Blog & Insights

The market for AI development services has exploded. Here's how to evaluate partners and identify who actually builds production AI systems versus who just talks about it.

Design outsourcing has matured significantly. Here's a current, practical guide to how to do it well — what to look for, how to structure engagements, and how to avoid the common failures.

How we think about building AI products — the principles, the process, and the mindset that guides every engagement we take on.

Agentic AI systems don't just respond — they plan, act, and iterate. Here's what that means in practice and how to build them.

When does it make sense to hire a UI/UX agency versus building an in-house design team? Here's a practical breakdown of when agencies deliver more value — and when they don't.

Vibe coding gets you to a demo fast. It rarely gets you to production. Here's what breaks between the two — and how to fix it.

Both approaches adapt LLMs to your domain, but they solve different problems. Here's how to choose.

Offshore development gets a bad reputation. Here's why the model works differently when your partner specialises in AI products and operates with product-first thinking.

India generates enormous amounts of healthcare data, but most of it is inaccessible, unusable, or simply lost. UX design has an underappreciated role in solving this problem.

A behind-the-scenes look at our week-by-week process for taking an AI product from idea to production in one sprint.

Fine-tuning is often the wrong answer, and RAG is often the right one. But when fine-tuning is the right tool, here's how to do it well for enterprise applications.

From MVP to Series A and beyond — how we structure our engagements to grow with the companies we work with.

Vibes-based testing breaks in production. Here's the evaluation framework we use before deploying any LLM-powered feature.

AI in Indian healthcare has enormous potential and specific constraints. Here's the practical starting point for healthcare organisations trying to figure out where to begin.

The UK health tech market has specific regulatory, NHS integration, and user trust dynamics that differ from the US and other markets. Here's what founders need to know.

Design is often treated as polish. For B2C startups, it's a core driver of whether users stay or leave. Here's how we approach design for pre-PMF products.

Voice AI in demos feels like magic. In production, it's a systems engineering problem. Here's what we've learned.

Building AI features into healthcare products is fundamentally different from building AI features elsewhere. Here's the roadmap we've developed from experience.

Women's health is not one-size-fits-all. Here's how we approach AI product design for FemTech — with bioindividuality, not averages, at the centre.

PMF isn't a feeling — it's a pattern of signals. Here are the 10 we watch when iterating toward product-market fit.

The best AI products in 2025 don't use one model. They orchestrate multiple models for different tasks. Here's how to think about model composition in production AI systems.

Designing for AI is different from designing for deterministic software. These principles guide how we approach AX design.

Internal teams get too close to the product to see it clearly. Here's what a UX audit from an external partner surfaces — and why it's especially high-stakes in health tech.

FemTech has a product-market fit problem that isn't talked about enough. Here's why standard PMF frameworks don't work for women's health, and what to do instead.

Llama, Mistral, Qwen — open models have caught up on many tasks. Here's when to use them and how to deploy them.

AI coding tools have genuinely changed what's possible for small engineering teams. But they've also created new failure modes that weren't possible before.

After building 50+ AI products, here's the stack we reach for by default — and why each choice matters.

Health-tech users don't behave like e-commerce users. Here's what the research tells us about how people search for, evaluate, and decide on health products online.

The build-vs-hire decision is one of the most consequential choices for early-stage startups. Here's our framework.

Most MVPs aren't actually minimum or viable. They're overbuilt, under-validated, and launched into markets that haven't been tested. Here's how to avoid the trap.

The most expensive mistake in product development happens before a single line of code is written. Here's how to get the start right.

Getting a CV model to 90% accuracy on a benchmark is easy. Deploying it reliably in production is not.

Every major technology shift produces winners and laggards. AI transformation is the defining shift of this decade — here's what that means for how companies need to act now.

Seat-based SaaS pricing breaks for AI products. Here are 5 pricing models we've seen work for AI-native products.

A design system is expensive to build and cheap to skip — until it isn't. Here's how to think about the timing.

Most enterprise AI pilots succeed technically and fail organizationally. Here's the pattern we've seen and how to break it.

Server components, streaming, edge functions — Next.js has become the default for AI applications. Here's why we agree.

The model is the easy part. Getting clean, labeled, versioned data to train it is where most AI projects stall.

The instinct to delegate product decisions early is understandable. It's also often wrong.

LLM costs compound fast. Here are the techniques we use to reduce inference costs without degrading quality.

Sprint rituals often become overhead. Here's a leaner approach that keeps velocity high and meetings low.

Orchestrating multiple AI agents sounds powerful. In practice, it requires careful design to avoid cascading failures.

Standard usability testing doesn't capture the unique challenges of AI interfaces. Here's how to adapt your research process.

Raising a seed round is a milestone. Navigating the shift in how you build product afterward is the real challenge.

pgvector, Pinecone, Weaviate, Qdrant — the vector DB landscape is crowded. Here's how to choose.

AI engineering is a new discipline. The standard software engineering interview process doesn't find the best candidates.

Building in public feels risky. In practice, it's one of the highest-ROI marketing channels for technical founders.