Software & professional services
Software and professional services
Consultancies, agencies, software firms and engineering practices sell effort before they know what it will cost and settle up afterwards. The margin on each engagement is decided in the choices made while the work is live: which project to watch, what to bid, whom to staff, what to renegotiate. By the time the accounts see it, it is history. So we model those choices so that they are made on evidence while there is still time to act, and we know this sector because we are in it.
The decisions that bind here
The five decisions that set the year
There is nothing exotic here. Each is made dozens of times a year, usually by feel, by someone senior who is good at it. But a few per cent wrong on each one compounds across a portfolio of engagements until it is the difference between a good year and a bad one.
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Which projects are quietly losing money?
Cost-to-complete and probability of loss, per project, refreshed as the work moves, and not confirmed at period-end when the levers are already gone.
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What to bid, and at what floor?
A minimum acceptable price that reflects the uncertainty in the effort and the risk of the specific client, in place of last quarter’s blended average.
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Whom to staff, and in what sequence?
Allocating scarce senior skills across parallel work to protect margin and delivery, ahead of filling a utilisation target.
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Bid or no-bid?
Which pursuits are worth the cost of chasing, given win probability, delivery risk and the capacity they would consume if won.
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What does plan-versus-reality mean?
Distinguishing an execution problem from an unrealistic plan, a client-created delay, or scope that grew without being repriced.
What it costs to get them wrong
Why the accounts find out last
The signal arrives late
By the time a project P&L turns negative, the decisions that caused it are weeks old and cannot be unmade. Reporting tells you what happened. It cannot tell you what to do while it is still happening, and that is the only time the information is worth anything.
Small errors compound
Scope creep, rework and a slow review each look minor in the week they happen. Together they move a project’s whole cost distribution, and across a portfolio one bad quarter absorbs the margin of a good one.
One loss pays for the fix
In a business running many capped or milestone engagements at once, one avoided loss usually exceeds the cost of measuring for it across the whole year. That is the arithmetic that pays for the audit.
How we engage
How an engagement runs here
We do not arrive with a platform. We start by finding whether a decision is worth improving at all, and prove the value on your own data before anyone commits to building a system to sustain it.
1. Audit
Find the decision that pays
A Decision Opportunity Audit runs on one project or a slice of the portfolio, establishes the baseline and the economic stakes, and ends with a candid recommendation, including the possibility that no system is needed.
2. Proof
Test it against your baseline
If the value is there, a proof of value tests the method on your own history before it is trusted, measured against a simple baseline, with calibration reported. Our standard for what counts as evidence is set out in how we validate.
3. Build
Build what proved out
Only what proved out gets built into something durable and owned. Find, Prove, Build keeps each stage honest, so scope follows evidence and not optimism.
What we need to see
Engineering activity (Git, issues, CI), a project or plan system, and enough cost and contract context to build a baseline. Where a baseline was never recorded, establishing one is the first deliverable. A forecast cannot repair data that does not exist.
Why we know this sector
Why we know this sector
Our own delivery work, long engineering engagements with software and data-heavy businesses such as Livesport among them, showed us the pattern first: the loss on a project is visible in the engineering signal well before it is visible in finance. That observation is the one Gitrevio turns into a repeatable product, and what the project-economics solution builds around. We name these as engagements. We do not dress them as case studies with numbers we never measured.
Where it applies
- Software consultancies and product studios on capped or milestone contracts.
- Agencies and creative firms selling retained or fixed-scope delivery.
- Engineering, architecture and other professional practices billing skilled effort.
- Internal delivery organisations answerable for margin as well as utilisation.
The research behind it
The method, published
When we are a fit
- Multiple simultaneous fixed-price, capped or milestone engagements.
- A sponsor close enough to the work to change staffing, scope or pricing.
- Engineering and project data that can be connected to a baseline.
When we are not
- A single project, or work too small to justify a system around it.
- No route to cost, effort or contract data.
- Nobody in a position to act on a forecast while it still changes the outcome.
Who you deal with
A partner owns the engagement from the first conversation to the last invoice. Work is confidential and unattributed by default, and the client names on this site are there because those owners agreed to it. An audit is a fixed fee agreed before it starts, so finding out what a decision is worth costs less than getting it wrong on one project. References will speak plainly about what did not work.
FAQ
Questions from firms like ours
Which decisions do you help with?
Which projects are quietly losing money, what to bid and at what floor, whom to staff and in what sequence, bid or no-bid, and what plan-versus-reality means. Each is made dozens of times a year, usually by feel, and the cost of getting them slightly wrong compounds across the portfolio.
How early can you flag a project that is losing money?
We forecast cost-to-complete and a probability of loss per project, refreshed as the work moves, and not confirmed at period-end when the levers are already gone. The point is to act while a staffing, scope or pricing change can still change the outcome.
What data do you need to start?
Engineering activity such as Git, issues and CI, a project or plan system, and enough cost and contract context to build a baseline. Where a baseline was never recorded, establishing one is the first deliverable. A forecast cannot repair data that does not exist.
Do you know this sector?
We are a software firm reading software firms. The pattern first became legible in our own long engineering engagements with software and data-heavy businesses such as Livesport: the loss on a project is visible in the engineering signal well before it is visible in finance. We name these as engagements. We do not dress them as case studies with numbers we never measured.
Start with the decision
Bring us one project
One project, or a slice of the portfolio. A short audit run by a partner will say where the margin is going and whether a system to watch it is worth what it would cost. The fee is fixed before we start.