Target outcomes

What we're building toward.

We're a small, new team, so we're not going to show you a wall of logos we don't have. Instead: the outcomes we design for, the architecture behind each one, and exactly how we'd measure whether we hit it — so you can judge the engineering rather than the marketing.

A long metal scaffold grid with one bay outlined in blue.

Read this first

These are targets, not results.

Nothing below is a delivered engagement, and we're not going to dress it up as one. Each scenario is the architecture we'd build, the number we'd aim at, and the method we'd use to check it — modelled from the unit costs we publish on our methodology page, where you can pressure-test every assumption against your own operation. When we do have client results, they'll appear here with the client's name and their sign-off, or not at all.

−40%

Target, not a result

Industry: Call CentersEstimate: 8 weeks

Cutting tier-1 call center workload by 40%

The scenario we design for: an operation whose agents spend their day on the same handful of repetitive tier-1 questions. A voice AI layer sits in front of the existing queue and handles triage, qualification and routing, escalating anything it shouldn't touch.

What we'd build: A voice AI layer in front of the existing tier-1 queue — triage, qualification, routing, and CRM lookup — deployed alongside the human team, not replacing it.

How we'd measure it: Workload measured as agent-handled tier-1 call minutes: a baseline taken before deployment, compared against the same window after. Agreed with you before we start, so the number can't move afterwards.

What we'd aim at

40% of tier-1 inbound handled without an agent
Out-of-hours coverage without new hires
Escalation path for anything the agent shouldn't handle
Per-call cost modelled at ~£0.18 vs ~£3.20

Architecture

Telephony / SIPVoice AI triageCRM lookupRoute or resolveLive analytics

Voice AITelephonyCRMAnalytics

Months

Target, not a result

Industry: Media / OTTEstimate: 12–14 weeks

An OTT platform in months, not years

The scenario we design for: a media business that wants a streaming product without assembling six vendors and a two-year roadmap. One backend, adaptive streaming that survives real mobile networks, and apps across web, mobile and TV.

What we'd build: End-to-end OTT build: transcoding and adaptive streaming, payments, multi-device apps, AI auto-tagging, and recommendations.

How we'd measure it: Delivery measured from scope lock to production launch. Time-to-first-frame and device coverage verified in QA against the targets agreed up front.

What we'd aim at

A production launch measured in months
Sub-2s time-to-first-frame as a design target
Web, iOS, Android and smart TV from one backend
ABR tuned for real networks, not office WiFi

Architecture

Ingest / transcodeAdaptive streamingMulti-device appsPaymentsML tagging & recs

OTTStreamingPaymentsMulti-deviceML

−65%

Target, not a result

Industry: Government / MunicipalEstimate: 10 weeks

Cutting citizen application drop-off by two-thirds

The scenario we design for: a council portal where most people who start an application never finish it. A long desktop-era form, no way to save progress, and no way to check what happened after you submit.

What we'd build: Rebuild of the citizen application flow — mobile-first UX, a long form restructured into a few logical sections, save-and-resume, and real-time status tracking.

How we'd measure it: Drop-off measured as started-but-not-submitted applications, baselined before the rebuild and compared after. Accessibility validated against WCAG 2.2 AA success criteria.

What we'd aim at

Drop-off cut from roughly two-thirds to roughly one-fifth
Fewer inbound 'where is my application' calls
WCAG 2.2 AA conformance as a build requirement, not a retrofit
Mobile completion as the primary path

Architecture

Mobile-first formSave & resumeStatus serviceNotifications

Citizen portalAccessibilityGOV.UK Design System

10M+/day

Target, not a result

Industry: TelecomEstimate: 12 weeks

A real-time CDR pipeline at carrier scale

The scenario we design for: call data spread across legacy systems, batch jobs that land hours late, and no way to replay a bad window. A streaming pipeline that goes from SIP event to dashboard in under a second.

What we'd build: Design, build, and migration of a real-time CDR pipeline to replace batch legacy systems, with replay and backfill on demand.

How we'd measure it: Latency measured end-to-end from SIP event to dashboard; throughput load-tested at projected production peak before cutover.

What we'd aim at

Sub-second end-to-end latency as a design target
10M+ records/day without data loss
Replay and backfill on demand
Storage tiered so petabyte scale stays affordable

Architecture

SIP eventsStream ingestDedupe & enrichClickHouse storeDashboard

Stream processingTelecomKafkaClickHouse

Want one of these aimed at your numbers?

Tell us the operation and we'll model it against your actual volumes — then you can decide whether the number is worth chasing.

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