Mastek Accelerator Program


A sixteen-week accelerator programme that provides start-ups with possibilities for development and expansion as well as access to main partner mentoring, investors, alumni, and a networking platform. The opportunity to develop a corporate-ready solution will be given to the selected start-ups.


Get mentored by senior executives

Co-create your solution

Gain market access through CII CIES Corporate Accelerator Program

Attain a faster product – market fit

Opportunity to showcase your products and services directly to  leadership

Opportunity to get a paid pilot.


Algorithmic content curation
Facial recognition
Activity detection model
Algorithmic content curation

Algorithmic content curation 

 A vast amount of thought leadership content from credible sources needs to be repurposed for publishing in either microblog or blog or vlog format on various channels. As a pre-step, to prevent plagiarism the solution needs to check if proper credits have been mentioned.

Use Case: 

1.Creation of rights-free (free-of-attribution and free of Plagiarism) thought (leadership) content in terms of aggregation and curation

2. Automatic publishing of such content on social media handles including LinkedIn, FB, etc. 

3. SEO of content to build community and engagement. 

4. Actual engagement with the community through a bot ( social curation – voting, rating, flagging, etc).

A manual Corpus of Thought leadership content from highly esteemed publications and journal like HBR, Economist, Fortune, INsead is in place as of now. These articles are tagged with Metadata for e.g. articles on Leadership, Governance, People Diversity and Inclusion, etc. At a later stage or in a different avatar, there are links to 50 highly esteemed publications from where articles should be picked up basis a user input taxonomy to do the below.

The solution should pick up say 2 or 3 articles based on input metadata/taxonomy and recency of a topic , produce new / rephrased content , the new content should be free of plagiarism, there should also be a summary of new content, the latter should be pushed to social media handles on click of a button.

Success would be measured by ability to deliver.

  • Access to unlimited high-performance quality content.
  • Automatic publishing of content.
  • Community aggregation of a million numbers per social media channel.
  • Actual engagement as reflected in comments, sharing, tagging, etc.  

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Facial recognition

Facial recognition

Retail stores of large scale have large number of employees and staff from various vendors moving in and out of stores. They work in various shifts and exact tracking of their time 'in-store’ is hard. Also, investing in high resolution cameras over a space of 10K sq. Feet is not feasible. However, the security cameras already installed can hold significant information on movement of individuals.

E-commerce companies are constantly looking for on-boarding new customers. One of the techniques they use is marketing through network of existing customers. An existing customer say - Noah has shared their social network co-ordinates in their profile, it is possible to extract images of people with whom Noah is mostly seen. Specific promotions can be sent to Noah's friends and improve the likelihood of on-boarding them as well.



Expected solution should be able to Utilise recorded video feeds from security cameras typically 3MP to 5MP and night vision enabled.

Leverage images on social media through web-scraping

From this feed identify a registered users or set of visitors (Customers, Store staff, Brand representatives, House-keeping staff)

Record timestamps when each individual was identified and the camera which identified them.

Success criteria:

Accurate - Registered users should be identified in at least 95% of cases

Unbiased - Ability to prove that it works equally well for various demographics (Age, Gender, Race)

Creative - Generate metrics that are insightful for store operations and security team (e.g. anomalies in time of entry or exit)

Scalable - Solution should be able to scale to a store with >10K sq. feet floor space

Extendable - Provide interface for adding more users to the recognition model


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Activity detection model

Activity detection model


Live video feed can be analysed for body pose estimation and meaningful human activities can be labelled. E.g. Walking, running, eating, drinking. Mature algorithms can even identify activities in a store such as picking items from shelf, putting them in cart / elsewhere, putting items back on the shelf.

It can also monitor activities of patients and nurses in a hospital e.g. taking oral medicines, administering injections, helping patient walk etc. Depth Sensor cameras capture sufficient information for activity detection and (unlike RGB cameras) they preserve privacy of the individuals.


We need a solution that can apply video intelligence to live feed from a ‘depth sensor’ camera

Identify predefined set of activities

Record timestamp and duration of the activities


Success criteria:

Accurate - predefined set of activities should be identified in at least 95% of cases

Unbiased - Ability to prove that it works equally well for various demographics (Age, Gender, Race)

Creative - Generate metrics that are insightful for operations and security team (e.g. fall detection)

Scalable - Solution should be able to scale to a hospitals with >250 beds or stores with >5K floor space

Extendable - Provide interface for adding more activities to the model

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-> You have an innovative tech solution in the Facial Recognition, Algorithmic content curation and activity detection model which meets one of problem statement.

-> Your business is a growth stage start-up or scale-up.

-> Your business has at least 2 full-time team members.

-> Willing to travel to the locations for implementation


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