[ Case Studies ]

What we built, and what it changed.

Platforms we designed, built and operate, and the commercial difference they make to the businesses running them.

01

Onrolo

Recruitment operations platformonrolo.ai

Taking the administration out of high-volume hiring, and the guesswork out of reporting it.

90+ hrsof manual CV screening removed per office, every month
8 in 10applicants reach a booked appointment rather than being lost
Minutesfrom application to first contact, instead of days
100%of candidate activity captured for reporting and audit

The problem

High-volume recruitment teams spend most of their week on administration rather than hiring. Reading CVs, chasing applicants, arranging interviews and rekeying the same details into three different places.

Speed decides the outcome. An applicant contacted within minutes converts. The same applicant contacted two days later has usually accepted something else, so every hour of delay is money spent on advertising that produces nothing.

And because the process lived across inboxes, spreadsheets and messaging apps, there was no reliable record of what had happened. Reporting was assembled by hand, late, and disagreed with itself.

What we built

01Applications captured automatically from job boards and inbound email, and turned into structured records without anyone rekeying anything
02AI screening that reads every CV against the role, scores it, and explains the score in writing
03Applicants contacted within minutes of applying, at any hour, without a recruiter being awake
04Interviews arranged, confirmed and reminded automatically, straight into the interviewer’s own calendar
05Onboarding, document signing and compliance checks handled in the same place as the hiring
06Every action recorded once, so reporting is a query rather than an afternoon of spreadsheet work

Capabilities engineered

AI voice calling, inbound and outboundAI screening interviews by chatNative calendar integration, Google and OutlookNative form building and sendingKanban pipelines for pre-hire and post-hireAutomated SMS and email sequencingExplained CV scoring against each roleDigital onboarding and document signingCompliance and right to work checksLive reporting across every officeNative iOS and Android apps

Status: In daily use across a national recruitment network, on web, iOS and Android.

02

Gorizzume

AI career platform, consumer and institutionalgorizzume.co.uk

Turning a government spreadsheet and a stack of job adverts into a decision someone can act on.

120,000+licensed sponsors tracked and refreshed every single day
Same dayvisibility of new sponsors, rather than a quarterly spreadsheet
3surfaces covered: web, mobile and browser extension
Cohortlevel reporting for institutions, generated automatically

The problem

Graduates and international students apply into the hardest market on record, and most cannot tell which employers are even able to sponsor them. The information exists, but only as a raw public register nobody can use.

The advice they receive is generic. A CV is either accepted or ignored, with no explanation, so nothing improves between applications.

Careers teams have the same problem multiplied by a cohort. Hundreds of people, no visibility of who is progressing, and no way to intervene before it is too late.

What we built

01A CV scored against a specific advert, with the weak sections rewritten rather than merely flagged
02Interview practice with a real spoken conversation and structured feedback afterwards
03The full licensed sponsor register turned into something searchable, and kept current automatically
04Live vacancies matched against both the sponsor register and the person’s own CV
05Scoring available inside the browser, on the advert the user is already reading
06Cohort licensing for institutions, with automatic enrolment and progress reporting

Capabilities engineered

Conversational AI interview coaching with real voiceAutomated daily tracking of licensed visa sponsorsCV scoring against a specific job advertSemantic job matching from a candidate CVIn-browser scoring of any live job advertAdaptive question practice that responds to performanceCohort seat licensing with automatic enrolmentProgress analytics for careers teamsNative iOS and Android apps

Status: Live for individual users and licensed to institutions by seat.

03

AI receptionist for The UK Driving School

Client build: AI voice reception and booking

Answering the phone when every instructor is mid-lesson, and showing the owner exactly what was said.

Any hourthe phone is answered, including while every instructor is mid-lesson
174scripted test calls the receptionist must pass before any change goes live
78local districts recognised by name, not only by postcode
No lost callerswhen a transfer fails: details are logged before the call is put through

The problem

The UK Driving School sells lessons through the phone. Its ads make the phone ring, and the people who could answer it are instructors in a moving car with a learner at the wheel. Every unanswered call is a pupil who rings the next school on the list.

The owner was not worried about a machine answering. He was worried about a machine answering wrongly: quoting the wrong price, promising a postcode the school does not cover, or failing a caller whose English is limited, someone a person would have understood.

And the diaries were on paper, one per instructor, so nothing that answered the phone could know who was free.

What we built

01A voice receptionist that answers in the school’s name, quotes the real prices, checks the caller’s area against the instructors who cover it, and takes the booking
02Firm rules on what it must hand to a person: reschedules, failed tests, complaints and refunds
03Live transfer to a human when a caller asks for one, logged before it is attempted so a missed handover still leaves a name, a number and the reason
04An owner console built as an audit surface: every call with its transcript and what was quoted, checked or refused
05A mobile calendar for instructors to mark themselves free or busy and move lessons with a tap
06Pupil records with lessons, payments, balances and progress against the driving test competencies, plus booking confirmations by email

Capabilities engineered

AI voice reception in the school’s nameArea recognition by district name and postcodeRule-based handover with live call transferGuardrails that rephrase rather than hang upPer-call transcript and audit trailInstructor availability calendar on mobilePupil records and lesson paymentsAutomated test calls run before every changeMulti-school design, branded per school

Status: Live in pilot at The UK Driving School.

04

Augustova GTM

CRM and go-to-market platform · private release, enquiry only

One platform from the first buying signal to the paid invoice: sourcing, AI research, calling, email, meetings, proposals and billing in a single place.

Minutesfrom naming a company to a researched account with an angle for the call
Screenedevery number checked against TPS and CTPS before it reaches the call queue
One recordfrom first signal to signed proposal and paid invoice, instead of six tools
Penniesper account researched, with duplicate research blocked automatically

The problem

Most B2B sales teams run on six or seven tools that do not talk to each other: a CRM, a data provider, a dialler, an email tool, a scheduling link, a compliance service and separate software for proposals and invoices. The context that wins a call ends up scattered across all of them.

Prospect lists on their own are noise. What matters is timing: a company hiring for a role that could be automated, visiting your website, or filing a change at Companies House this week.

And outbound calling in the UK has rules. A number registered with the Telephone Preference Service must not be called, and a stack that leaves that check to memory is a liability waiting to happen.

What we built

01Signal-led sourcing: hiring activity, website visits and Companies House records brought into a single live view
02AI account research against a scoring rubric, with the evidence behind every score and a recommended angle for the first conversation
03A sales brief on every account and a tailored opener on the call screen, so each call starts from something specific
04Browser-based calling, inbound and outbound, with every call, note and outcome logged against the account
05TPS and CTPS screening built into the call queue, so an unscreened number can never be dialled by accident
06Email sent and received through the CRM, with replies landing on the account timeline and bounces and unsubscribes suppressed automatically
07A built-in calendar and booking link, with meeting reminders, host prep and no-show follow-ups
08Proposals and quotes with an online accept page, then invoices, credit notes and monthly plans from the same record
09A live selling dashboard: dials, connect rates, meetings, show and close rates, cost per meeting and revenue

Capabilities engineered

Signal-based account sourcingAI account research with scored evidenceOn-demand contact enrichmentBrowser-based calling, inbound and outboundIn-browser call transcriptionTPS and CTPS compliance screeningTwo-way email inside the CRMReply detection that turns interest into signalsCalendar, booking links and meeting automationProposals and quotes with online acceptanceInvoicing, credit notes and monthly plansLive selling dashboard against targetFull CRM pipeline from first signal to paid invoiceSpend controls on every AI research run

Status: Private release. Not publicly available: access is by enquiry only.

05

AI cost governance

The discipline behind every platform we run, offered as a service

Knowing exactly what your AI features cost, and being certain they cannot cost more.

Per usercost visibility, rather than a single unexplained monthly bill
Cappedspend that cannot be exceeded, enforced automatically
Forecastableunit economics before a feature is scaled to everyone
Auditedrecord of every automated decision the system makes

The problem

AI features spend money on every single request, and almost no organisation can say what one customer costs them in a month. The invoice arrives, and the reason for its size is no longer recoverable.

The controls meant to prevent that are usually written once, reviewed once, and never seen to work. A limit that has never been watched stopping something is an assumption, not a control.

The result is a budget nobody can forecast and a risk nobody can quantify, attached to the part of the product the business is betting on.

What we built

01Cost attribution down to the individual request, tied to a customer, a feature and a model
02Usage metering wired directly into billing, so the invoice and the underlying record cannot drift apart
03Hard spending ceilings enforced in real time, tested by watching them actually stop work
04Intelligent routing, so routine work runs on economical models and only the hard work runs on expensive ones
05Caching and batching wherever the workload allows it, which is usually most of it
06A complete audit trail on every automated action, for reporting and for compliance

Capabilities engineered

Per customer and per feature cost attributionReal time spend ceilings with automatic cut-offIntelligent model routingUsage metering tied directly to invoicingResponse caching and batch processingFull audit trail on every automated actionForecasting from measured unit cost

Status: Applied across both platforms. Available as a fixed scope engagement.

[ Founder track record · Data & analytics ]

Trusted with the data. Delivered on the date.

Before Augustova, our co-founder Hamid Gondal coordinated and analysed data engagements for a major international news broadcaster and a London borough council. Both are described here with the clients’ permission and without their names. Neither ran late.

06 · Media & broadcasting

From a day of spreadsheet work to a click

Clienta major international news broadcaster · marketing team

The marketing team had the data. What it lacked was a way to get an answer out of it without losing a day each time.

Time to headline metricsAs reported by the delivery team
Before Around a day
manual consolidation, every time a question was asked
After Minutes
one dashboard, refreshed by the pipeline

Metrics that previously took around a day to pull together could be viewed in minutes. The time saved went back into presenting insight to decision-makers rather than assembling it.

The problem

The marketing team was collecting large volumes of data across its channels, but had no automated system to consolidate it, analyse it and surface insight.

Without a pipeline bringing the data together in one place, every question meant assembling the answer by hand, and findings reached decision-makers late and with less confidence than they deserved.

What was delivered
  • An automated data pipeline consolidating the team’s marketing data into a single, reliable source.
  • A Power BI dashboard giving the team its headline metrics at the touch of a button.
  • A reporting workflow the team could run itself, making it far easier to present insight and support data-driven decisions.
Hamid’s role
Project coordinator and data analyst: strategised the solution and managed delivery to completion.
Timeline
6 weeksCompleted on time
Outcome
Significantly reduced friction in getting answers from the data.
Client feedback
Happy with the service, and indicated interest in working together again.

Client anonymised at their request. Nothing about their data, or how they intended to use it, is disclosed. That information was shared only so a solution could be built to fit.

07 · Local government

Housing demand, five years ahead

Clienta London borough council · housing department

A council held the data on housing demand. It needed the questions answered, and a view of the next five years it could plan against.

From data to planning decisionStructure of the engagement
Input
The council’s housing demand data
Held by the housing department, handled under GDPR throughout.
Analysis
The department’s specific questions
Approach agreed up front, then managed to the council’s deadline.
Output
Five-year projections of council housing demand
Plus related figures, in a one-off analytical report the department could plan from.
GDPRPersonal and housing data. The questions, the figures and the findings stay with the council.
The problem

The council held data on housing demand, but data on its own does not answer a planning question.

The housing department needed that data analysed against the specific questions it had, turned into forward-looking projections, and delivered to a fixed date with the care personal and housing data requires.

What was delivered
  • A one-off analytical report answering the council’s questions directly.
  • Five-year projections of council housing demand and related figures, to support planning.
  • Delivery to deadline, with the analytical approach strategised up front and managed through to hand-over.
Hamid’s role
Project coordinator and data analyst: strategised the approach and managed delivery to deadline.
Timeline
8 weeksDelivered to deadline
Outcome
Not disclosable. The outputs involve personal and housing data covered by GDPR.
Client feedback
None recorded. We would rather say so than invent one.

Client anonymised at their request. Nothing relating to their data, the specific questions asked, the projected figures or the results is disclosed.

[ What carried into Augustova ]

The same discipline, now built into our products.

These engagements were delivered as part of a consultancy’s team, not by Augustova. What transferred is the way of working, and you can see it in the three case studies above.

From 04 · pipeline and dashboard

Reporting that is a query, not an afternoon

The consolidate-once, report-instantly pattern is exactly how Onrolo records every candidate action and reports live across every office.

See Onrolo · 01
From 05 · five-year projections

Forecasting before you commit

Projecting demand before a council plans is the same habit as forecasting unit cost before an AI feature is scaled to everyone.

See AI cost governance · 03
From 05 · public-sector data

Personal data handled properly

Council housing data under GDPR set the bar. Augustova is ICO registered, with a data processing agreement available on request.

Security and data
[ Data & analytics ]

Bring us the question your data isn’t answering.

A pipeline that consolidates what you already collect, a dashboard your team runs itself, or a one-off analysis to a fixed date. Fixed price, and you own what we build.

A good fit if
  • Your data lives in several places and every report starts with copying and pasting.
  • Decision-makers wait days for numbers that should take minutes.
  • You need a forecast to plan against, and a defensible method behind it.
  • The data is sensitive and you need it handled to GDPR from the first day.
[ Your turn ]

We build the same way for other companies

Fixed price, working software, and you own it. Tell us what your team still does by hand every week.

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