For a world where software can act, I design agentic products and systems for high-stakes industries.
Agentic products, regulated industries.
From first build to funded.
Plus the operating system they run on.
For industry-agnostic multi-agent experience.
Retail to regulated AI. That range is why I can make the call in week one instead of exploring for a quarter.
I build the function, not just the file: hiring, systems, and the standard that holds after I stop touching it.
Four gates before an agent acts on your authority
Four enterprise products on one staircase
A co-pilot that moves with the conversation
Formally trained, then applied daily on an agentic platform in energy, aviation, government and supply chain. The method is written up as Defensible Autonomy.
I work backwards from the outcome a business needs, then report on it afterwards. The index above is that record.
Head of Design twice over, and Lead Product Designer through six years of transformation at a 32M-customer business.
Ten stops, art direction in Manchester to agentic systems for regulated enterprise. Press play to walk it, or open any stop for the detail.
2026 – NOW
Orbitron AI
Design lead for Nova OS, an agentic operating system for regulated enterprise, owned end to end.
2025 – 2026
Desertcart
Cross-border commerce rebuilt across 160+ markets in six months, so it reads as local wherever it lands.
2018 – 2024
Marks & Spencer
Six years of digital transformation for 32M+ customers. I led Sparks from zero, including the first session.
2016 – 2018
Neilson Financial Services
Regulated financial products, where the constraint is the brief rather than an obstacle to it.
2014 – 2016
Ladbrokes Coral
High-volume consumer platforms, where I saw how fast a small interface decision compounds at scale.
2012 – 2014
Sirenna Learning
Manchester. Where it starts.
Founding
Lifeforce
Longevity venture backed by Tony Robbins, Peter Diamandis and Serena Williams. I set the foundation.
Founding
FOUND
Switzerland’s first three-sided AI recruitment marketplace. Time to hire fell from 100 days to 10.
Fractional
Rechitta
AI voice co-pilot for real estate. Voice that moves with the conversation, interfaces that act.
Advisory
Ventures
Prop-tech, DeFi, fintech and e-commerce, mostly before the product has proved it deserves to exist.
Confidential
The Agentic Design Framework: six pillars, one sentence per agent, and trust you can grade. Day-one autonomy fails for a reason that has nothing to do with model quality: nobody has agreed what the agent is allowed to do, or how anyone would know it went wrong.
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Don’t take my word for it. Here is what the people I have shipped with have to say.
“She built an entire design system from scratch, collaborated seamlessly with developers, and consistently demonstrated flexibility to find the best solutions for both users and the business. Aishwarya is a rare talent, highly skilled, reliable, and truly user-centric.”
Cemre Akkaya
Vice President Marketing
I've spent the last six months embedded inside enterprise clients across supply chain, aviation, and compliance, designing agentic AI experiences in the room where the product meets the people who actually have to use it.
For a long time, there wasn't a name for what I do. Then “Forward Deployed Designer” started showing up in my feed. Having spent six months actually doing this work, I want to share my perspective — not theory, not prediction, but the view from inside the room.
The Forward Deployed Engineer is having its moment. Job postings grew by more than 800% through 2025. Every major AI company is now running some version of the Palantir playbook: embed engineers inside enterprise clients, own the deployment problem, make the model work in the real world. The logic holds. MIT confirmed what practitioners already knew: 95% of enterprise AI pilots never scale. Not because the models fail, but because integration, workflow redesign, and the gap between demo and deployment do.
FDEs close a real gap. They make the agent technically possible.
Getting the integration right is an engineering problem. Getting a compliance analyst or a supply chain officer to genuinely rely on an agent in their daily job is a human problem. That's where the Forward Deployed Designer comes in.
The supply chain officer didn't want a chat interface. Her job wasn't a conversation, it was a series of decisions she made every morning, informed by options she'd learned to read at a glance. Going chat-first added work, not removed it. She'd have had to keep asking for every option instead of seeing them. The pushback was specific: show me more upfront, why this recommendation, what else was scanned, and the reasoning behind all of it, so trust in the agent's capability could actually start building.
So we went back. We're now testing a different approach: what if the interface showed her the agent had already done the job, and explained why its top recommendation made the cut? Not a chat, but a view — her options surfaced and ranked, the reasoning visible right alongside them. The same cognitive work she did every morning, done in advance, waiting for her confirmation.
It's early, but the direction came from listening to her, not from assuming that what works for a developer tool works for a procurement workflow.
A second deployment is testing this further. The compliance analysts I'm working with process upwards of 1,000 reports a day. Their job is operational: see the state of a queue, understand what's changed, identify high-priority exceptions, and act on them in sequence, at volume, with accountability attached to every decision. Based on their mental model and the nature of the work, a conversation-led interface feels like it would land the way it would if you suggested reading your emails by asking a chatbot to describe each one. These are hypotheses, not conclusions. We're building an iterative approach, bringing in the actual users so we hear their day-to-day problems directly, and solve for those using the technology.
This tracks with where Anthropic has pointed publicly. Their stated long-term direction is that chat stays the universal fallback, while models generate dynamic UI on the fly when a UI serves the task better than conversation does. The key word is fallback — not default, not primary.
There's a widespread assumption in enterprise AI that agentic means chat, that the interface question has already been answered by the products that most visibly succeeded with that paradigm. Chat works for tools like Claude.ai or Cursor because the job they serve is fundamentally linguistic: you produce something by describing it, and the interface and the output share the same medium. That's the structurally correct choice for that task. But most enterprise work isn't linguistic in nature. A supply chain officer makes sequential decisions across structured data. A compliance analyst monitors a large, changing queue. These jobs aren't conversations, and treating them as conversations isn't an oversight. It's a category error.
The long-term vision for agentic AI is compelling: less information, fewer screens, the operator simply takes the action. But reaching that vision means building through it, not skipping to it. Users need to see the agent doing the job they recognize before they'll trust it with the job they can't see. Gartner predicts nearly 40% of agentic AI projects will be cancelled by 2027, and a significant part of that will be adoption failure rather than technical failure — the cost of products that skipped the trust-building and jumped straight to the end state.
Every step toward less interface has to be earned. Interface choices in enterprise agentic AI need to come from the user's mental model of their job, not get inherited from consumer products that solved a different problem. The question isn't whether your agent is capable. It's whether the surface you've built matches the way your user actually thinks about their work. We're still figuring that out. The supply chain officer and the analysts are telling us what's wrong. We're listening.
Human in the loop is often treated as a compliance checkbox — a confirm button before the agent acts. That's not what it is.
Designed well, human in the loop is the trust architecture. It tells users: the agent knows your job, it's already done the thinking, and you're in full control of every decision. Not because we don't trust the agent. Because adoption depends on users feeling confident, not just informed.
The supply chain officer doesn't need less information. She needs the right information, presented in a way that reflects how she already thinks, with clear sight of the agent's reasoning and full control over the outcome. When that's right, the confirmation step isn't friction. It's what makes the product feel like it was built for her.
That surface doesn't design itself. It comes from someone who spent time understanding how she works before anyone started building.
Design in this context isn't producing artefacts. It's not wireframes handed across a wall. It's not a Figma file attached to a Jira ticket.
By the time a Forward Deployed Designer gets involved, a prototype already exists. Engineers have built something. The client has expectations. The product is taking shape. The value I bring isn't starting from scratch — it's seeing what's wrong before users do: looking at a live experience and knowing whether the trust arc is right, whether the interface choice fits the job, whether accountability actually lives in the surface or is just implied somewhere in the backend.
The FDE makes the agent technically possible. The FDD makes it something people will actually use.
Right now, most enterprise AI deployments have one and are missing the other. The model works. The integration holds. The experience fails — not dramatically, but quietly: adoption numbers that look worse every quarter, users who were politely resistant from day one, contracts that don't renew.
In regulated enterprise, where accountability is real and the tolerance for ambiguity is essentially zero, trust isn't a feature you add after the product works. It is the product. And designing it takes someone whose job is specifically to understand users deeply enough to know when the agent is earning that trust, and when it's quietly losing it.