Extending Cognition: From Knowledge Hoarding to Knowledge Leverage

We’re back after a short hiatus. And with a new format.

Over the past few months, Just Curious has evolved from long-form summaries into something sharper and more personal. Going forward, each issue will pair insights and excerpts from a recent interview with five focused questions that bring out the expert’s practical perspective.

We think this structure delivers a better experience, giving you both the big ideas behind how leaders are using AI and the specific lessons you can apply in your own work.

This week, we’re spotlighting Nitesh Pant, co-founder of DevDash Labs, a company helping middle-market companies and consulting firms turn their collective knowledge into leverage through AI. 

Nitesh’s approach helps these organizations automate repetitive research, reuse institutional knowledge, and scale their intellectual property. And in doing so, spend less time recreating work.

What to expect:

• How DevDash Labs is helping middle market companies and consulting firms turn past work into reusable knowledge assets

• Why every firm should run the “intern test” to identify quick-win automation opportunities

• The difference between automating research and losing the human layer of insight

• A case study: how a DTC eyewear brand used AI to cut Rx verification time by 95% and grow sales

• Why you’ll learn more from two weeks of AI experiments than six months of “AI strategy”

• What it means to build a firm-specific AI “brain” that actually thinks like your consultants

Want to connect with Nitesh? Just reply to this message, and I’ll facilitate

💬 Expert Q&A: Nitesh Pant, Co-Founder of DevDash Labs

(Each week, we’ll be asking applied AI experts like Nitesh a rotating series of questions to surface unique perspectives, lessons, and insights. His answers and my own thoughts follow.)

1. Describe who you are, what you do, who you do it for, and what makes your approach unique.

“I’m Nitesh Pant, co-founder of DevDash Labs, where we're transforming how strategy consulting firms handle knowledge work through AI. We work specifically with consulting firms to automate the research-heavy, manual processes that consume massive amounts of their consultants' time. What makes our approach unique is that we're not just building generic AI tools. We're creating specialized systems like Alkemy that understand the specific workflows consultants actually use, from M&A due diligence to company research and qualification.”

Stu's Thoughts:
When I asked Nitesh how DevDash started, he told me, “I got tired of seeing LinkedIn experts selling impossible promises to business owners who wanted real solutions.” That frustration became a mission. Today, DevDash is building research-backed systems that help companies, including consulting and professional services firms, eliminate repetitive knowledge work and focus on insight generation. As Nitesh puts it, “We build measurable solutions, not marketing decks.”

2. What problem are you most focused on solving right now, and how are you anticipating solving this problem with AI?

“We’re solving the massive knowledge management problem in consulting firms. Right now, when a consultant needs to research and qualify a company for an M&A transaction, they’re manually sifting through countless documents, building profiles from scratch, and recreating analysis that’s probably been done before.

With Alkemy, we're building an operating system that can instantly research, analyze, and qualify companies against specific criteria, while also making all of a firm's past work searchable and reusable. Instead of starting from zero every time, consultants can leverage their firm's entire knowledge base.”

Stu's Thoughts:
In our interview, Nitesh described Alkemy as a “custom brain” for consulting firms, an AI that not only retrieves knowledge but understands how the firm thinks. “You can’t just talk to ChatGPT and expect it to see the world from your firm’s viewpoint,” he said. “We train our agents on your actual frameworks and past projects, so they think like your consultants would.” The result? A system that makes firm knowledge accessible, searchable, and reusable at scale.

3. If you had to pick one process every company should automate with AI today, what would it be?

“After the obvious wins like meeting transcripts, I'd say handling 80% of their research needs. Most knowledge work involves gathering information, synthesizing it, and making it actionable. AI can now handle the heavy lifting of research while humans focus on strategic insights and client relationships.”

Stu's Thoughts:
In a recent project, Nitesh’s team applied this same principle to a completely different industry — eyewear — helping a DTC brand automate its prescription verification workflow. The impact was significant: 95% automation, 10+ hours of contractor time saved per week, and hundreds of thousands of additional annual revenue from faster customer response times.

 👉 Watch the full interview and case study →

4. Where do companies waste the most effort because they’re not using AI yet?

“They spend months planning their “AI strategy,” but never actually start experimenting. Companies form committees, hire consultants, and debate which enterprise AI platform to buy, while competitors are already automating real work with simple tools.

The best ROI is still just getting everyone a company-wide ChatGPT license, asking your team what repetitive tasks they’re doing, and then systematically automating those workflows. You learn more about AI’s real potential in your business from two weeks of experimentation than from six months of strategy sessions.”

Stu's Thoughts:
Nitesh’s advice echoes one of his favorite workshop exercises: the “intern test.”

“If you can teach an intern to do it in a few hours,” he says, “you can probably automate it.” 

At DevDash’s AI Discovery Workshops, teams list all the tasks that fit this pattern and build quick prototypes to test automation potential. The takeaway: stop planning and start experimenting. Your best learning comes from building, not whiteboarding.

5. What’s one way AI has made your personal work life easier or better?

“I’m a power user of LLMs. Google AI Studio's 1-million-token context window has been game-changing. I can dump entire project files, research reports, and documentation into a single conversation and ask complex questions that span all of that information. Instead of manually cross-referencing multiple documents, I can ask, “What are the key differences between these three companies’ approaches to market expansion?” and get synthesized insights immediately.”

Stu's Thoughts:
For Nitesh, this is just as much about extending cognition as it is about efficiency. “AI lets me think across my entire knowledge base at once,” he told me. It’s the same principle behind Alkemy: if you can compress and contextualize knowledge instantly, you can make faster, better decisions.

Turning These Insights into Leverage

1. Run an “Intern Test.” If a task could be done by an intern after a week of training, it’s a candidate for automation.
   

2. Build From Your IP. Don’t start with tools. Start with what your firm already knows and does best.
   

3. Prototype, Don’t Plan. Two weeks of experiments teach you more about AI’s ROI than six months of strategy decks.

Connecting with Nitesh

Here are a couple of options:

• Respond to this newsletter, and we’ll help facilitate (it’s free)

• Check out Nitesh’s Just Curious profile and connect with him (also, free)

• Go find Nitesh on the internet and connect (also free, but a lot more work)