Industry
AI for professional services
Professional services firms sell expert time, then spend a large share of it on work no client would knowingly pay for: onboarding paperwork, document assembly, status updates, file notes and internal reporting. AI automation moves that work off your fee earners without touching the judgement they are paid for.
We integrate with
- Xero
- MYOB
- HubSpot
- Microsoft 365
- SharePoint
- DocuSign
- Karbon
- and more
The processes we automate in professional services
Client onboarding and engagement
New client details captured once, then flowing through everything: conflict check, identity verification, engagement letter drafted from your templates against the actual scope, e-signature sent, matter or job created, and the welcome sequence started. Days of elapsed time become hours.
Document turnaround
Long documents read and reduced to the parts that matter (obligations, dates, risks, changes from the prior version) with a summary in the firm's own format. The professional reviews an analysis rather than producing one from a blank page.
File notes and correspondence
Meeting and call notes structured into the file record, follow-up actions extracted and assigned, and correspondence drafted in the firm's voice for review. The administrative tail of every client interaction, handled while the detail is current.
Recurring client reporting
The monthly or quarterly pack assembled from source systems, with the commentary drafted from the actual movements rather than reconstructed from memory the day before it is due.
Proposals and scoping
Proposals assembled from your capability library and prior engagements, matched to the client's stated requirements, with a coverage check against the brief before it goes out.
Practice administration
Timesheet prompting from calendar and system activity, WIP and debtor follow-up, deadline tracking, and the internal reporting that currently occupies a partner every Friday afternoon.
The leverage problem
Professional services firms have one structural constraint: revenue is tied to expert hours, and expert hours are finite and expensive to add. Every hour a senior person spends on document assembly or status reporting is an hour of the firm's scarcest input spent on something with no client value at all.
The traditional answer is leverage: push work down to juniors. That still works, but it has been getting harder: junior salaries have risen, retention has fallen, and the work juniors traditionally learned on is precisely the work AI now does in seconds. Firms that keep juniors doing document review purely as training are paying a great deal for a training method with a shrinking future.
The better trade is to move the mechanical work to software and put juniors on the analytical work sooner, supervised. That is a change to how a firm develops people, not just how it processes documents, and it is worth planning deliberately rather than stumbling into.
Professional obligations, handled properly
Every profession we work with carries obligations that constrain how AI can be used: confidentiality, privilege, professional standards, independence requirements and, increasingly, client expectations about disclosure. These are design inputs, not risks to be managed after the fact.
- Client confidentiality addressed through enterprise platforms, with the training and retention terms confirmed in writing
- Matter-level access controls, so the system cannot read across a boundary a person could not
- Conflict and independence checks that run before anything else does
- Professional review on every piece of advice or work product that reaches a client
- An audit trail sufficient to answer a professional standards enquiry
- Where client engagement terms require disclosure of AI use, that language written into the templates
The advice stays yours
We do not build systems that give professional advice. The output is always a draft, an analysis or an assembly for a qualified person to review, adjust and take responsibility for.
What is realistic, and what is being oversold
This sector is currently the target of a great deal of aggressive vendor marketing, so let us be direct about where the line sits.
Document review
- Realistic today
- Extracting terms, dates, obligations and differences reliably
- Oversold
- Deciding what the document means for this client
Drafting
- Realistic today
- A strong first draft from your templates and precedents
- Oversold
- Final work product without professional review
Research
- Realistic today
- Finding and summarising material in your own knowledge base
- Oversold
- Citing external authority without verification
Client communication
- Realistic today
- Drafting routine correspondence for approval
- Oversold
- Autonomous client advice of any kind
Administration
- Realistic today
- Onboarding, filing, reporting, follow-up, end to end
- Oversold
| Task | Realistic today | Oversold |
|---|---|---|
| Document review | Extracting terms, dates, obligations and differences reliably | Deciding what the document means for this client |
| Drafting | A strong first draft from your templates and precedents | Final work product without professional review |
| Research | Finding and summarising material in your own knowledge base | Citing external authority without verification |
| Client communication | Drafting routine correspondence for approval | Autonomous client advice of any kind |
| Administration | Onboarding, filing, reporting, follow-up, end to end |
The last row is the interesting one. Administration is where the returns are unambiguous and the risk is close to zero, and it is consistently the least discussed application in the sector, because it is not exciting, and excitement is what gets marketed.
Frequently asked questions
Is it safe to put client documents into an AI system?
Into a consumer AI tool, no. Into an enterprise platform configured properly, with the training and retention terms confirmed, the hosting location known, matter-scoped access and logging in place, it becomes a reasonable proposition. Those terms differ by vendor and tier, so they are worth confirming rather than assuming. Either way it is a materially better position than the current reality of staff using personal accounts because nothing sanctioned exists. That gap is why our governance work and our automation work usually arrive together.
Will this replace our junior staff?
It replaces the work juniors currently learn on, which is a real problem worth naming rather than glossing over. The firms handling this well are moving juniors onto analytical and client-facing work earlier with more supervision, rather than hiring fewer of them. That is a deliberate decision about how the firm develops people, and it is better made on purpose.
Do we need to tell clients we use AI?
It depends on your professional obligations, your engagement terms and the client. The trend across enterprise and government clients is clearly toward asking, and increasingly toward requiring disclosure in the engagement. We would rather help you write clear language into your terms now than have the question arrive in a tender response.
How do we measure the return?
For administrative automation the honest measure is elapsed time and hours returned, not billable recovery. The hours come back as capacity, and what you do with that capacity is a management decision. We agree the metric before the build and instrument the system to report it, so the answer is measured rather than asserted.
More questions answered on the full FAQ.
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Which of those is costing you most?
Tell us which one and we'll tell you straight whether it's worth automating, roughly what it would take, and what we'd do first.