AI for insights and analytics
A client turning over four million pays 6,500 a year against a firm median of 12,000, took 13,400 of time last year, and shows three advisory triggers in its accounts with no conversation logged. AI analytics reads the practice, time and deadline files, and answers the partner's question: where is the firm under-priced, and where is advice waiting to be sold.
These are the questions accounting firms ask. Each one maps to a measure our tools compute from your files. The AI model chooses the measure and explains the result; the arithmetic is done by our code, and every total is checked.
| The question | The measure behind it | What comes back |
|---|---|---|
| Which clients pay well below the norm for their size? | Fee per client against size norm | Fee against the firm median for the client's turnover band, with realisation on the engagement. |
| Which clients use one service line where similar clients use three? | Service lines per client against norm | Service lines per client against the norm, with the fee gap valued at the firm's own rates. |
| Which advisory triggers in this year's accounts have not been acted on? | Advisory triggers acted on | Triggers such as a new entity, a threshold approached or a director's loan, by client, with whether a conversation is logged. |
| Who is overloaded this season? | Deadline load per manager | Deadlines by manager by week against capacity, with the peaks. |
| Where is realisation lowest? | Realisation by client | Fees billed over time at standard rates, by client, with write-offs listed. |
| Which clients are slow to pay, and slow to be billed? | Lock-up by client | Work in progress and debtor days by client, with the ones over threshold. |
Every figure below was computed by a tool from the rows and checked before it was shown. The lines sum; the percentages match; each line opens to the rows that make it.
Where are we under-priced against our own norms?
Fees below the firm's median for each client's size band total $1.0m a year across 3 offices, on engagements where realisation is also below 60 percent. Main office holds $520k, 51% of it.
| Main office | $520k | 51% of the total |
| North office | $310k | 30% of the total |
| Riverside office | $190k | 19% of the total |
| The 3 lines sum to | $1.0m | 0 unexplained |
Which main office clients?
3 clients hold $14k of the $520k gap at the Main office, 3% of it. Each has advisory triggers in this year's accounts and no advisory conversation logged.
| Fairfax Engineering | $5.5k | fee 6.5k against 12k median; realisation 49%; 3 triggers |
| Redwood Care | $4.8k | fee 7k against 11.8k; new entity formed |
| Linden Foods | $4.2k | fee 8k against 12.2k; audit threshold approaching |
| These 3 are | $14k | 3% of Main office |
What do the partners do before next year's engagement letters?
Re-price at renewal with the time record in hand, and open the advisory conversation the accounts are already asking for. The manager owns the client call; the partner owns the price.
| Client managers | Raise the advisory triggers with Fairfax, Redwood and Linden | This month |
| Partners | Re-price the three engagements at renewal | Before renewal |
| Operations partner | Review fee against norm and realisation quarterly | Quarterly |
Each measure has one formula, one source and one meaning. They are computed per manager and partner and in total, and every one carries an identity that must hold before it is shown.
| Measure | Formula | From | What it tells you |
|---|---|---|---|
| Service lines per client against norm | Service lines with fees ÷ median for clients of the same type and size | Practice management; billing | Compliance-only clients who need more |
| Fee per client against size norm | Annual fee ÷ median fee for the client's turnover band and complexity | Billing; client master | The under-priced book, ranked by gap |
| Season watch | Clients whose records, start or draft are later than their own usual date by more than a stated margin | Workflow dates, current and prior years | Late clients and quiet departures, early |
| Deadline load per manager | Hours of work due per week, per manager, against hours available | Filing calendar; budgets; capacity | The crunch, weeks ahead |
| Advisory triggers acted on | Triggers with a logged conversation within 60 days ÷ triggers found | Client accounts and returns; CRM | Advice the data called for and nobody offered |
| Realisation by client | Billed ÷ time at standard, per client, with trend | Time and billing | Fixed fees that no longer cover the work |
| Retention by fees | Prior fees of clients retained ÷ prior fees; losses by reason | Billing | Whether the clients leaving are the large ones |
| Lock-up by client | (Work in progress + debtors) ÷ average daily fees | Ledgers | Clients the firm is financing |
| Scope creep on fixed fees | Hours on work outside the engagement letter, per client, billed and unbilled | Timesheets with work codes | Extra work given away |
| Onboarding time | Days from engagement to first deliverable; information requests outstanding | Workflow records | New clients stalling before they start |
Each measure is worked through, with the export it comes from and what to drop, in Practice KPIs for tax and accounting firms.
From your question and the measures declared for tax and accounting, the model picks the one that answers it, and the period and comparison the question implies.
Deterministic code reads the rows, computes the measure, and checks the identities below. The same question on the same data gives the same answer, every time.
The AI model writes the sentence around the result, naming the manager or account behind it. It states no figure that is not in the result, and every figure links to its rows.
| Realisation | Time at standard = billed + written off + work in progress |
| Deadline load | Filings due = filed + in progress + not started; every filing has one manager |
| Retention | Opening fees + new − lost ± fee change = closing fees |
| Triggers | Found = acted on + declined + open |
The exports accounting firms already produce. Column names are mapped once and the mapping is reused. A file is the way in; scheduled delivery and connections to your systems come with the plan, and every source is listed here.
An answer is a list with an owner and a cadence, or it is a chart nobody works.
| Measures | Owner | Cadence |
|---|---|---|
| Season watch; deadline load | Managers; operations partner | Weekly in season |
| Advisory triggers; onboarding | Client managers; partners | Monthly |
| Realisation; scope creep; lock-up | Partners; finance | Quarterly |
| Fee against norm; service lines; retention | Managing partner | Annually, before fee letters go out |
From your own client base: the median fee for clients in the same turnover band, computed from the practice file. A client paying half the median is a fact; whether the engagement is simpler than its peers is the partner's judgement, made with the time record beside the fee.
A fact in the client's own data that usually leads to advice: turnover approaching a threshold, a new entity, a large director's loan, a change of ownership. You define the list; the tool finds the triggers in the accounts data and checks whether a conversation is logged in the CRM. Nothing is inferred by a model.
Deadline load per manager by week is computed from the deadline file against each manager's capacity, so the peaks are visible in advance and work can be moved. The tool does not move it; it shows where the overload will be.
The client and fee file, time at standard rates, the deadline list, and the CRM log. Accounts data by client adds the advisory triggers. Client names can be replaced with identifiers before upload on Enterprise.
Advisory trigger · Season watch · Deadline load · Realisation · Lock-up · Norm
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Bring a few thousand rows. The data map opens next, every column mapped once, and the first question is answered in minutes. Free, in your browser, no account.