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We are pleased to share with you another investment opportunity.

When you think about AI, you have a version of this one story in mind: some large enterprise puts a bunch of people in white lab coats in a huge room, guarded by access cards, attack dogs, as scary as possible. This AI was custom built with a singular purpose. To master something big in a world. Although this AI exists and has silly names like Watson and gets a lot of publicity, that is where money is right now. 

In business, AI is now in high demand in enterprise software automation, in adding new possibilities on top of existing decades of technologies. Within those enterprises 87% of data projects never make it to production. Why? Because their companies were not built for that. They were built for the opposite. 

Syntactic.AI developed 50 % faster model deployments enabling Reckitt Benckiser to predict Sales, 30 % lower loan default rate for Loanzen and improved operational efficiency by 70 % for Denali. 

Synctactic.AI’s end-to-end platform analyses and extracts insights from data, cutting on time and costs of hiring data science professionals. The platform can be deployed on-premise, on-cloud, or in hybrid multi-cloud, regardless of the customer’s industry. The company claims that its platform democratises the use of data science, machine learning, and AI. With Synctactic.AI, customers can increase operational efficiency by 30% and improve decision-making and customer engagement by 50%.


The team is looking for 200.000 EUR from angel investors in total 1 million round for a runway of 12 months and further growth.


We will be allocating slots as follows:

  1. Business Angels of Slovenia members
  2. BAS partner

Further information:

  1. The minimum investment is EUR 20,000
  2. We might have to cut back if we are oversubscribed
  3. Accepting allocations until the 22nd of April 2022 (if not oversubscribed earlier)

You can find detailed information here.


If you are interested, please email or mark your interest directly here 



About Syntactic.AI 

Synctactic.AI is an Indian software development enterprise, providing clients with a platform to connect various data sources, define workflows, visualise insights, and build ML/AI models. It offers a Data-as-a-Service (DaaS) solution where teams can collaborate using a single source of truth, optimise their workflows, and mobilise their data or store it anywhere from on-premise deployments to any of the cloud providers. The company set up US headquarters recently. They are a Delaware Corp ” SynctacticAI, Inc” and currently employs a team of 11 members. 


DECK: Here 

ROUND DETAILS: Raising 200.000 EUR angel round, 5.000.000 pre-money valuation (4 % equity share)

Information rights: Yes

Soft – committed in this round – USD 270.000

Previous funds raised for this project: In the first round the company raised $130,000 from friends in order to get the beta out of the market


In the current round they have already raised app. $270,000 out of the $650,000 – $870,000 they are seeking

In the future (1 year from now) they are also planning to raise additional capital in the size between $4,500,000 and $6,800,000.


In today’s world there are countless pieces of information, data, numbers etc. Very rarely, we are able to make sense out of those numbers and calculate real predictions. Usually, data is not connected in a right way making companies less effective and difficult to reach targets, initiatives and projects are not finished.

Value Proposition

Synctactic.AI’s end-to-end platform analyses and extracts insights from data, cutting on time and costs of hiring data science professionals. The platform can be deployed on-premise, on-cloud, or in hybrid multi-cloud, regardless of the customer’s industry. The company claims that its platform democratises the use of data science, machine learning, and AI. With Synctactic.AI customers can increase operational efficiency by 30% and improve decision-making and customer engagement by 50%.

Product Portfolio

The data science platform enables clients to turn data sets into actionable insights. By leveraging advanced data science tools, the platform communicates to various storage systems and databases, using algorithms to clean the data sets and extracting knowledge from any structured or unstructured data collection. The platform includes four key features: (1) Sync Discover, (2) Sync Data, (3) Sync Learn, and (4) Sync Analyse.


Synctactic.AI is a data science platform that helps businesses turn their data sets into actionable insights. Platform connects to various storage systems and databases, automates and simplifies the Machine Learning pipelines from data collection, data prep, scalable training to model serving, allowing faster development of intelligent application

Their system offers much quicker and more reliable data analysis, and the model can be customised and suited to each specific user.


More about how solution works:

  1. Sync Discover – This feature enables users to import their data sets in any form. Once imported, algorithms analyse the sets to understand their structure and identify specific relations. Additionally, it provides the users with results in sub-second response times, giving precise information on their data infrastructure. 

The platform can also specify the data type, scheme, and relationships with other data sources. For users to broaden the understanding of their data sources, it provides an interactive graph showing how they are mapped to each other. 

The platform also extracts metadata from the sources, enabling statistical insights on data sources. 


  1. Sync Data: The Sync Data feature refers to operations regarding processing data at scale. Data can be processed in batch or in real time thanks to webhook functionalities that allow pushing data to the platform in real time and at high volume. The operations on data can be executed using predefined operators or by using custom scripts.

The platform supports a range of connectors from NoSQL to Relational databases, Flat Files to object stores, and EDWs such as redshift, big query, and snowflake. It includes pipes that can configure data destinations, connect multiple data sources into a complex workflow, perform operations using operator libraries, and push various data sources into any data destination. Moreover, the drag and drop interface enables customising business schemes easily, combining any column across the datasets with any column from the data source without running multiple ETL jobs and writing complex SQL queries.


  1. Sync Learn leverages machine learning to help clients increase revenue and reduce costs by providing a data-driven decision-making tool. Once the target variable, features, and input parameters are selected, it trains the models. The models can be further deployed into production-ready environments. 

The features of the machine learning models can be defined using pipes, emphasised through feature selection algorithms, and generated using operators and data enrichment. Models are chosen from multiple libraries such as SKlearn, SparkMLibl, and H2O, with the possibility to configure and optimise their hyperparameters to get the best-fit model performance. The performances can be visualised through measures like f-score, AUC/ROC curves, confusion matrix, and precision-recall. AuroML can be used to feed data sets to select a target variable or goal.


  1. Sync Analyse Due to the built-in charting library, any data point on the platform can be visualised by choosing a data source with a single click. It automatically formats all the data points to select the chart type, drag and drop the parameters, and visualise them. The platform helps customers understand the various data types and generate measures, dimensions, geo points, and time series aggregations. All data sources and sets can be visualised using pre-built charts available on the platform.

The platform also enables Reports, which lets multiple visualisations into a single view that can be published for teams to stay on top of crucial metrics. It provides insights on each column’s metadata, including type, distribution, minimum and maximum value, etc.


Synctactic.AI is a platform, and they don’t have any Hardware based products/solutions.


Assisting high growth startups and SMB’s to set up out-of-the-box data infrastructure ready by deriving faster GTM for analytics and machine learning and enabling enterprises to build and deploy AI models at scale using MLOP’s framework.

AI is scalable and serves for better risk, demand, and future projection predictions in a much more effective and quicker way.



Their customers are businesses who want to leverage their data to derive accurate business predictions like demand forecasting, customer LTV, churn, risk and segmentation across retail, internet, and DTC domain.

The size of the market is 83,000 million euros; this estimation is based on the market factors in terms of need and relative valuation that is achieved by comparing few others in the market offering similar solutions.


About Synctactic.AI Team

Core of the team consists of 3 founders:

Chethan K R (CEO) is 50% stake holder, experienced in IT, Business development, and Sales. He already had a successful exit with his previous startup venture (DigiFutura) where he met the current co-founder and CTO of Synctactic.AIO.

Ashish Koushik is a CTO with IT knowledge, responsible for Platform Architecture and Development. He owns 25% of the business.

Shaleen M R is Chief growth officer with Branding, Marketing, and Sales experience and a bi proponent of AI and Machine learning systems.

Company has 2 advisors (Amin Karr and Brian McMahon) with experience in IT, investing, startups and fortune 500 companies.

Currently, the company employs 11 full-time employees.


Business Model

Synctactic.AI operates on a B2B model, offering solutions for the IoT, FinTech, and retail industry, among others. The company’s revenue model is subscription-based. The subscription plans are designed according to the customers’ required computing and storage; each plan can be upgraded when the cap of computing and storage is exceeded. The company claims that 50% of its customer base are US clients, 25% are based in Singapore, and the remaining 25% spread across India and the rest of the world. The company has 3 large marquee customers like Reckitt Benkiser, Pfizer, GrantThronton and other major clients like Shriram Finance, Chef Social, Local Ferment Co, WuupTo, and Cube Monk in their portfolio.  

The company firstly charges their customer the initial onboarding fee, which varies depending on the size of the company, how they are acquiring their data and the complexity of the data. The onboarding fee varies in the range of 10,000 euros and up to 30,000 euros.

After the initial onboarding there are 2 different tiers offered to the companies depending on their size and needed services. These cost 2500 EUR for the smaller size and 3500 for the bigger one.



  • Revenue is already being generated 
  • Between the September of 2020 to April 2021, the company generated $110,000 revenue.
  • Currently the company has $400,000 in their sales pipeline and is growing
  • The company also currently has a monthly recurring revenue of $15,000 and is expected to reach $36,000 in the next 6 months.


Future sales pipeline

According to Markets and Markets, the global enterprise data management market will reach $122.9 billion by 2025, growing from $77.9 billion in 2020 at a CAGR of 9.5% in the forecast period between 2020 and 2025. Key factors expected to drive this growth are the increasing need to effectively manage the hierarchical master data generated across departments, the adoption of IoT, and overall digitalization. Major industry players for the forecast period include IBM, SAS Institute, Teradata Corporation, Oracle, SAP SE, Talend, etc.

Below are the details of each customers and prospects in pipeline with projected revenue for next 6 months:

  • Reckitt Benkiser (US and UK), current customer with projected revenue of $ 100,000
  • Pfizer (US), current customer with a projected revenue of $ 80,000
  • GrantThronton (India), current customer with a projected revenue of $ 15,000
  • Musgrave retail, proposal stage with a projected revenue of $ 120,000.
  • Denali (India), proposal. proposal stage with a projected revenue of $ 750,000 (total contract value)
  • Proctor and Gamble(US and India), proposal stage with a projected revenue of $ 180,000
  • National Life & General Insurance Company SAOG (Dubai), proposal stage with a projected revenue of $ 1,25,000
  • Daimler (Germany), discussion stage with a projected revenue of $ 500,000
  • (total contract value)
  • NMB Bank (Tanzania) , discussion stage with a projected revenue of $ 90,000.


Planned revenues

  • May 2021 to April 2022 – $ 320,000.00 
  • May 2022 to April 2023 – $ 2.5 Million
  • May 2023 to April 2024 – $ 4.5 Million


Unit Economics

Currently the company is not yet able to give a correct estimate of how much is their acquisition cost for new customers.



Owned by the company, full data security is provided within the solution.


Information about the past Fundraising and future rounds.

Listed in the Best Bangalore-based Analytics Companies by Data Magazine, 2022. Joined the F10 Singapore’s First Fintech Accelerator, 2021. Joined the PwC Singapore’s accreditation program for corporate adoption readiness, 2021. Closed national clients such as Shriram Finance, Chef Social, and Local Ferment Co, and international clients such as Cube Monk from the US.

  • In the first round the company raised $130,000 from friends to get the beta out of the market
  • In the current round they have already raised an app. $270,000 out of the $650,000 – $870,000 they are seeking
  • In the future (1 year from now) they are also planning to raise additional capital in the size between $4,500,000 and $6,800,000.


Current Shareholders Structure

  • Chethan K R, CEO- 50% equity
  • Ashish Koushik, CTO- 25% equity
  • Bharath G, Promoter, Investor- 25% equity

The team plans to keep 10% for the option pool before they close the current round.


Synctactic.AI wants to become one of the main smart science data platforms.



Main barriers identified

1.Today cloud service providers are increasingly adding more modules and products that enable companies to adopt big data analytics and machine learning within their customers’ cloud environments. 

2.Vendor lock-in- With most of the enterprises using legacy tech infrastructure, there is a great dependency by these companies on their existing stack which is stopping them from exploring. 

  1. Hesitation to use a platform / automated system as there is a huge skill gap.


Exit Strategy

The company has already had offers for a buyout but they have declined them, because they believe they could still develop their services further.

In the future a possible exit could be made in the domestic (Indian) market to a bigger IT solutions providers and IT service companies. Furthemore an exit is also possible to any other large IT conglomerate that would like to expand their IT services/department.

Some examples of potential companies to which an exit could be made are Snowflake, Databricks.


Please note that we are yet to complete our in-depth DD. This is the information we have received from the start-up and have not cross-checked everything. If we find any untrue information in this memo and you commit, we will inform you. 

Investing in an early-stage start-up is a risky investment, therefore we advise you not to invest what you cannot afford to lose.