variables of the human mind are being tested with scientifically validated tests. These tests differ from normal tests you do at Facebook (“which city am I”, “which character of Sex and the city am I”). Years of research are needed to create these tests, to make sure that they measure what they actually pretend to measure.

Out of these psychometrical assessments, three classes of tests can support your hiring process significantly with a combined validity of up to 80%!

Three classes of tests which let you science hack the hiring process

  • Intelligence
  • Personality
  • Conscientiousness

Research showed that testing the intelligence of your applicants in combination with personality tests and motivational tests help to determine the potential fit of your employee significantly. As research showed it can increase the validity of your hiring process to up to 80%. That is quite a jump from 5%-25% validity by just using personal interviews.

Testing Procedure

All testing is being done digitally:

  • Personality, Conscientiousness and Motivation is tested via digital questionnaires.
  • Intelligence is tested via Proxy testing. This means that a psychologist is observing the candidate via screen sharing and camera while, the candidate is doing the test.


All results are first orally presented to the company by a studied psychologist and then to the candidate. Furthermore a visually appealing and easy-to-understand written report is provided. Examples are shown below.

You will see clearly, how the candidate personality profile will differentiate from the desired profile. This can be a generic profile depending on the job or the average profile of your existing team.

In over 22 different sub-scales you will see, how the candidate’s intelligence is fitting the demands of the role.

The average for each sub-scale is 100 and the dark-blue is showing clearly the margin of error. The red line show’s the candidates result in comparison to a huge test sample.

AI Project Management – Control Phase

The purpose of the control phase is to make sure that the problem does not return.

For that you have to solve technical and non-technical problems.

Let’s start with the non-technical problems:

  1. Train your employees.

It is of utmost importance that you train all relevant employees. Train the people who are going to use the A.I. solution and train those who are going to take care of the A.I. solution. Make sure before your outsourcing partner leaves the house that the employees will be trained. Trust me. The real questions arise when your product is released on the employees or customers. Plan this additional time and budget ahead. If you start training your employees while your outsourcing partner is maybe already on another project, you will never get as good and fast feedback and support as if you had it slotted it in in the overall project plan

  • Document everything

Actually also a no-brainer, but mostly overlooked. You will need documentation for people being trained (so that also new employees in 6 weeks will be able to be trained) and also the complete A.I. solution needs to be documented. It is a complex thing which you are building. You do not want to find out how badly your solution was documented only in the moment where it stops working.

  • Develop monitoring plan

Just imagine you implemented an A.I. solution in your system and it runs well in the beginning. But as time goes by, the data/ customers/ products / inputs you are using now do not fit the algorithm any more. The result will be that your formerly artificial intelligence product will not make intelligent decisions anymore. So discuss with your outsourcing partner in advance to set up a monitoring system. Let them come up with key performance indicators for your A.I. solution. You should have a dashboard where you see how reliable your new algorithm is working. Once you have worked out a set of KPIs and also found a way to display the values in your monitoring dashboard, have them help you set it up and train an in-house employee on actually monitoring it. This again sounds trivial, but many of us have seen monitoring systems just not being used in companies as nobody felt responsible looking at it. Furthermore discuss with your outsourcing partner and with in-house employees, what exactly will happen, once your monitoring system will sound alarm. Who will be able to retrain your algorithms? I suggest to have a service contract with your outsourcing partner which will regulate all these questions. But also come up with a plan, if your outsourcing partner should not be able to do it. Either they just might not have the capacities to help you at that time or they might have gone out of business in the future. As a good business person, you plan ahead.

  • Develop implementation plan

Many people forget that their precious prototype also needs to be implemented in their systems and processes. If your outsourcing partner develops the prototype in Google’e AI environment, but you are using AWS? What would you do? Talk about implementation also at the beginning of the project to allocate enough time and ressources for these questions. Furthermore make sure that the data-flow is properly aggregated, so that the new algorithms are set up automatically with the right formatted data.

As you see, there is a lot to think of. Please make sure that all future collaborations with your outsourcing partner are being written down in a service level agreement so that everyone knows, what to do.

Overall over the course of the last two weeks, we discussed how process optimization can bridge the gap between project management, agile development and A.I.. And I hope that these short texts could give you a primary idea, how to handle your first A.I. projects.

AI project management – Improve Phase & AI Scoping Workshop

AI project management –  Improve Phase

This is the phase where you all have waited for. You did your homework, have a clear understanding of your company and are ready to talk to outside outsourcing partners. By now you have a pretty good understanding why the previous steps were important and why you needed to invest time first to start strong with your outsourcing partner.

Normally the first real meeting is an A.I. scoping workshop. This is mostly really fun, because many people come into contact with A.I. teams for the first time. The scoping workshop has many interactive elements and involves all important stakeholders from your side. The outsourcing partner will be very happy that you did such a wonderful job in getting your story straight beforehand, because now you all can dive deep into the actual topic: How can A.I. help you solve your problem.

In the following passage we continue to talk about the A.I. scoping workshop.

A.I. Scoping Workshop

After we defined artificial intelligence, we will turn towards our A.I. scoping workshop.

Imagine you and your team are sitting together with bright data scientists discussion how you can use A.I. to solve your previously defined problem.

The first step in this scoping workshop is actually to exclude all aspects which are not A.I. solutions. Many people find that counterintuitive, but reality shows that many problems can actually be solved by not using A.I. first.

Let me dive into this a little bit more. Let’s consider the topic of our two baristas, Betty and Michele, which we talked about in one of the previous articles. Betty is much better in working the machine than Michele. We spotted that in our analysis phase and can now try to solve it with A.I. (write algorithm to implement it in the machine to counteract “bad” handling) or we could just have Betty train Michele. If Michele is untrainable, we will offer a job as a cashier or let Michele unfortunately go. As a rule of thumb: if you can solve it without A.I., solve it without A.I.. Reduce complexity in problem solving and try the easiest way possible. I need to stress this, because people in general will suggest solutions close to their own profession. If you ask an engineer, he will give a solution using engineering. If you ask a data scientist, she will give a solution using data science. That is one of a reasons, why we at Goldblum Consulting tend to support our clients in these scoping workshops to find the best solution. And by best I mean best for your company and yourself.

There are several levels in problem solving, we will look at during the A.I. scoping workshop:

  • Solving problem by changing business processes (e.g. change supplier or training staff)
  • Using robotic desktop automation
  • Using robotic process automation
  • Using Business Intelligence (descriptive statistics) – visualizing data
  • Using Business Intelligence (inferential statistics) – infering and predicting
  • Using Artificial Intelligence

We will go in each of the above topics in more detail over the next weeks, but for now we will just name them, so that you have an understanding what “non-A.I. solutions” could be.

Carving out true A.I. based solutions

Good outsourcing partners will be supportive in separating the non-A.I. from the the A.I. solutions. Follow your guts in these workshops. If they start feeling like a sales pitch, just leave. But if you feel understood and supported, stay 

Once you found true A.I .applications, your outsourcing partner will be able to play out his A.I. muscles. First of all, you will determine to what cluster of A.I. problems, your problem belongs. Will it be for example a sensing problem like in image recognition or a prediction problem in finding for example potential fraud cases. Or is it a forecasting problem or a case of processing of unstructured data? Or is it NLP (natural language processing) where you try to process emails, SMS or PDFs?

Each cluster of A.I. problems has its standard set of algorithms and specialists. By clustering your problem first, your outsourcing partner will be able to assign cluster specialists to your problem and might be able to use standard tools and algorithms to tackle your problem. In 2021 we are far enough in A.I. that the wheel does not have to be invented with every project you are doing.

At the end of the workshop, you will have a good idea, how A.I. will be able to help you and will have created a rough timeline for a lean prototype to be developed. Furthermore your A.I. partner should have a good understanding where your A.I. solution will “live” in your company and who should use it. They should have a very good understanding, how your employees will need to work with the A.I. solution and in what way it will be implemented in your company.

In the next part we will look at the last phase – the control phase. This phase will insure that your amazing A.I. solutions will continue to live in your company and not just collect dust in the virtual software shelf.

AI project management – Analyse Phase

In the previous chapter we reduced the potential causes for your problem in the measure phase. Until now, your main tool of problem solving was the brainpower of your team. Many people oversee the risk that until now there is no real “proof” that your ideas and suggestions are actually true. You might have experienced something called “group think” in your meetings. At a certain point you might find that not the best idea wins, but the one which has the most supporters – be it out of political reasons or just because the boss likes it or some employees are more vocal and aggressive in their opinions than others.

But instead of trying to change human nature, we will try to circumvent the downsides of brainstorming by applying mathematical methods to prove or disprove the impact of your “few vital X’s”. In order to do so, we are converting our practical theories of our causes/ Xs into scientific hypotheses and then use mathematical analysis tools to prove or disprove our hypotheses. As a side note: in pure scientific theory hypotheses of course cannot be fully proven, but for our sake of argument, we will stick to this easier notation.

Our next step is to collect enough data for each X. This is a very critical moment for a company. Did you actually collect enough historic data? Where is this data stored? In what format are you having the data stored? Did you invest enough time and money over the last years to have a solid data base to work with. Nothing is more frustrating than realizing that you have to drop certain Xs or postpone your projects for months to start collecting data because right now you have no database to work with. Or that you logged the wrong data for the last 6 months. I strongly advise that you have a very good understanding for yourself about the existing data before you talk to your outsourcing partner. Data is the fuel your whole project will run on. And their engine will only run as good as the fuel you provide.

Regarding the data you should be able to answer following questions: Is my data accessible? Is it accurate? Do I have the right employees in my team who could support the outsourcing partner to collect and maybe change data? Just imagine your outsourcing partner would need just a subset of a table. Do you have employees on stand-by who can work with your existing databases or write SQL-statements. And if not, are you feeling comfortable enough to give an outsourced partner full access to your servers to “get the data for themselves”?

In the following passage we will briefly discuss how statistical testing could look like. I stress the word briefly, because these texts are for the general public to understand. This means we will not go into mathematical depths

Mathematical hypothesis testing

Today we will look at mathematical hypothesis testing. The purpose of appropriate hypothesis testing is to integrate the Voice of the Process with the Voice of the Business to make data-based decisions to resolve problems.

Hypothesis testing always analyzes two alternatives and our mathematical analysis will determine which answer is correct. Imagine your overall problem is the low quality of customer satisfaction with your coffee in your café. One of your Xs you assume is that different coffee bean suppliers cause statistical differences in the taste of your coffee.

Then your first hypothesis H0 would say:

H0: There is no difference in taste depending which coffee bean supplier you choose.

The alternative hypothesis H1 would however say following:

H1: There is a difference in taste depending which coffee bean supplier you choose.

In order to run a mathematical analysis, you need to have the coffee being tasted for a certain period of time  and being rated by your customer (for optimal results on a 5-7 point scale). At the same time you need to write down which coffee bean was used when a customer rates the coffee.

The mathematical analysis will help you to answer following questions: how many samples do I have to take? Which variables out of the collected data will I use to make my judgement? How much different do my results have to be to disprove H0 or H1?

Just imagine, supplier A beans show an average customer satisfaction of 4.3, supplier B beans yield an average customer satisfaction of 4.5 and supplier C beans produce an average customer satisfaction of 4.7.

Would you be able to say with certainty and without mathematical help that supplier A is significantly better? Is a difference of 0.2 enough to call it already “better”? And what does average actually mean? In a worst case scenario half of your customers loved it (7) and half of it hated it (rating it 1.6). As you see, you will also look at the so called variance of the data collected.

To answer all these questions, we would use inferential statistics. Inferential statistics is the cool brother of descriptive statistics which you are using in Excel. Bar or pie charts would be examples of descriptive statistics and all they do is describing data.

Inferential statistics is, as Wikipedia puts it, “the process of using data analysis to infer properties of an underlying distribution […] Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates.

In the above example we assumed that there was a linear connection between supplier and coffee taste. But in reality there would be a group of Xs interacting with each other to create the taste of the coffee. Imagine your barista Betty is amazing in working the machine while Michele is not as good. And your machine is also producing probably better results after having been cleaned in the morning. So in this example you have three interacting variables which influence the taste of your coffee. These interacting effects can also be modeled with inferential statistics.

If your models get to complicated and no one in your company is able to run these analyses, try to get help early on to get this right. Because identifying the potential vital few root causes of your problem will later on boost the results of your outsourcing partner.

As the old saying goes: “Good data in, good data out”.