Work
We work with sales, finance and operations teams to automate the manual work that costs them the most time. The problems worth solving are rarely well defined. We work inside the team to find them, with the technical depth to build the fix in days rather than weeks.
The examples below are a sample of that work, grouped by function. Each one starts with the problem, then describes what the system does and what it produced for the client.
Go-to-market
Go-to-market is where most companies bring us in first. Typically the sales team is growing and the work around it is still manual: reps sourcing their own leads, inbound leads worked by hand, call notes written up at the end of the day. We work with the sales leader on whichever of these is costing the team the most, and build tooling that keeps up as the team scales.
Our team brings GTM engineering specialists who can start immediately, where hiring the same capability typically takes months. We work inside several of the fastest-growing companies in Australia, and bring the systems and tools that work to our clients.
Sales reps building their own lead lists
Most companies with a sales function leave lead sourcing and enrichment to the SDRs themselves. A rep can spend tens of hours a week assembling an ICP list by hand or in a tool like Clay, and the quality of what comes out rarely justifies the time that went in. Every one of those hours is an hour not spent on the phone.
In these instances we typically look at the company's proven go-to-market strategies and automate what's already working. For instance, we'll take an existing ICP and build a signals engine that combines data from multiple sources, checks and dedupes each account against what's already in the CRM, and automatically imports qualified leads into the rep's queue with detailed notes attached, so the list is verified and ready to dial.
Businesses that aren't in your typical databases
The standard data providers are built around companies with a LinkedIn footprint. If you sell to restaurants, trades or medical practices, most of your market doesn't have one, and the accounts that fit best are the ones missing from any list you can buy.
We build these lists ourselves, from public registries, government business data, professional directories, association lists, mapping data and the businesses' own websites, then resolve everything to a single verified record per business. For one client this produced 14,876 net-new accounts within their ICP.
Teams working from stale CRM data
After a few years in use a CRM carries duplicate accounts, contacts who have moved on and records owned by people who have left, and the dial lists built from it carry the same problems: numbers that are wrong, out of date, or formatted in a way the dialler can't read. Every one of those dials counts as activity and was never going to connect.
We clean up the CRM and the dial list together. Duplicate accounts are merged into a single record, ownership is reassigned from departed users, contacts who have changed roles are updated, and every number is reformatted, verified across several providers and washed against the Do Not Call Register before it reaches the dialler. Formatting alone typically recovers a large share of the list before a single new number is bought.
Sales reps typing up their own calls
Every call a rep makes leaves admin behind it: notes, next steps, a follow-up to send and CRM fields to update. That work is done at the end of the day with less detail than the call had, so the CRM holds a thinner version of each account than the rep does, and everyone else on the deal works from that.
We build tooling that does the write-up from the call recording. It extracts what was discussed, writes it into the CRM fields for notes, next steps and handover, and files a summary where the team already reads them, filling only empty fields so nothing a rep has entered is overwritten.
Finance
Accountants reconciling transactions by hand
Matching a transaction takes information spread across tools: the bank feed, the ledger, the billing system and the invoice sitting in an inbox. No single tool holds that context, which is why reconciliation has stayed manual and takes a finance team several days each month.
We build reconciliation engines that give a model the same context the person has, by connecting the bank feed, ledger, billing system and inbox. The engine matches each transaction to its invoice and attaches it to the record, proposes the match with its reasoning, and flags anything it isn't confident about for review. The team reviews the exceptions and signs off before anything posts to the ledger.
Finance teams rebuilding the same deck every cycle
The month-end and quarter-end pack is assembled from the same exports each cycle, with commentary written on what moved. Most of the work is repetition, and the part that isn't is reading the numbers and explaining the change.
We automate the assembly and leave the judgement with the team. Figures are drawn from source, the same structure is applied every cycle, and the commentary is drafted from what moved, so the team reviews a draft of a pack they already sign off.
Operations
Ops teams repeating the same steps across systems
Most operations work is a sequence of steps across systems that one person knows by heart: setting up a new customer in billing, the CRM and the support tool, answering the requests that arrive in a shared inbox, assembling the same weekly report from the same four exports.
We map the process step by step with the person who runs it, then build it to run on its own. A shared inbox becomes a system that reads each request, pulls the answer from the relevant systems and drafts the reply. Anything it can't resolve goes to the right person, with the context it has already gathered.
Each of these started as work someone inside the team was doing by hand, and was built around the tools they already used. We work with teams to find problems worth solving, then build the systems that fix them.