AdTech · named client · one case, one stealable principle

An ad agency became the market’s aggregator — and two weeks of planning became three hours.

Five years inside the platform rebuild that turned the UK’s largest out-of-home agency into the market’s aggregator — a DSP with its own demand and supply sides. Campaign planning fell from two weeks to three hours, traders stopped overriding the machine, and the growth its 2017 investor called “exceptional” on exit is on the public record, with the next investor naming the technology as the reason it bought in.

2 wks → 3 hrscampaign planning, once traders bet on the model — measured inside the engagement
<5% → 100%of Talon’s UK bookings through Plato in one year — Talon’s CPO, on the record
6 systemsone connected platform — trading, audience data, creative management, play reporting, media-owner SaaS and white-label franchisor booking · three of them public as Plato, Ada, Atlas

AdTech · Talon Outdoor · Plato, Ada & Atlas · 2019–2025

Client
Talon Outdoor — the UK’s leading OOH agency
Role
Lead Product Designer
Team
50+ distributed agile team
Span
2019–2025 · one connected platform, five years
Planning
2 wks → 3 hrs
Status
Shipped · named client, publicly documented
The platforms are real
Documented by people with no stake in my career: a Digital Bulletin cover feature and film, launch coverage in the trade press, and Talon’s own live product pages. Every link on this page was verified working before it was printed. Jump to the public record →
My role is vouched for
The article is about Talon and its partnership with Sahaj — I appear nowhere in it, and that’s the point: the platforms’ reality doesn’t rest on my say-so. My part rests on the people who watched me do it — an engineering leader’s testimonial below, and a named reference available on request.
Two ledgers, never mixed
Numbers measured inside the engagement (planning time, media-value uplift) are mine to defend in an interview. Numbers on the public record (booking share, build speed, consideration lift) are Talon’s leaders speaking to the press — quoted and linked, never re-badged as mine. If a number here has no link, I’m its source and will stand behind it in the room.
The principle this case proves

Users don’t adopt the most accurate system. They adopt the one that lets them argue back.

The house belief was the industry’s belief: make the model more accurate and adoption will come. The model got more accurate. Adoption stayed at zero. This page is about the mechanism that actually moved it.

One principle · proven at £400M scale · wrong turn included

The stakes: what believing the opposite was costing Talon Outdoor.

Out of Home reaches 98% of the UK population every week, yet in 2019 buying it still worked the way it had for decades: fragmented inventory across hundreds of site owners, availability discovered by phone tag, proof-of-play arriving late and on trust. Talon — the UK’s leading independent OOH agency, handling media for Omnicom, Havas and others — decided to digitise not just its screens but, in the words of its Chief Transformation Officer Josko Grljevic, “the entire end-to-end business journey.”

That partnership was a relationship somebody had to hold, and for five years a large part of it ran through me: sitting with Talon’s commercial and product leadership, with media owners, and with the agencies buying through the platform, then coming back and deciding what got built. Not a brief handed down and drawn up — the conversation and the interface were the same job. Talon’s own account of why they chose Sahaj as the engineering partner is on the record: “We believed they had the hardcore engineering skills… that partnership has built over the last three years to such an extent where we don’t treat them as an external company, they’re an extension of our business.” I was on that team for five years — the product-definition and design side of a 50+ person distributed effort.

What “transformed” means, in the customer’s own words. The cleanest way to show the before/after is the conversations an advertiser could suddenly have:

The advertiser asksBeforeAfter
How soon can my campaign start?“At least two weeks — to search, book, and dispatch adverts.”“Upload your ads; we can start the next hour.”
Can you reach pub-goers, Fridays 9 PM–1 AM?“Impossible to cherry-pick media like that.”“The data platform recommends the sites near that audience.”
Why these sites?“Trust us — we’ve done this for years.”“Here’s the data: unique views and ranking, per site.”
Can I cancel?“A few days to roll everything back.”“Pause or cancel in one click.”
Change my ad when it rains?“Not possible.”“Yes — creative can switch on live conditions.”
Can I monitor delivery?“You’ll get a site list after the campaign.”“Views and demographics per site, as it runs.”

The ground truth: what every actor in this market actually needed.

I was the technical side of the room where the business was decided. Before anything was designed I mapped the market’s ecosystem: buyers from mom-and-pop stores to enterprises and franchisors; sellers from individual site owners to media and creative agencies, DSPs, SSPs and exchanges. Competitor teardown across the category — Google Ads, Facebook Ads, AdQuick, Hivestack, Place Exchange, Campsite (since acquired by Broadsign), among others. Then research by actor: business-model canvases, personas and journey maps with the sales team, campaign planners, media sellers, creative agencies, and the admins who keep it running. The pain-point ledgers below are drawn directly from those sessions — their words became the feature map.

Planners said
“Clients change the brief constantly; prices and availability drift under us.” · “Cherry-picking good frames in unknown areas is guesswork.” · “A 500-site campaign is unmanageable across markets.” · “Sellers don’t respond with rates in time.”
So the platform
Holds briefs, rates and bookings in one place · scores every site against audiences and recommends · runs amendment and cancellation as first-class workflows, not favours · puts sellers on notified, contract-bound response loops · generates per-site delivery reports clients can hold.
Sellers said
“Every unsold week is a permanent loss.” · “Managing 500 to 50,000 sites — prices, availability, contracts — needs a sales team we can’t afford.” · “Creatives arrive at the last moment.”
So the platform
Gave them inventory management free — heat maps of unsold media, forecasts, automated sales — and in return their inventory came onto the aggregator. That trade is the supply-side engine: the free SaaS wasn’t generosity, it was how supply showed up without a single phone call.

Why aggregation was hard enough to be worth five years. Hundreds of site owners, each with their own inventory format — some had APIs, some shared files, some just answered the phone. Sizes, renting plans and per-site rules that resist standardisation; advertising law that changes by region; availability that shifts while you’re planning against it. And the constraint that shaped my work most: adoption. Advertisers were used to choosing sites by hand — trust in automated delivery doesn’t come with the software, it has to be designed for. That last constraint is the whole next section.

The test began with me failing it: two weeks spent sharpening a model that was already sharp.

The recommendation engine outperformed the buyers, visibly. Adoption sat near zero anyway. I got the diagnosis wrong first — I spent two weeks trying to make the model smarter before admitting it was the wrong suspect. The traders weren’t being stubborn; they were being rational. A bare recommendation asks for faith, and traders deal in collateral.

The model didn’t need to be more right — it needed to give the traders something to bet on.

So the bet became: move adoption without touching the model at all.

THE DESIGN MOVE The model didn’t change. What it handed over did. WHAT THE ENGINE PRODUCED A ranked list of billboards correct, and impossible to defend in a room no case, no cost of being wrong, nothing to adjust traders read it, then played their hunches the screen between WHAT THE TRADER RECEIVED A full campaign plan · reach, budget, formats, locations · the KPIs it will be judged on, attached · the reasoning on the card — which audiences, which environments, what trade-off Customise in one click disagreement is a control, not a dead end Every edit logged and fed into next week’s recommendations fighting the model turns into coaching it No confidence score ever reached a trader. The unit of trust was a plan they could argue with.
The model didn’t change — what it handed the trader did
The loop IS the principle: disagreement is fuel, not friction. A system that punishes pushback never earns a second bet.
Plate 03 · Campaign planning — reconstruction · all names and figures synthetic Redrawn from memory at production fidelity. A plan instead of a list, the KPIs on the card, and customisation the model learns from — the screen that turned right-and-ignored into right-and-acted-on.

The whole case fits in one card. Pick a planning priority and watch the plan re-shape — then open the reasoning, then customise it:

Reconstruction — anonymised, rebuilt from memory for illustration · client under NDA · figures synthetic
Recommended plan · Campaign 7

Evening CTV + roadside D6 cluster — weighted for maximum audience.

reach 1.8M est. ROI 2.1x spend 100%
Audience fit — movement patterns near sites
30-day site performance
Inventory price trend

Customised — logged · feeds next week’s recommendations

Keyboard-friendly · nothing you click here leaves the page
The strongest objection, kept in

“Letting traders reshape the machine’s plan undermines it.” That was the room’s position, and it is the reasonable one. It is also backwards: an unchallengeable system doesn’t read as authoritative — it reads as unaccountable, and experts decline to stake their name on the unaccountable. The objection dissolved the first time a buyer watched their own override sharpen the following week’s plan.

I built three things. A recommendation that arrived as a plan, not a pick. KPIs on the plan itself, so the ROI case was visible before anyone committed budget. And customisation that took one click — every adjustment logged and fed into next week’s recommendations. The first time a buyer watched their own pushback sharpen the following week’s calls, the relationship flipped from fighting the model to coaching it.

Adoption unlocked the bigger reframe: buying moments, not spots. The old brief named a place — “a billboard on Oxford Street for a month.” Once the plans were believed, the brief could name an audience and a moment — "reach party-lovers on Friday nights, 9 PM to 1 AM" — and the system chose the sites. Talon’s product strategy director described the same capability to Digital Bulletin: outcome-based measurement, omnichannel planning, behavioural audiences.

WHAT TRUST UNLOCKED The brief itself changed shape. BEFORE — THE BRIEF NAMED A PLACE “A billboard on Oxford Street for a month.” the buyer picked the sites; the system priced them once the plans were believed AFTER — THE BRIEF NAMED AN AUDIENCE AND A MOMENT “Reach party-lovers on Friday nights, 9 PM to 1 AM.” the system chose the sites; the buyer judged the case Adoption was not the end of the work — it was the thing that made a better question askable.
What adoption unlocked — a brief that names an audience, not an address

The principle, compounding through six systems — each named, each public.

The structural play: a media agency became the market’s aggregator. The platform positioned Talon as a DSP with its own demand side — advertisers and agencies planning in one system — and its own supply side, where media owners’ inventory flowed in through free software instead of phone calls. Six systems, one connected platform — the trading floor, the audience intelligence, the creative management, the play reporting, the supply-side SaaS, and a white-label booking product franchisors brand as their own — plus PlanIt on top of the data they generated. That is what replaced the Excel sheets — not a tool bolted onto the old market, but the market, re-plumbed.

Plato — the automated trading platform: explore, plan, check availability, reserve and trade paper and digital inventory across markets, in one system. Talon’s Chief Product Officer Amy Horton, on the record: bookings through Plato went from under 5% to 100% of UK bookings in about a year, and an availability search that took three days — “48 sheets in Sheffield in June” — now takes 30 seconds. Grljevic: the MVP was built “in four months, under immense pressure.” Plato on Talon’s site ↗

Ada — the data-management platform: billions of audience data points profiling how people move, so billboards could be planned against audiences rather than addresses. McDonald’s was a launch partner; Google a regular user. Talon’s public claim for Ada-optimised campaigns: +53% consideration versus standard OOH distribution. Built, per the article, “a complete data management platform in a year, while transitioning the engineering from an incumbent.” Talon × Ada on YouTube ↗

Atlas — the programmatic buying platform, launched September 2020: audience-led, transparent, clients pay only for confirmed impressions. Grljevic’s build story is the one he showcased: existing Plato and Ada services extended into “a brand new DOOH programmatic platform in six months… we put revenue through it a month later — that is unheard of.” It has since been extended to the US market with Place Exchange. Atlas launch, ExchangeWire ↗

PlanIt — and one more product on top of the six, a client project built on the platform’s own footfall data (London Underground + National Rail): a planning tool scoring places by quietness, 2021, now deprecated but archived. Honest scale: ~1,000 monthly visitors, 250 active users. PlanIt, archived ↗

The creative management solution — the layer that made a campaign’s artwork behave like the plan did: versions, formats and the rules for swapping them, so a brief that named a moment could carry creative that matched the moment. Without it, audience-and-time buying is a promise the artwork can’t keep.

The play-reporting system — proof of play, the part of out-of-home that used to arrive late, manually and on trust. Turning it into reporting is what let the platform sell on confirmed impressions rather than booked ones — the accountability the whole aggregator claim rests on.

And a sixth system, on the other side of the demand curve — a white-label booking product a franchisor brands as its own. A chain negotiates billboard inventory once, centrally; its franchisees then browse those sites on a map, book them themselves, and raise a complaint or an enquiry without a phone call in the loop. Closer to Uber than to media buying, and aimed at a third audience the platform had never served directly: not advertisers, not media owners, but the hundreds of local operators underneath a national brand. One negotiation, many self-serve buyers — the demand-side counterpart to giving media owners their software free.

And the supply side — inventory software for media owners, free: media owners list what they have, and supply comes to the platform. That is the quiet product-led-growth mechanism under the whole aggregator.

One design system made six products one platform. I introduced a new design system across the line — a modern face, but the point was family resemblance: Plato, Ada and Atlas stopped reading as separate tools someone had bolted together and started reading as rooms of the same building. For a company selling an integrated platform, looking integrated is not cosmetics; it is the claim, made visible.

Then the design system learned to build itself. I packaged its rules as a Claude plugin — an installable artifact developers add to their own environment, not a prompt I kept re-pasting — and shipped it across the engineering org; a second plugin followed at Sahaj. Front-end work now starts on-system instead of drifting from it. Builds got faster and more precise, with fewer UI bugs, and developers began prototyping with Claude themselves rather than queueing for a designer. The same argue-back principle, one level down: give the machine your rules, and it hands you back speed. Both plugins sit behind a client wall, so this one travels as a claim rather than an artifact — and without numbers, because I never measured it.

Falsifiable evidence — my numbers and theirs, kept apart on purpose.

Measured inside the engagement — mine to defend
Campaign planning 2 wks → 3 hrs once traders bet on the recommendations · £69,000 average media-value uplift per client · 45% reduction in time and effort to plan and book · 3× purchase-intent and 70% audience uplift vs traditional bookings. Not in the article; measured in the work; I’ll defend each in the room.
On the public record — Talon’s leaders, to the press
UK bookings through Plato <5% → 100% in a year · availability search 3 days → 30 seconds · Ada-optimised campaigns +53% consideration · Plato MVP in 4 months, the data platform in a year, Atlas in 6 months with revenue a month later · “the things that would normally take us 12–18 months to build were built in six months.”
The investors’ ledger — the win a standup aims for
Mayfair Equity Partners backed Talon in 2017; in July 2022 it exited fully to Equistone, calling five years of “exceptional growth”: 200+ employees across seven cities, four acquisitions, the largest global OOH planning-and-buying network across 100 markets, Apple and McDonald’s on the client list. Equistone’s stated reason for buying in: to “accelerate the investment into talent and technology” — the platforms this case describes. Deal terms undisclosed, so no multiple is claimed here; the receipts are the exit itself, and who bought, and why.
Not mine
The bidding model and the 50M-bids/hour infrastructure — engineering’s win, and they own it. The platform build speed Grljevic praises is the whole 50+ team’s, Sahaj’s engineers above all.

Where the principle breaks.

Argue-back needs an accountable expert on the other side — a trader with a number against their name, an analyst signing a memo. Casual users generate noise, not coaching; a feedback loop trained on noise degrades. And the principle cannot rescue a weak model: collateral only works when the house can pay. If either condition fails in your product, this case argues against copying it — which is exactly what a principle worth stealing should do.

The public record — watch it, read it, click it.

Start with the film: Digital Bulletin’s cover-story documentary, with Talon’s transformation chief, product director and CPO on camera about the platforms this case describes.

The film · Digital Bulletin / NODE · 2021“Talon Outdoor: An Out of Home evolution” — the client, on camera, about the transformation. Not produced by me; that’s what makes it evidence. Read the Digital Bulletin cover feature ↗
Atlas, as sold today ↗Talon’s live product page — planner, dataset mapping and studio interface shots. The current generation of the platform this case seeded. Plato, as sold today ↗The trading platform’s live page — explore, plan, reserve and trade, still the end-to-end system. The full technology suite ↗Ada, Plato and Atlas on one page, in the client’s own words.

No public walkthrough video of Plato, Ada or Atlas exists — verified again before writing this line, so none is faked here. The product-and-design evidence is above: the campaign-planner reconstruction, rebuilt from memory at production fidelity with synthetic figures, and interactive so you can work the screen a trader worked.

The rest of the record — every link verified working on 13 August 2026.

Arpit consistently demonstrated exceptional speed, creativity, and attention to detail… What stood out most was his ability to present multiple design options along with clear pros and cons, which made it much easier for different stakeholders to make informed decisions and align quickly. I highly recommend Arpit and would be delighted to work with him again.
Sanjesh AnandaSoftware Engineering Leader · worked with Arpit on this platform

Where the line sits

The article is Talon’s story and Sahaj’s engineering — it does not name me, and I won’t pretend otherwise. What it proves is that the platforms, the timelines and the outcomes I describe are real and were worth a magazine cover. Who held the product-definition and design pen across those five years is a question for the people who were in the room — and a named reference for this work is available on request.

Next case study → Send me the role ↗