What we built, and what it changed.
Platforms we designed, built and operate, and the commercial difference they make to the businesses running them.
Onrolo
Taking the administration out of high-volume hiring, and the guesswork out of reporting it.
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
Capabilities engineered
Status: In daily use across a national recruitment network, on web, iOS and Android.
Gorizzume
Turning a government spreadsheet and a stack of job adverts into a decision someone can act on.
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
Capabilities engineered
Status: Live for individual users and licensed to institutions by seat.
AI receptionist for The UK Driving School
Answering the phone when every instructor is mid-lesson, and showing the owner exactly what was said.
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
Capabilities engineered
Status: Live in pilot at The UK Driving School.
Augustova GTM
One platform from the first buying signal to the paid invoice: sourcing, AI research, calling, email, meetings, proposals and billing in a single place.
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
Capabilities engineered
Status: Private release. Not publicly available: access is by enquiry only.
AI cost governance
Knowing exactly what your AI features cost, and being certain they cannot cost more.
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
Capabilities engineered
Status: Applied across both platforms. Available as a fixed scope engagement.
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.
From a day of spreadsheet work to a click
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.
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 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.
- ◆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.
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.
Housing demand, five years ahead
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.
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.
- ◆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.
Client anonymised at their request. Nothing relating to their data, the specific questions asked, the projected figures or the results is disclosed.
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.
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 →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 →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 →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.
- ◆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.
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.