Metric · 01
Rework rate
How often does work need to be redone? Our quality-gate models catch problematic patterns early so the rework curve flattens, sprint by sprint. We track this baseline-to-current at every quarterly review.
How we think
We build the invisible layer
We build the invisible layer
that turns expertise into infrastructure.
Most consulting either describes the problem or hands you a tool. We do neither. We sit inside your operations, capture how decisions are actually made, and build the data and models that let your firm compound that intelligence over time.
The shift
Tools are converging. Intelligence is the moat.
In the next five years, every firm in your sector will have access to the same generic AI features. The same autocomplete. The same dashboards. The same off-the-shelf models trained on industry-wide averages.
When everyone uses the same tools, everyone gets the same 15% improvement. The firms that pull ahead won't be the ones with the biggest software budget. They'll be the ones who captured what their own people know — and trained systems on it — while competitors were still deciding whether it mattered.
That gap, between firms with proprietary intelligence and firms without it, compounds quietly for years. Then suddenly it's a category. We work with the firms that want to be on the right side of it.
Five principles
How we approach every engagement.
No. 01
Capture before you optimise.
Optimising what you can't measure is a folk practice. Before any model, any automation, any dashboard, we instrument the workflow so the decisions become structured data. The bottleneck almost always sits somewhere different from where everyone agreed it would.
No. 02
Invisible beats imposed.
Adoption is the silent killer of business technology. Tools that demand workflow changes get abandoned within a quarter, no matter how impressive the demo. Our capture layer is silent — your people work the way they always have, and the data accumulates without anyone noticing.
No. 03
Your data, your moat.
We are processors, not owners. Captured data lives in your infrastructure under your governance policies. Models are trained on your premises (or your cloud account) on data that never crosses our boundary. Everything we build is documented well enough that any competent engineer could pick it up.
No. 04
Models that learn what you mean, not what the market means.
Generic models predict averages. Your firm's value lives in the deviations from average — the patterns that make your delivery distinctive. We train sequence and classification models on your own historical data so the systems learn the way you do things, not the way the industry generally does them.
No. 05
Compound, don't ship.
We don't sell projects with a closing party at the end. We sell multi-year compounding — quarterly reviews, model retraining, new capture surfaces as your operations evolve. The intelligence accumulates. The advantage widens. Your fee proposals start including data assets nobody else has.
Outcomes
What we actually measure.
Vanity metrics (lines of code, models trained, dashboards delivered) describe activity, not advantage. These are the numbers we hold ourselves accountable to.
Metric · 02
Senior-time leverage
Senior people are the scarce resource. We measure the percentage of senior decisions that the model can pre-suggest correctly. As that number rises, your seniors spend more time on the genuinely novel work — and less re-doing what they've already decided fifty times.
Metric · 03
Onboarding time-to-quality
How long does it take a new hire to produce work indistinguishable from a senior's? Pattern-suggestion systems shrink this dramatically. We track months-to-quality before and after deployment, and report it transparently.
Metric · 04
Data moat depth
How many months of clean, structured operational data do you have? In year one we're building this from zero. By year three it should be measured in the millions of structured events. The depth of this dataset is the floor under everything else.
Metric · 05
Model lift vs baseline
For every model we train, we publish its accuracy against a baseline (the off-the-shelf alternative, or human-without-assistance). If a custom model isn't out-performing the baseline by a meaningful margin, we don't deploy it.
Metric · 06
Engagement compounding
Year over year, the same fee should deliver more value as the dataset matures and models compound. We measure (and present at each annual review) the increasing ROI per dollar of engagement spend.
Plain talk
What this isn't.
It isn't a SaaS product.
We don't sell a licence to a dashboard. The engagement is hands-on consulting from senior people, with capture, engineering, modelling, and deployment baked into a single multi-year arc.
It isn't fast.
Real intelligence layers compound — they don't ship. The first six to twelve months are about capturing data cleanly. Visible automation usually arrives in months six to nine. The compounding starts in year two.
It isn't for everyone.
Firms with chaotic operations don't have patterns to capture yet. Firms unwilling to commit beyond a single quarter won't see the compounding. We're transparent about who this suits, and just as transparent about who it doesn't.
It isn't generic.
We do not retrofit playbooks from other industries. Every engagement starts with weeks of operational ethnography because the patterns that matter are specific to your firm, your sector, and your customer base.
If this is the right shape of partner for you,
let's talk.
A 20-minute intro call. We listen, ask a few sharp questions, and tell you honestly whether what we do is a fit for what you need.
Start the conversation →