I’m Neeraj, founder of Lemon Juice Labs. We’re building four AI products, and yes, the homepage has a black cat in a purple blazer and sneakers. The cat handles brand confidence. I handle the harder question: does this actually help someone?
That question connects everything we’re working on: market intelligence, quantum readiness, revenue intelligence, and health information. Different domains. Plenty of ways to produce a convincing answer that turns out to be useless.
The four products we’re building at Lemon Juice Labs
LemonJuiceLabs AI: making markets easier to investigate. The focus is market research, news, and insights for curious retail investors. My interest is in helping people understand what changed, what evidence supports it, and what deserves a closer look. A fluent paragraph isn’t much help if the underlying information is stale.
QCAI: turning quantum curiosity into readiness. Quantum computing is fascinating. “Quantum changes everything” is a fairly unhelpful migration plan. The direction here is quantum readiness and post-quantum migration: bringing an enormous technical topic closer to concrete questions about systems, dependencies, and priorities.
FunnelUp AI: helping revenue teams find the useful signal. Sales and marketing teams already have dashboards. The opportunity is revenue intelligence and productivity: helping a person understand a situation well enough to choose their next action. Generating more activity is easy; making that activity worth doing is the interesting problem.
BetterHealth AI: making health information easier to navigate. The focus is information and knowledge tools. The boundaries matter: this is informational, not medical advice. In this domain, clear sources, uncertainty, and restraint belong in the product experience.
The model call is the easy part
Here’s the technical work I want this blog to get into. These are engineering questions and design standards—not a claim that every product already solves them.
Retrieval: What information should reach the model? A useful pipeline has to select relevant evidence, preserve its source, and account for freshness. For a market question, a document’s publication date and the date of the event it describes can tell very different stories.
Evaluation: How would we know an update made the product worse? I want test cases built around real tasks, including missing evidence, conflicting sources, and questions the system should decline to answer. “Looks good to me” is a fragile release process.
Permissions: If a product connects to business data, retrieval must respect the user’s access. A model should never become a creative workaround for permissions. This needs enforcement in the application and data layers.
Cost and latency: Every extra model call needs a job. Routing simpler work to smaller models, caching appropriate results, and keeping context focused are useful options. The right tradeoff depends on measured quality and how long a person is willing to wait.
Why put these ideas in one lab?
Because the questions travel. What counts as evidence? When should a system admit it doesn’t know? Where does automation save effort, and where should a human make the decision?
The answers won’t be identical across products. A stale market update and an unsupported health claim require different treatment. Shared engineering ideas still need domain-specific judgment.
What I’ll write here
Expect technical deep dives, product experiments, founder decisions, and opinions about where technology is heading. I want to explain tradeoffs with enough detail that you can disagree with them—and occasionally build something from them.
At Lemon Juice Labs, I want each technical post to show its working: the question, the approach, the constraints, and what I would test next. A retrieval experiment should explain how relevance is judged. A model comparison should include latency and cost alongside quality. That’s the kind of detail I want to read, so it’s the standard I’m setting here. There’s room for fun, too. We can take the work seriously while giving the mascot an unnecessarily good wardrobe.
— Neeraj Sabharwal
Founder, Lemon Juice Labs AI, Inc.