At FreeStand, empathy is one of the most core values. We are an emotional bunch. We whoop at every win and lament at every loss. This ability to just feel, is what I believe leads my co-founders to sense the pains of today which might become the torments of tomorrow for our clients. This has led FreeStand to develop some amazing solutions for our clients. One of such recent discoveries was the problem of consumers trying to claim multiple samples of our clients’ brands from the same address - the address duplication problem.

We had the basic checks in place to identify strictly duplicate addresses but since Indian consumers are crafty, many of them could bypass them by using what I call - linguistic tricks.

One such instance is, we have had consumers trying to claim samples by giving 7-A, ABC Street, City Name, State Name, Pincode and VII-A, ABC Street, City Name, State Name, Pincode as their addresses.

Upon review, such addresses would have to be marked as duplicate and we would dispatch a sample for one of them. We receive thousands of sample claims daily, creating a review hell for anyone verifying addresses before processing them for delivery. As a startup, every resource at hand is super valuable. And time, is one of the most paramount amongst them. Every moment that is spent not creating value by even one members incurs a significant cost on all others. With our regular volumes, this would take about 8 hours on a daily basis. On days of increased volumes, the process would go upto 12 hours at times as well.

As we are in the business of dealing with end consumers of our clients, it is always made sure that in this process of review and detection, a genuine address shouldn’t get missed. Fearing the possibility of numerous such cases occurring, I was sceptical about taking the human judgement out of the process.

Similarity Score

One of the core tenets of building things at FreeStand is iterations. And this problem was solved similarly. It started out when one of our developers and the CEO was catching up with each other post work hours. The developer decided to take on building the first iteration of enhancing our review system by creating a similarity score for the reviewer. With vector embeddings to create the score and some UI/UX magic to streamline the review process, he was able to reduce the time by about 20%; bringing it down to 5 hours on regular days though it was still 8 hours on high volume days.

The results spoke for themselves and my earlier reservations about algorithmic intervention in the judgement process were lifted.

LLM opinion

A follow-up conversation with my co-founders (who had also participated in a few review sessions) along with other regular reviewers made it clear to me how excruciating the experience would become. It was very evident that the existing solution was due for an upgrade. The developer who had worked on the first iteration was occupied with another crucial build (a follow up piece coming soon on that!) and so the second iteration was taken over by someone else.

We started discussing possible ways in which we could solve it and he made a suggestion about prompting one of the LLMs to make a judgement call on the duplicity of the addresses having a similarity in the mid ranges. These made the bulk of the addresses in review. This seemed like a really intriguing proposition to me and I told him to run a test on the subset of the data. The experiment involved running several variations of prompts with different OpenAI models. We tracked accuracy of each prompt with different models, keeping economics and latency in mind for every combination. The results of this experiment showed great promise and we decided to implement it.

This implementation led us to further cut down our review timing by HALF! We had reached to 2 - 2.5 hours from 8 - 12 hours. Another win in the process, another moment of kaafi khushi for us.

Agentic Consensus

But I felt more could more could be done. More importantly, I felt it was one iteration shy from achieving its true potential.

Amid the constant stream of new model releases and the buzz of election season in Delhi, it struck me—we could further reduce ambiguity in the review process by having multiple LLMs weigh in on dubious address duplicates.

A consensus mechanism would be reliable enough to reject further duplicate address. And so we decided to proceed with building an “Agentic Consensus” layer on top of it. We decided to use 3 different LLM AIs for it - Grok, OpenAI gpt 4-o mini and Anthropic Claude 3.5. When all three of them would have the same opinion on the duplicity of the address, then our review system would accept that result. In case of a difference in opinion by even one of them, the address would be left to review. And again with a little bit of UI/UX magic to this, we ended up bringing down our review time to under 30 mins. A 95% reduction.

I am told that the reviewers wake up a lot happier and with a lot of possibilities.

Product 1: Planning Agent

Product sampling is an omnichannel activity, you can distribute your samples at offline retail stores, via quick or e-commerce platforms or via promoters. FreeStand has over 50 sampling destinations with over 1Million+ unique setup combinations.

To help a marketer navigate this quickly, we have created a “Planning AI. " Its primary focus is to help a marketer set up an entire sampling campaign with hundreds of thousands of samples simply by using natural language.

This alone has been a huge bottleneck for marketers at FMCG companies of all sizes. A well-planned product sampling campaign can be ten times more successful.

Below is a screenshot of what it looks like:

Product 2: Validation Agent

India is a complex country with a huge socio-economic divide. Brands often give away product samples to their customers for free; it would be helpful if you could guarantee that the samples reach only the most desirable customers.

FreeStand’s “Validation agent” attempts to improve customer and data quality across all product sampling workflows.

The validation agent has several capabilities, including suggesting the purchasing capacity for a given address and facilitating warehousing and inventory operations.

The technology behind the builds:

We are leveraging OpenAI’s Assistants API, tool calling, and custom functions to develop our Planner agent.

The Planner agent uses OpenAI’s API to interpret user inputs and execute complex workflows. Tool calling enables seamless integration with external functions customised to perform specific tasks. These custom functions enable the agent to handle specialised queries and access on-demand customer data, ensuring highly accurate and contextually relevant results.

For the Validation agent, we are utilising vector embeddings in combination with a PostgreSQL database enhanced by the pg-vector extension. By integrating OpenAI’s embedding model with the vector database, we efficiently store, search, and retrieve high-dimensional embeddings, enabling a robust similarity matching and validation process.

Conclusion

It is still early days for integrating AI capabilities into our offering, but we are thrilled to see the benefits. From a technical perspective, we feel that we should restructure our backend databases to be more optimised for the additional data load that AI tends to bring.

Select customers with whom we have shared our offerings have been great design partners during our development process, and we thank them wholeheartedly.

We are excited to share more about what we build in the future. Cheers!

On 04th July 2024, FreeStand secured the Silver award at the afaqs! Marketers Excellence Awards in the category of Best Product Sampling for the innovative digital sampling campaign for L'Oréal India's CeraVe launch.

This recognition highlights the CeraVe launch Digital Sampling campaign—the first of its kind marketing campaign in the country!

The award was even more trilling as FreeStand was competing with brands such as Coca-Cola and KRBL India Gate Basmati Rice.

ET BrandEquity Shark Awards

On the next night, 05th July 2024, FreeStand celebrated winning the bronze ET BrandEquity Shark Awards for our digital sampling campaign in the FMCG-Personal Care category(a highly competitive category) for the CeraVe launch. This victory marks the third major accomplishment with L'Oréal India for this campaign, underscoring the successful partnership between the two companies.

Competing against industry giants like Ogilvy, ITC, and Wipro Consumer Care, this victory was delightful for FreeStand.

Looking Ahead

With these impressive achievements, we continue to set the bar high in the product sampling industry. The success of the CeraVe launch campaign with L'Oréal India is a testament to the effectiveness of digital sampling and the innovative solutions FreeStand brings to the table.

Here's to many more milestones and continued success in the future.

Kaafi Khushi

Why It Matters?

This award is more than just a trophy on our shelf. It highlights the power of strategic digital sampling & marketing in revolutionising product launches. In a country as vast and diverse as India, traditional sampling methods often don’t cut it. Our campaign's success demonstrates how leveraging modern martech tools can not only reach millions but also engage them in meaningful interactions, building lasting relationships with our customers.

The Revolutionary Use of Digital Sampling, a Martech Campaign No One in India Can Execute

Our campaign was a blend of creativity and cutting-edge technology. This sampling campaign simply would be impossible to imagine without the modern day marketing tools. Sampling has been the same forever, FreeStand’s sampling innovation coupled with the targeting abilities of Meta platforms and automated chatbots enabled the brand to deliver a targeted, measurable, qualified and most importantly pilferage free sampling experience. 

FreeStand Sampling SaaS Platform: Our platform managed all operations involved in executing the digital sampling campaign, from customer interactions to data analytics and fulfillment operations. We successfully executed over 2,000 deliveries daily with a turnaround time of just three days.

What We Achieved:

• Reach: We reached over 10 million women in Delhi and Mumbai with distinct skin conditions.

• Interaction: Over 200,000 women exchanged more than 2 million messages with our chatbots.

• High Feedback Rate: We achieved 30 times the average market feedback rate, with a high intent for immediate purchase.

• High Customer Interaction: Our chatbot system kept consumers engaged for an average of 2 minutes and 30 seconds.

• Robust Data Collection: We gathered seven times more unique first-party data per person, revealing top pin codes for retargeting and prevalent skin challenges.

Why It Matters for FMCG Product Launches Like CeraVe

Launching an FMCG product in India requires more than just traditional marketing techniques. Our digital sampling campaign for CeraVe shows how advanced martech tools can revolutionise product launches.

Here’s why it’s a game-changer:

• Targeted Reach: We ensured our marketing efforts were directed towards the most relevant audience, maximizing impact and efficiency.

• Customer Insights: By collecting structured and validated data, we could make better decisions in real-time.

• Qualified Leads: We made sure that samples reached potential customers who met specific criteria, increasing the likelihood of conversion.

• Feedback Collection: Our effective re-engagement strategy captured valuable feedback and enhanced customer experience.

This successful campaign sets a new standard for product launches in the FMCG sector, highlighting the importance of integrating modern marketing technology to achieve impactful and measurable results. 🚀

Dr. Apoorva Ranjan Sharma, MD and CEO of Venture Catalysts said, “We are thrilled to announce our investment in FreeStand. As per various industry sources, in India, an estimated 20,000 products are sampled annually. With each brand distributing an average 300,000 samples across online and offline sales channels, this market is expected to grow to $1.5 billion - $3 billion by 2027 and this allows FreeStand’s innovative platform to emerge as a game-changer, offering a seamless and data-driven approach to executing sampling campaigns.” 

We have gained traction among major FMCG players like HUL, Nestle, Reckitt Benckiser, L’Oreal, Nivea, Piramal Healthcare, Cipla Health, and Emami.

FreeStand's capabilities extend across multiple channels like Ad-Networks, E-Commerce, and QuickCommerce. With the new capital, the company plans to expand its offerings to include event and retail sampling channels and leverage AI to enhance and simplify the product experience for FMCG marketers.

Konark Sharma, Co-Founder and CEO of FreeStand shared, “FreeStand’s tech supercharges what FMCG brands can accomplish with product sampling. Brands today can utilise the FreeStand platform to sample across multiple channels like Ad-Networks, E-Commerce and Quick Commerce, with the help of this capital we will be expanding our offering to include event and retail sampling channels. We will also be leveraging AI capabilities to enhance and further simplify our product experience for FMCG marketers.”

Sneh Soni, Co-Founder and Head of Business Operations at FreeStand further added, “Setting up a sampling campaign takes at least 20-30 days for an FMCG brand today, with the help of the FreeStand platform, brands are now able to find the right sampling channel/partner in less than 3 minutes. We’re excited to have the backing of Vcats, and Nocking Point in this round, and this capital will help us further expand our data and integrations with various sampling channel partners to enable FMCG brands to gain a greater ROI from their sampling campaigns.” 

Kavach Chandra, CTO and Co-Founder said, “Sampling, today is one of the most effective ways of getting your product to the right consumer. Tech allows us to capture and encapsulate the scale of all that is possible with this marketing activity and with this capital, we will unlock new and higher levels of scale, bringing intelligence to the entire ecosystem of sampling for all the stakeholders involved.”

Now back to building 💪🏽

At GFS they tell you to “ask for help from any stakeholder from within Google, and we will give it our best shot to make it happen.” Of course, there are jokes about whether Sundar is on the table.

One of our requests yielded us an introduction to Guneet Singh, the first call was 45 minutes long, where Guneet spent 35 minutes just hearing us out and 10 minutes offering us advice, and those 10 minutes were pure gold minute for minute, the time ended and we felt there was more to learn here, we asked him for a 2nd meeting and he generously agreed, this led to a third meeting, I vaguely mentioned that we were raising and he showed interest, and voila!

Guneet Singh isn't just an investor at FreeStand; he' also the mentor who delves into the details of our business model and product nuances. He offers strategic insights which is strengthening our business and operational capabilities with each interaction. With Guneet, the relationship extends beyond mere financial investment— it's about visualising exponential growth and strategic market positioning.

Guneet Singh is super kind and generous, patiently answers all of our questions and offers us deep insights about selling to FMCG brands and negotiating complex partnerships.

At FreeStand, we are thrilled to have him on board, godspeed 🚀

We had a great year, super easy to summarize in metrics, super hard to capture via feelings, thoughts and emotions, but let’s see how we do at this challenge.

This will be a mix of the two feelings and metrics, we will have a mention towards the end with all of our key metrics, but everything before that will be an attempt at capturing the feelings and emotions behind the year.

We raised funds, twice, closed more customers than we ever did and grew 100x, interviewed several, wrote a lot of lines of code and had about 3 parties in the entire year.

For a pre-seed stage startup, we served 5 Fortune 500 enterprises. We partnered with the largest startups in India. What!!!!!

We didn’t update our website for the 2nd year in a row and still got about 40+ inbound demo requests. How is this still happening???

We had a key tech team member join our business team and that enabled us to build a business unit. Not only did this make our business unit stronger but also enabled our tech team to gain a true insider they could trust on matters of the whys of our product and feature decisions. It’s incredible I tell you, this was my idea, pat on back Konark!

We graduated from Google Startup Accelerator, ISB-Iventure and IIM-CIPs Samriddh Accelerator, these were 3 different flights for the respective accelerator’s demo day.

Graduating from the Google startup accelerator was awesome, we felt like somebody as a result and got introduced to Guneet Singh, who eventually ended up joining us as an investor and a business & sales advisor. Wow!!

I met a popular Indian VC who has an amazing Twitter presence and semi-annoyed him, cause he couldn’t understand or thought that we are stupid to call ourselves a marketplace. Sigh!

Oh, our tech team is jacked, everyone goes to the gym between 6:30 to 9:30 PM, almost making it impossible for our CTO to have an all hands. Our youngest team member Pratik, with whom I share my alma mater completed 1 full year of being stupid and awesome, like all interns should do, we offered him a PPO, and that phone call melted my heart, a few joys of being a founder. Also, no timeline was met without delay, may that change in 2024. Gen AI features have gone into development Q1 2024 should see the first few releases going live.

Mohit and Lakshay continued to be the backbone of our stability. Sushant delivered a successful hiring event in the form of Externship 1.0 and is now leading our AI efforts too, Abhishek pumped up our infra game, Kavach was more than a CTO, managed legal and ops like a champ, Sneh rode the rocketship and brought us to $100k, how she managed this is only known to her, but she did and we are all grateful.

I continue to be grateful for the opportunity that I get to lead this team day in and out.

2023, was awesome for all of us here at FreeStand and we hope, you the reader also had a great year. We at FreeStand would like to wish you, your team and your families a very happy and prosperous 2024.

Key metrics:

1. Revenue TTL: $100k

2. Samples distributed: 1.3Million

3. Brands Served: 7

Thanks for checking us out, learn more at www.freestand.in

Read on to understand all the decisions we made regarding server, database and deployment.

Server: Firebase to NestJS

We went from a serverless technology, using Firebase, to a self maintained server using NestJS framework. We used Firebase auth and cloud functions before for any computational heavy tasks or tasks that required security, because we wanted Firebase's auto scaling feature and a short time to market.

The problem with using cloud functions was that although cloud functions make it easier to micromanage situations and needs because of their easier deployment, they add extra complexity on the overall architecture itself by virtue of not having a centrally maintained ecosystem of code like say in a NestJs codebase.

Cloud functions also suffer with the problem of cold starts.

Hypothetically we could have everything running on cloud functions (with the minimum instance limit set to reduce cold start problems) but that’s a cost tradeoff we need to address.

The things we need to consider to understand this decision are:

• The costs associated with running everything (or most) things on cloud functions with a minimum instance limit set to at least 1 to “reduce” (not eliminate) cold start issues.

• The hidden dev costs associated with poor concreteness of the codebase (with everything running on separate cloud functions)

• We may even want to consider becoming too vendor locked in as a potential future downside.

Advantages

NestJS solved the issues by being a self managed framework to build server side applications . Other great thing about Nest is its robustness and maintainability. NestJS code has its own structure which brings all the developers on the same page and creates a harmony within the team.

• Great code infrastructure

• Robust framework

• Consistent coding experience

• Great abstraction for features like securing API endpoints, authentication, etc.

• Typescript support

• Easily extendable

Disadvantages

• Steep learning curve

• Manual scaling

Database: Firebase to PostgreSQL

Database too was the Firebase Realtime database (RTDB) and Firestore. The problems with RTDB were with scalability, our claimed samples are now close to 1-2 lakhs, and a lot of data was stored about each sample. We moved from RTDB to Firestore, but it also gave these problems:

1. No-SQL has issues dealing with relational data.

2. Performing analytics on the data itself becomes challenging as pointed out by Kavach.

Advantages

Firestore is a No-SQL database and PostgreSQL is a relational database. We made this transition because we wanted a ground up relational approach to our data.

• Relational database for better analytics

• Robust

• Open source

Disadvantages

• Not flexible like other No-SQL databases

• Steep learning curve

As a bonus we also implemented Prisma as our ORM so that changing databases in the future is a breeze.

Deployments: Firebase to GKE

Our deployments were also done using the Firebase hosting service. A big disadvantage of it is that it was not possible to deploy docker containers on it. We needed containerisation for a much better developer experience and with the new backend we want to be able to deploy Docker containers. For that we use Docker and Google Kubernetes Engine (GKE).

Advantages

This was the right choice because.

• Helps in managing deployment pipelines.

• Provides auto-scaling capabilities on an application level.

Disadvantages

• Steep learning Curve

• Complex

As a bonus we implemented CI pipelines using Bitbucket for automated testing using Cypress.

About Sampling Central

Sampling Central (powered by FreeStand) is a SaaS enabled B2B marketplace for all your product sampling solutions. It helps you manage, track and analyse all your sampling campaigns at one place. To explore our product, you can contact us at anytime.