Rishit Lakhani | Head of Solutions Engineering at REAP

01By the numbers

A decade of building.

Functions built from scratch, teams across three countries, and a decade in go-to-market.

10+

Years leading technical sales and GTM

100%

Quota attainment, every year

200%+

Peak year. President's Club at VMware

2×

First solutions hire, at Nile and REAP

02Profile

I join early and build the go-to-market function, before the process exists.

The pattern has been the same across my roles. I join before the process exists and build it. The discovery motion, the qualification rubric, the proof of concept, the playbooks the field runs on, and the team that carries it after me.

I did this at Nile as the first solutions engineer in stealth, building the function to a team of six across three countries and working with enterprise teams including Stanford, Tesla, Carta, and Sigma Computing. I am doing it again at REAP, an agentic AI company, as the first customer facing hire, from discovery through to the feedback loop back into product.

What connects the roles is the same work. Making complex, technical products clear to the people who buy them, and owning the commercial motion end to end, from the first conversation, to the close, to the playbook the team runs on.

NOWHead of Solutions Engineering, REAP

BASEDSan Francisco Bay Area

DOMAINAgentic AI, technical sales, and enterprise GTM

FOCUSBuilding GTM and SE teams, sales strategy, and agentic AI go-to-market

EDUCATIONMS Telecom, RIT. BE, Univ. of Mumbai

03Selected work

The work, role by role.

The roles change. The work stays the same. Build the function, grow the team, win the business.

Head of Solutions Engineering, REAP

2026 to now

Seed-stage agentic AI platform. First customer-facing hire.

Own the full technical sale. Discovery, demos, proofs of value, design partner conversion, and competitive positioning.
Set the quality bar for the platform's agentic AI reasoning. Every automated decision must establish what happened, why, the evidence, the impact, and the recommended action. Restating the input is rejected.
Built the qualification motion from scratch, including the scored account rubric that decides which prospects advance to design partner.
Own competitive and pricing intelligence for the category and authored the phase one pricing and packaging model.

Solutions Engineering Lead, Nile

2021 to 2025

First solutions engineer in stealth. Grew the function to a team of six across the US, Canada, and India.

Owned enterprise technical evaluations end to end across healthcare, retail, higher education, and enterprise IT.
Led enterprise projects for teams at Stanford, Tesla, Carta, and Sigma Computing.
Early product lead for Nile Guest Service. Ran discovery, validated it in the field, and drove it to adoption and recurring revenue.
Wrote the field playbooks, qualification criteria, and enablement the go-to-market team ran on. Presented at Tech Field Day.

Staff Systems Engineer, Team Lead, VMware

2018 to 2021

VMware SD-WAN. Promoted twice. Led a team of five systems engineers.

Carried and cleared quota every year, and reached 200%+ in a peak year to earn VMware's 2019 President's Club award.
Partnered directly with AT&T on large scale SD-WAN deployments and joint GTM initiatives.
Built the SE new hire program that trained more than 100 engineers and started a biweekly SE learning series.
Coached several engineers to promotion and served as first escalation when deals came under pressure.

04Where I can help

Advisory for founders taking technical and agentic AI products to market.

I take on a small number of advisory conversations. These are the areas I know best.

Building the SE and GTM function from zero

The discovery motion, qualification rubrics, proof of concept design, hiring and ramp, and the first playbooks. What to build first when there is no process yet.

Taking agentic AI to market

How to position agentic AI for enterprise buyers, set evaluation criteria for model reasoning, and separate a real capability from a demo. Where the technology creates value someone will pay for.

Sales strategy and pricing

Pricing and packaging, competitive positioning, qualification, and the commercial motion. Moving from founder-led selling to a go-to-market that repeats.

Also glad to talk about Startups and early GTM Technical pre-sales Agentic AI evaluation Leadership and team building

05Writing and speaking

Featured thinking.

Bylines in Forbes and IFMA, and current writing on agentic AI and where it actually creates value.

REAP

Agentic AIGo-to-market for AITechnical salesSolutions engineeringEvaluating model reasoningEnterprise buyingPricing and packagingThe first GTM playbook Agentic AIGo-to-market for AITechnical salesSolutions engineeringEvaluating model reasoningEnterprise buyingPricing and packagingThe first GTM playbook

06Recognition and service

Beyond the day job.

Limited Partner, Z21 Ventures

ACTIVE LP · EARLY-STAGE VC

RIT MIS Advisory Board

SAUNDERS COLLEGE OF BUSINESS

President's Club

VMWARE · TOP GLOBAL PERFORMER

Published Bylines

FORBES · IFMA

Tech Field Day

PRESENTER

07Questions

Common questions.

The things founders, operators, and recruiters ask most often.

Who is Rishit Lakhani?

Rishit Lakhani is Head of Solutions Engineering at REAP, a seed-stage agentic AI company, based in the San Francisco Bay Area. He builds go-to-market and solutions engineering functions from zero at early-stage technical companies, and has been a startup's first solutions hire twice, at Nile and at REAP. Before that he led a systems engineering team at VMware for SD-WAN.

What does Rishit Lakhani do at REAP?

As the first customer-facing hire at REAP, he owns the technical sale end to end: discovery, demos, proofs of value, design partner conversion, and competitive positioning. He built the scored qualification rubric that decides which accounts advance, authored the phase one pricing and packaging model, and sets the quality bar for the platform's agentic reasoning.

What does Rishit Lakhani advise founders on?

Three areas. Building the solutions engineering and go-to-market function from zero, including hiring, ramp, discovery motion, and the first playbooks. Taking agentic AI products to enterprise buyers, including how to separate a real capability from a demo. And commercial strategy: pricing, packaging, qualification, and moving past founder-led selling.

How do you build a solutions engineering function from zero?

Start with the qualification rubric, not the demo. Decide in writing which accounts are worth a proof of value and which are not, then design a proof that tests the one thing the buyer actually doubts. Write the discovery questions down so a second person can run them. Hire the first solutions engineer for judgment and range rather than product depth, because the product will change. The goal is a function that outlasts the founder and the first hire.

How should an enterprise buyer evaluate agentic AI?

Judge the reasoning, not the output. A useful agentic decision establishes what happened, why, the evidence behind it, the impact, and the recommended next step. Restating the input in fluent language is not analysis. Test the system on your own messy data, on cases where the right answer is to escalate rather than act, and ask who is accountable when the agent decides.

How do you get in touch with Rishit Lakhani?

Book a call at calendar.app.google/a9HY5gUDGSssE5bV9 or email lakhanirishit@gmail.com. He takes on a small number of advisory conversations at a time, usually with founders building technical or agentic AI products for enterprise buyers.

Let's talk

Building something at the edge of AI and infrastructure?

Whether it is building a solutions team, sharpening a commercial motion, or pressure testing where agentic AI actually earns its place, I am glad to trade notes.

Book a call Email me

I build the go-to-market engine behind AI infrastructure companies. The team, the motion, the trust.