Startup Stories - Mixergy: Episode #2293

Guest: Tarun Thumala, Founder of Press W
Host: Andrew Warner
Date: January 12, 2026

Episode Overview

This episode features Tarun Thumala, the founder of Press W, a $2 million/year AI engineering firm based in Austin, Texas. Andrew Warner invites Tarun to break down how he built his AI services business from scratch, examining their business model, sales strategies, team dynamics, and how they've used AI both as a product and an operational accelerant. The discussion is candid and practical, offering deep insights for anyone interested in building or scaling a tech-enabled services business.

Key Discussion Points & Insights

1. Press W’s Business Model & Revenue

• Revenue Status:

  • Press W will surpass $2 million this year (00:33).
  • Last year revenue topped $2 million (00:38).

• Business Focus:

  • Primarily a custom AI application developer for enterprise and mid-market clients.
  • Projects range from enhancing SaaS products to building internal operational tools.
  • Notably, they handle complex, unstructured data problems (i.e., document workflows for background checks) (01:14).

• Example Project:

  • Automated document review for a background-check company, streamlining work across 5,000+ unique US court record formats. Achieved automation of 90-95% of manual document handling (01:14–02:02).

• Pricing Model:

  • Evolving from traditional time-based consulting to fixed-fee and capacity-based models.
  • “We are constantly getting faster and better… How do you charge more value-based rather than just raw time spent? That’s tough.” – Tarun (02:04)
  • Offers a “forward deployed engineer” model: dedicated engineer for a set period, priced on capacity (02:04–03:16).

2. Founding Story & Team Dynamics

• Motivation for Consulting Start:

  • Personal: Post-acquisition, needed deeper real-world context and business knowledge (03:56–04:08).
  • Market Insight: AI solutions demand more professional services than standard SaaS; plug-and-play isn’t enough (04:08–05:24).
  • Flexibility & Learning: Professional services provided cashflow and fast learning through proximity to client problems (05:24–06:05).

• Getting the First Customer:

  • Came through relationships—specifically, a partner’s former employer.
  • “The story of our first customer… has been largely the same. It’s relationships and networking.” – Tarun (06:08)

• Three-Partner Team:

  • Tarun (business development and translation between tech and business needs), two others in architecture and AI.
  • Importance of working with people you enjoy and trust (07:30–08:11).
  • “I'd rather my story be written with them than without it.” – Tarun (07:30)

3. Customer Acquisition: Network, Events, and Referrals

• Early and Current Pipeline:

  • Less than 10% of business came from “old relationships.”
  • 90%+ net new, but all driven by networking, events, and industry connections (09:04).
  • “I’m not always trying to go out there and hunt for sales. I just naturally talk about what we’re doing.” – Tarun (09:04)
  • The AI wave means more people are open to these conversations.

• Networking Tactics:

  • Active at select industry networking events; now often hosts his own (10:16–10:45).
  • VC and PE relationships developed more from genuine curiosity and sharing knowledge than pushing for referrals (11:04–12:28).
  • “It's like 80% me just really excited about AI and them talking about that.” – Tarun (11:40)

• Time Investment:

  • About once a week at events, but focused on deepening meaningful relationships over maximizing volume (12:35–13:42).

• Event Hosting:

  • Ran polished industry events (“AI at the Proper Hotel”) as a way to “cement ourselves as a real player in the Austin space” (17:25–18:13, 19:20).
  • Budgeted $20-25K (with $10K from sponsors); direct ROI on at least one deal per event (17:37–19:14).

• Results:

  • Events aren’t usually about direct sales but about increasing “surface area” in the market. BD outcomes are “a bonus” (16:12–17:25).
  • Growing plans: secret “AI World Fair” event and smaller gatherings in the pipeline (19:20).

• Notable Quote:

  • “What I find with those events is like it’s almost never direct… Mostly just increasing Tarun surface area in the world.” – Tarun (16:12)

4. Product Offerings and Specialization

• Evolving Positioning:

  • Previously used terms like “AI Transformation,” but moving toward more concrete outcomes and ROI (26:09).
  • “AI transformation… is a black box with no instructions and no clear outcome for the business… People are sharpening up their budgets and… asking: How?” – Tarun (26:09)

• How They Pitch:

  • In-person elevator pitch: “I run an AI engineering firm.”
  • Focused on building generative AI applications, placing 80%+ team emphasis on engineering, not just advisory (23:03).

• Competitive Landscape:

  • Press W specializes in regulated industries (financial services, legal, healthcare), focusing on advanced document workflows (34:10).
  • “We’re really, really good at document… processing workflows where you might need a lot of escalation handling or context…” – Tarun (34:13)

• Market View:

  • It’s still “blue ocean” with vast opportunities—most firms have barely integrated AI, and the market is expanding (34:53).

5. Using AI in Operations and Delivery

• Internal Automation:

  • Heavy internal use of coding agents and automation to speed project delivery and operations.
  • “Time spent on coding was probably 80% generation, 20% review. It’s flipped that into 80% review, 20% generation… and the raw time has dropped by 10x.” – Tarun (31:05)

• Unique Internal System – “Atlas”:

  • An AI “operating system” that sits between people and organizational data.
  • Automates insights extraction from sales calls, proposal generation, team allocation, project planning, and ticketing—with minimal human intervention (37:25–39:49).
  • “I think we are approaching the point where our business could go from one sales call to a finished project with very, very little human in the loop.” – Tarun (39:01)

• Tech Stack:

  • Custom connectors pull data from tools like Fireflies (call recording), Notion (documentation), GitHub (code).
  • Orchestrated in their custom application built on top of Claude (AI model) (39:54–40:42).

6. Industry Perspective

• On SaaS Agencies Using Zapier:

  • Tarun believes outcomes are what matter; simplicity or custom build is moot if the business value is there.
  • “Who really cares about what it's made in… The thing that you should be buying is the outcome for the business.” – Tarun (24:37)

• On AI Adoption and DIY:

  • Broadly sees that most companies will eventually need internal expertise to integrate and manage AI.
  • “If they build the infrastructure correctly… over time… a lot of them can do it themselves.” – Tarun (35:40)

Notable Quotes & Memorable Moments

• On Charging for Outcomes Not Time:

  “We are constantly getting faster and better at what we do. So how do you charge, you know, more value-based rather than just like raw time spent? That’s tough.”
  — Tarun (02:04)

• On Early Customer Acquisition:

  “The story of our first customer… has been largely the same. It’s relationships and networking.”
  — Tarun (06:08)

• On Networking Style:

  “Most of the time at these networking events, I go into it with a lot of dread and I’m like, I don’t want to meet all these new people. I always end up having a good time, but it’s taxing for me. I’m much more of a one on one person.”
  — Tarun (13:59)

• On Why Events Matter:

  “For me, the goal was always just like… to cement ourselves as a real player in the Austin space… The BD stuff was honestly a bonus.”
  — Tarun (19:20)

• On Internal Automation:

  “I think we are approaching the point where our business could go from one sales call to a finished project with very, very little human in the loop. Basically just prompting and nudging.”
  — Tarun (39:01)

• On Naming Press W:

  “The three of us came from playing computer games… On a keyboard, WASD… W is always the forward key. So press W means to move forward.”
  — Tarun (40:44)

Timestamps for Major Segments

| Timestamp | Segment/Topic | |-----------|--------------------------------------------------------------------| | 00:33 | Revenue and business model overview | | 01:14 | Case study: Document automation for background check company | | 02:04 | Discussion on pricing models and value-based pricing | | 06:08 | First customer and early sales | | 07:30 | Team structure and selection | | 10:16 | Networking: events, hosting, and referrals | | 17:25 | Running high-touch industry events for business development | | 22:55 | Breaking into private equity via outreach and conferences | | 23:03 | Positioning the company: “AI engineering firm” | | 26:09 | Moving past “AI transformation” as a pitch | | 28:31 | How Press W leverages AI internally | | 34:10 | Specialization: Regulated industries & document workflows | | 37:25 | Internal automation and their AI “Atlas” system | | 40:44 | Origin of company name, "Press W" |

Conclusion

Tarun candidly unpacks how Press W built a thriving AI engineering services business by focusing on relationships, deep specialization, event-driven marketing, operational innovation, and a relentless drive to automate and optimize. The firm’s future is bright, with expansion in networking, events, and further internal automation, and a recognition that while the “blue ocean” won’t last forever, depth and focus will continue to set them apart.

Final Notable Quote:

“Press W means to move forward.”
— Tarun Thumala (40:44)

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