Somesh Nayak — Founder's Office | Product Strategy

I work on messy problems before they become obvious products.

I'm Somesh Nayak, a B2B SaaS professional with 2.5 years of startup experience working across customer discovery, zero-to-one validation, product feedback, GTM, AI product evaluation and business operations.

Somewhere between product, customers and execution.

My experience has rarely fit inside one function. I started in business analysis and gradually moved closer to customer problems, product decisions, GTM and founder-level execution.

I spent approximately 2.5 years at Ninety North Software Pvt. Ltd., a B2B SaaS startup where the product evolved from Enfund to Bilderly and eventually ywork.ai.

My work moved across market research, customer discovery, product validation, customer demos, GTM, AI product evaluation, CRM, operations, hiring and internal documentation.

The part I enjoy most is the stage where the answer is not obvious yet: understanding what customers are actually struggling with, testing whether the problem matters, structuring what needs to happen next and then helping execute it.

My Civil Engineering background also gives me useful construction-domain context, particularly around procurement, supplier quotations, BOQs and takeoff workflows.

Customer → Product

Discovery, workflow mapping, validation, feedback synthesis and product iteration.

Product → Market

Product demos, early GTM, customer objections, CRM and commercial feedback.

AI → Business Value

Evaluating AI workflows through accuracy, reliability, latency, cost and customer usefulness.

Founders → Execution

Cross-functional execution across product, GTM, operations, hiring, documentation and internal initiatives.

Selected Work

Three problems that shaped how I think.

Not just what I worked on — how I approached the problem, what we tested and what changed as a result.

Customer Discovery · Zero-to-One

Finding a problem customers would pay to solve.

During UK construction-market discovery, recurring problems appeared around supplier quotation management, RFQ chasing, quote comparison and project-document organisation. Instead of immediately treating those conversations as a feature wishlist, we tested whether the underlying workflow was painful enough to pay for.

Discovery

Conducted 50+ structured conversations with UK contractors, developers and suppliers to understand workflows and recurring friction.

Validation

Worked with a £2,000 internal discovery budget and supported manual delivery of parts of the workflow before full software development.

Learning

Used real customer behaviour and willingness to pay as a stronger signal than positive interview feedback alone.

Outcome

The validation resulted in 4 paying customers before the full software product existed, giving the team stronger evidence about which workflows were worth productising.

Product Feedback · GTM

Turning customer conversations into product feedback.

As Bilderly evolved into ywork.ai, my role sat close to both the customer and the internal team. Product demos became more than a sales activity — they were another source of workflow evidence, objections and product feedback.

Customer

Conducted or participated in 100+ product demos across UK and US markets, understanding workflows and customer expectations.

Feedback

Captured objections, feature requests and recurring workflow problems and communicated those patterns to founders and technical teams.

Commercial

Supported early product adoption and approximately 15 paying deals while maintaining a feedback loop between prospects and the product team.

What changed

The product evolved around workflows including document organisation, RFQs, supplier quotation management, quote comparison and later BOQ/takeoff use cases.

AI Product Evaluation

Evaluating AI based on the workflow — not the model name.

As AI became more central to the product, I participated in evaluating OpenAI and Claude across real product workflows. My contribution was from the product and business side rather than ML engineering.

Evaluation

Compared outputs using practical criteria including quality, accuracy, reliability and prompt performance.

Trade-offs

Considered latency and cost alongside output quality instead of treating model accuracy as the only decision criterion.

Product Lens

Evaluated whether outputs were actually useful for the customer workflow rather than simply technically impressive.

Takeaway

AI product decisions are trade-offs between quality, reliability, speed, cost and customer usefulness — not a benchmark competition.

Understand → Validate → Structure → Execute → Learn

Understand

Start with the customer, workflow and business context before jumping to a solution.

Validate

Look for behaviour, urgency and willingness to pay rather than relying only on stated interest.

Structure

Turn an ambiguous problem into hypotheses, priorities, owners and measurable next steps.

Execute

Work across teams and functions instead of stopping once the analysis is complete.

Learn

Bring customer and market feedback back into the next product or GTM decision.

Worked closely with founders. Here's what they said.

Recommendations from the leadership team I worked with at Ninety North / ywork.ai.

“

He spoke to 50+ UK real estate professionals, identified a real market pain, and came back with 4 paying clients — before we had built a single line of code. That model became the foundation of ywork.ai. What sets him apart is his ability to identify what needs to be done and execute without being told.

Nishant Singh

Founder & Director — ywork.ai · Ninety North Ltd UK

View signed recommendation ↗

“

He was not just a support function. He was a thinking partner. Somesh partnered with me on designing our target customer segments, building outreach strategies, and executing our product launch. He approached every challenge the same way — understand the problem deeply, build a clear plan, and execute without waiting to be told.

Nisha Singh

Co-Founder & Director — ywork.ai

View signed recommendation ↗

Broad enough for the founder's office. Focused enough to execute.

My experience spans multiple functions, but the common thread is understanding problems, creating structure and moving work forward.

Product & Strategy

Customer Discovery Market Validation Workflow Analysis Product Validation User Feedback Synthesis Zero-to-One Validation Feature Prioritisation Support

GTM & Commercial

B2B SaaS GTM Product Demos Account Research Early-Stage Sales HubSpot Apollo.io Pipeline Visibility

AI & Analytics

AI Product Evaluation Prompt Testing LLM Workflows OpenAI Claude n8n SQL Excel Python — Working Knowledge Tableau

Cross-Functional Execution

Founder Collaboration Stakeholder Coordination SOPs Documentation Hiring Interviews Onboarding Webinars Events

Treelync

An AI mock interview project built around a focused 20-question / 20-minute interview experience. The project explores how candidates can practise realistic interview conversations and receive more structured preparation before speaking with an actual interviewer.

Looking for problems worth solving.

I'm exploring opportunities across Founder's Office, Product Strategy, Product Operations, Associate Product Management and GTM-focused roles.