Engineering.

We build what we design. The same studio either side of the handoff, because handoffs are where most software gets worse. Web, mobile, platform, and the AI behind them. Built for environments where the architecture and the interface have to clear policy together.

i / iii — Design
ii / iii — Engineering
iii / iii — AI

Web Mobile Platform APIs AI Auth Design ↔ Eng

Engineering, for us, sits on the same side of the table as design. The team that draws the screens writes the code that ships them. That is not a scheduling decision. It is a structural one. Most software gets worse at the handoff, and we don't have one.

The work tends to start as a design conversation and become an engineering one without anybody noticing. By the time a client realises the prototype they liked is now a production system running in their environment, the team in the room is the same team that drew the first frame. Faster, because nothing waits to be re-explained.

We work where the engineering has to clear a real test. Regulated industries. Wealth, tax, banking, pharma, government. Architectures where a missed edge case is a compliance event, not a bug. The team is small and senior on purpose. We don't take on more work than we can hold the details of.

One studio, no handoff

Engineers who design. Designers who engineer.

Most product work breaks at the seam between design and code. We don't have one. Our engineers sit in the design reviews. Our designers read the pull requests. The Figma file and the repo are edited in the same week, by people who know each other's work.

It's why a Two Words engagement that begins as a design brief often ships as a production system, on the same team, without a re-onboarding tax in the middle.

0
Handoff between
design and engineering
1
Team, either side
of the brief
How the team is built
Workspace
Ujala — pushed component to staging
Tarun — Figma updated to match. Spacing token v2.
Chetan — review queued. Ship by Thursday.
design ↔ eng · same thread

Built for rooms that ship.

Selected engineering clients,
2019 to present.

AI brand management
prototype, engineered
Tax platform
front-end, in production
Investor portal &
AI agents, end-to-end
Pharma launch
web platform
B2B sales platform,
channel ecosystem

The shape of the work.

A list of what we tend to be hired to build. Most engagements span more than one of these. The lines between them are not real, but they are useful for talking about scope.

i.

Full-stack web applications

Production web platforms in React, Next.js, and Node. The kind that have to hold up to real users, real load, and real regulators on day one.

ii.

Mobile applications

Native and cross-platform builds, mostly in React Native and Swift. Customer-facing apps, internal tools, and the bits in between that nobody wants to maintain but everybody depends on.

iii.

Platform & data architecture

How a product is shaped underneath. Postgres, event flows, services, and the boring decisions that decide whether a platform can grow without being rewritten in two years.

iv.

APIs & enterprise integration

The wiring between systems that were never meant to talk to each other. CRMs, ERPs, brokerage rails, payment gateways, compliance tooling. Built so the user never sees the seams.

v.

AI implementation

Agents, RAG pipelines, intelligence layers. Built into the product, not bolted onto it. Most of what we ship sits behind the interface, where it should.

vi.

Front-end systems

Production-grade interface code. Component libraries, accessibility, internationalisation, performance. The work that turns a Figma file into something people can actually use.

vii.

Authentication & identity

SSO, OAuth, KYC, and the rest of the work that decides whether a regulated product can take on its first user. Most teams underestimate this. We treat it as foundational, not as a feature to bolt on.

viii.

The design ↔ engineering interface

Where designers and engineers share files, not handoffs. Token systems, code-mapped components, and the workflows that let the two sides of the work move in step instead of in turns.

ix.

Performance & accessibility

The two things that get cut first and matter most. We treat them as design problems, not engineering ones. They are why a product feels considered or careless.

x.

Deployment & delivery

Shipping discipline. CI, environments, observability, the boring scaffolding that lets a small team release on a Tuesday and sleep on a Wednesday.

The tools we ship in.

A working list, not a complete one. We pick stacks for the problem, not the brochure. These are what we reach for first.

Languages
TypeScript · Python · Swift
Web
React · Next.js · Node
Mobile
React Native · Swift
Data
Postgres · MongoDB
AI
Anthropic · Vector stores
Marketing platforms
Framer · Webflow
Cloud
Vercel · AWS
Tooling
GitHub · Linear · Figma
AI Agents · Investment Dashboard · Adventum Wealth

Putting agents inside
a regulated dashboard.

Client — Adventum Wealth, UK & India
Practice — Engineering + AI
Sector — Wealth · Regulated · Cross-border
Adventum Wealth — agents inside a regulated investment dashboard
— The problem

Adventum's analysts were spending most of their day pulling the same numbers from the same places. Portfolio performance, market context, document review, client correspondence. The question was not whether AI could help. It was where it could be trusted to, inside a wealth dashboard that clears two regulatory environments.

— Our approach

We built the agentic layer inside the investor dashboard rather than alongside it. The agents read portfolio and market context, surface decisions worth making, and stop at the line where a human has to approve. The architecture is designed so an auditor can read what the agent did, and why, after the fact.

Read the full case study

Notes on the practice.

Read all insights
Practice 8 min read

Why design and engineering shouldn't be two teams.

The case for collapsing the handoff. What changes when the people drawing the screens and the people shipping them are the same five people, and what most studios get wrong when they try.

Read the piece
Field notes · AI 14 min read

Building the AI layer inside an enterprise product.

What we learned shipping the AI Intelligence Layer at Klay Securities. The architecture choices, the regulatory edges, and where the line between agent and analyst actually sits.

Read the piece
Tools 9 min read

Framer at production scale, seriously.

When the design tool is also the deployment tool. Where Framer holds up against a custom build, where it doesn't, and the workflow we use to ship marketing platforms in weeks instead of quarters.

Read the piece

Three shapes of engagement.

Most projects start as one of these three and become something else. The first conversation is usually about which one to start with.

i.

Technical discovery.

A short, defined first engagement to scope the actual problem. Architecture review, build options, integration risks, and a written direction. Two to four weeks. Often where vague engineering briefs become workable ones.

Two to four weeks
ii.

Build.

Scoped delivery against a clear brief. Most of our product engineering sits here. Eight to twenty weeks, a small senior team, milestones agreed at the start. Designed and engineered on the same team.

Eight to twenty weeks
iii.

Embedded partner.

For platforms with a long horizon. We work alongside an in-house engineering team on retainer, attend the standups, hold the architecture reviews, ship in their environment. Most of our oldest engagements are this shape.

Twelve months & up

Some of our best projects
started with a two-line email.

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