1/10 The Idea: It all started with a personal problem.

Working on projects involving geolocation previously, we had to build lots of internal tools.

We saw:

Companies collecting huge amt of lat/longs BUT

not having the right tools to use this data in operational decisions.

2/10 First round of validation: ~50 companies.

Found out that~

Internal tools were:
1. Needed eng bandwidth to update
2. Painful to maintain
3. Didn’t give localized, real-time insights

Business users keep waiting for dashboards for engg. sprints & metrics from analyst teams

3/10 Premise:

“Getting insights on ground operations for city/ ops teams should be as simple as getting insights on websites for marketers.”

To customize decisions based on how areas behave.

We packed our bags, quit jobs to move to a new city. @localeai was born in March’19.

4/10 MVP -> PoCs:

Rishabh hooked together our first MVP (a prototype in 3 days).

We demoed that to get some PoCs with top delivery startups in India.

Countless hrs. in their offices + 16 In-depth interviews gave us our:

1. Pain points
2. ICP
3. Solution
4. Positioning

5/10 Fundraising: With these answers, we needed to start building the product now.

For the product, we needed engineers
For engineers, we needed $$$
For $$$, we needed to raise

Fundraise is a complete sine wave on its own. We finally closed our pre-seed from Better Capital!

6/10: Now what?

We always wanted to be a product-led company:

Try Locale, get the feel, decide value.

We underestimated the complexity in building data pipelines at large scale, high freq & combining data from:

1. marketing events
2. supply pings
3. order transactions

7/10 Postponing 1st launch~

V1 ready after 4 months. But,

After deploying it for India’s largest micromobility co, users didn’t know

“which metrics to create” & “what to measure”.

Part of it was bad UX!

We realized we needed to make the process of getting insights simpler!

8/10 v2’s philosophy:

Help users make decisions by showing the right metrics.

We picked our target industries -> wrote top decisions -> mapped each decision to top metrics.

Different decisions required diff visualizations + diff metrics.

v2 was so actionable & insightful!

9/10 Validating v2:

I started our cold outreach & doing demos of v2 while the team got down to building!

Lockdown helped us do ~70 demos in 2.5 months.

We got lots of nice feedback & rejections.

Now, we’ve engineered some solutions into the product to combat those rejections

10/10 The Grand Launch: We are finally ready to present @localeai to the world.

Product launch is like a marriage proposal:

You want a positive response + you’re scared of failure + nervous about everything else.

Product’s not perfect but I’m proud of how it turned out🥺

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