Bharat in Betastartup field guide
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No. 067Venture thesisSoftware and AI

Forward-deployed motions take AI to the 99%

Source

The idea

Use embedded, forward-deployed teams to discover and implement AI opportunities inside legacy verticals, consulting, services, systems integration, and manufacturing beyond Silicon Valley.

Why this is interesting

  • An embedded delivery model becomes repeatable when an implementer can name what transfers between customers and what must remain bespoke. Observe one customer process before packaging a general product.
  • A large addressable population is not evidence of a scalable service. Include discovery, integration, training, support and later maintenance in the comparison.

Possible starting points

01

One-process implementation study

With an operator’s permission, document a bounded recurring process and test a proposed improvement in a disposable copy of its inputs.

02

Second-customer handoff

Give a different implementer the first pilot’s reusable components and explicit exceptions, then test which parts genuinely carry to a second agreed setting.

Questions to answer

  1. Does the customer accept a measurable workflow improvement after the implementer leaves?
  2. How much of the next deployment can be reused without hiding customer-specific work?
No. 058Venture thesisSoftware and AI

The next wave of observability will be physical, not digital

Extend observability into physical operations using multimodal sensors and AI to make industrial environments measurable, diagnosable, and optimizable in real time.

No. 848Venture thesisSoftware and AI

Robotics-as-a-service access models

Reduce upfront financial barriers to business-critical robots.

No. 886ProposalSoftware and AI

AI leapfrogging marketplaces

Create AI-native marketplaces in digitally lagging sectors such as legal, construction, manufacturing, government, and health where no strong cloud incumbent exists.