How we work
Every engagement has the same general shape: three to six months,
one or two core applications, a principal embedded with the
client team. Below you'll find the three areas the firm works in,
the four-step engagement structure, and what actually happens on
a first call.
AI-Augmented Dev-Ops
Fitting AI tooling into the development pipeline so it actually
reduces effort. Test generation against existing codebases,
assisted refactoring on long-lived modules, automated review
for the categories of issues that suit model review (typing,
common bug patterns, style), and scaffolding or migration code
where the patterns are repeatable.
Teams usually call us in here for one of two reasons. Either a
CTO has approved AI tooling but six months later the
productivity numbers look the same; or individual engineers are
using AI assistants productively but the gains aren't
compounding into team-level throughput. There's a third version
too (refactoring is bottlenecked on engineer hours and the
codebase happens to suit AI tooling), but that one's rarer.
An engagement typically opens with a baseline measurement of
where engineering time is going across a representative team,
sprint by sprint and ticket type by ticket type. We pick two to
four categories of work with the strongest expected return,
implement the tooling and workflow changes for those, then
re-measure against the baseline at six and twelve weeks. What
teams walk away with is some combination of dev-month load
reductions on the targeted categories, workflow patterns the
team keeps using after we leave, and a baseline framework the
engineering organisation can apply to future tooling decisions
on its own.
AI-Ops Optimization
The economics of running AI in production. Model selection
against the actual quality requirements of the task rather than
against benchmark scores. Inference cost reduction through
caching, smarter routing between model sizes, and quantisation
where it actually fits. Infrastructure tuning across the
serving stack: autoscaling, request shape, retry policies. The
point is to treat the AI cost line as a real engineering
discipline instead of a usage-based bill that scales with
adoption.
Teams usually call us in here when inference costs are growing
faster than the revenue from AI features, model spend has
turned into a board-level conversation, or the team picked an
over-specified model six months ago and is now paying for
quality the actual task doesn't need. Sometimes AI features
have shipped without anyone building the cost-monitoring and
model-selection discipline that production AI demands.
We start with a measurement pass on the current cost structure:
model by model, feature by feature, request by request. We
identify the largest cost categories and the changes most
likely to move them. Then we implement iteratively, starting
with the biggest cost categories: caching layer, smaller-model
routing, request shape changes, infrastructure tuning. Each
change is measured against the cost baseline before we move on.
Engagements like this typically take 30–55% off monthly
inference spend on the targeted features, and leave behind a
model-selection framework plus monitoring on cost regressions
that used to slip through unnoticed.
Process Re-Engineering
Most engineering organisations don't have a tooling problem;
they have a flow problem. Process Re-Engineering is the work
of redesigning how work moves through the pipeline: ticket
flow and hand-off reduction, sprint structure and standup
discipline, coordination overhead between teams, and the gap
between "we use Jira" and "Jira tracks the actual work." This
area sells fewer tools than the other two, which is part of
why it's usually where the biggest gains live.
It tends to be the right intervention when the organisation
has grown but velocity hasn't scaled with headcount. Meetings
have multiplied to coordinate work that used to coordinate
itself, there are more hand-offs between teams than there were
a year ago, and senior engineers are spending more time in
process than in code. The block is rarely engineering
complexity at that point; it's coordination cost.
The work starts with observation: sitting in on standups,
shadowing tickets through their lifecycle, mapping hand-offs
and queue times. From there we pick the two or three
coordination patterns producing the most overhead and
implement changes with the team rather than handing them down.
Depending on what the observation found, the changes might be
to ticket structure, hand-off rules, meeting cadence, or
ownership boundaries. Cycle time and coordination time get
re-measured against the baseline at the end. The artefact
teams seem to value most isn't any single process change but
the honest answer to "where is the time actually going."
Engagements run three to six months. Scope is usually one or
two applications or teams. We won't take on the whole
engineering organisation at once — that's not a pilot, that's a
transformation programme, and it isn't what the firm does.
Fees split in two: a fixed-fee base for the engagement plus a
bonus tied to the agreed outcome metrics. The structure aligns
the firm's incentives with the client's. Numbers depend on
scope and get worked out on the first call.
We don't bill hourly, we don't sell dedicated headcount, and
there are no ongoing monthly retainers. The engagement model
is one shape on purpose.
Thirty minutes on a call. Nothing to prepare beforehand. We'll
ask what's prompted you to look at this: AI adoption that
hasn't moved the metrics, a modernisation that's been stalled
for too long, an AI rollout you want to get right the first
time, or whatever else surfaced. You'll ask whatever you need
to about how the firm works.
Whether it's a fit becomes clear during the call, not
afterwards. If it is, the next step is a one-page proposal
describing what we'd measure, what we'd work on, the duration,
the fee structure, and the outcome metrics we'd both agree on.
If it isn't, we'll say so.
The engagement model only works when the firm and the client
are both confident in the fit. If we don't think we can move
the metrics that matter for you, we'll say so on the call.
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