AI for professional services, built for the 5–100 person shop.
Law, accounting, insurance, consulting, financial advisory: firms that bill for judgment and lose too much of the week to everything wrapped around it. The examples below lean legal, because law makes the stakes plain — but the pattern holds across the desk professions.
Where does AI actually pay in professional services?
Three places, in our experience. Each one is drawn out below as a before-and-after — illustrative vignettes, not client claims. In every one, the machine drafts and chases; the person who knows the client decides. How that handoff gets taught is the subject of Teaching the Machine Your Business.
The request is qualified before it reaches your best people.
Before. A referral emails the managing partner: “Possible commercial dispute, wants to talk this week.” No entity names, no opposing party, no dates. A senior associate spends forty minutes on the phone collecting basics and twenty more writing them up — none of it billable — and the conflicts check has not even started. Multiply by every request in a month.
After. An intake agent reads the email and replies within minutes, asking for the facts the firm always needs: the parties, the matter, the timing, the deadline driving it. It checks the names against your conflicts list, drafts a one-page summary, and routes the matter to the right practice group. Whether to take the client is still a partner’s call. The agent just makes sure the call starts informed.
The win: senior time starts at “should we take this,” not “what is this.”The work gets chased without the month-end scramble.
Before. It is the 28th. The office manager exports unbilled time from one system and open invoices from another, reconciles them in a spreadsheet, and drafts reminders one at a time — softening some, skipping the awkward ones entirely. A handful of invoices quietly age past sixty days because chasing them is nobody’s favorite job.
After. An automation watches unbilled time and open invoices every day. It drafts each reminder in your voice, matched to the invoice’s age and the client’s history, and queues the routine ones for one-click approval. The exceptions — the disputed bill, the client a partner has known for twenty years — get flagged to a person with context instead of fired off blind. Nothing sends without approval.
The win: receivables get chased every week, and no client ever hears from a robot.Every matter shows the same status everywhere.
Before. A client calls and asks where things stand. The document system says one thing, the billing system says another, and the real answer is three weeks deep in a partner’s inbox. Someone reconciles the three by hand, the client waits until tomorrow, and the same reconciliation happens again next month.
After. An integration keeps matter status in one place. When a filing lands, an invoice pays, or a deadline moves, every system shows it — the same record, everywhere. “Where does this stand” has exactly one answer, and anyone at the firm can give it in under a minute, including the person answering the phone.
The win: status questions cost a minute, not an afternoon.Illustrative vignettes, not client claims. What we’d actually scope is whatever your free assessment shows is worth automating first. The one build we can name is MARCUS, a private AI back office for a regulated lender.
For most firms the right first move is the AI Readiness Audit: two to four weeks mapping where the hours actually go before anyone builds anything. For a firm shaped like yours, that usually means four questions — where intake time goes, what unbilled work is aging, which systems disagree about matter status, and which documents get rebuilt from scratch when they should be assembled. Two redacted pages of a real audit deliverable are published at the sample audit, so you can read the document before you spend a dollar. Pricing is published, in plain numbers: audits run $3,500–$8,500; sprints $12,000–$45,000, fixed quote in writing.
What this costs for a firm your size.
Our ROI calculator models professional services with two round assumptions: $4,000 a year of repetitive manual work per employee, and $2,000 a year of lost revenue per employee — capacity your people cannot bill because the wrapper work eats it. Here is the model worked for a 25-person firm.
Round numbers, stated in the open.
$4,000 per employee per year in repetitive manual work: intake write-ups, billing reconciliation, status chasing. $2,000 per employee per year in lost revenue: hours that could bill but never do. These are the same figures the calculator shows; nothing is hidden in the math.
$150,000 on the table.
Twenty-five people × $4,000 puts $100,000 of manual work in reach. Twenty-five × $2,000 adds $50,000 in recoverable capacity. Total year-one opportunity: $150,000.
+$132,000 projected, year one.
At this size the model prices the Implementation Sprint at $18,000 — inside the published $12,000–$45,000 range, and fixed in writing before work begins. $150,000 minus $18,000 leaves +$132,000.
A model, not a promise. The savings are assumptions you can see above, not your books. The whole model is published — with what breaks it — in the ROI math guide. Run it at your own headcount → Then we run your real numbers in the free assessment.
If your first objection is confidentiality, you are thinking about this correctly. A firm holding privileged files has the same problem as a lender holding borrower files — and a lender is the strongest analogy we can offer. MARCUS, the system we built for B:Side Capital, runs entirely on the client’s own hardware: PII is stripped before any model reads a document, and every action writes to a tamper-evident log. That is the pattern a regulated firm should demand from any vendor, ours included: ask where the model runs, what it sees before redaction, and who can prove what happened afterward. A vendor who cannot answer those three in plain English has answered them. Where your files go, and where they should never go, is the subject of Where Your Data Goes.
AI for professional services firms.
Want the numbers?
The full price list is published — audits, sprints, managed services, all on one page.
Read the price list →01What can AI do for a professional services firm?+
Usually the intake and coordination work — qualifying requests, summarizing, routing, and prepping the repetitive documents — so your people spend time on judgment, not handoffs.
02Is our client data safe?+
It can be built so nothing leaves your office. MARCUS — the system we built for B:Side Capital, a regulated lender — runs entirely on the client's own hardware: PII is stripped before any model reads a document, and every action writes to a tamper-evident log. A firm holding privileged files can ask for the same pattern.
03Do we need to replace our practice management software?+
No. We build an integration layer on the practice management, billing, and document tools you already run. Nothing gets ripped out; the systems you have start talking to each other.
04What happens to billable-hour economics?+
The hours you bill are judgment hours, and those stay human. What the machine takes is the non-billable wrapper — intake write-ups, chasing invoices, reconciling status — so more of the week is billable in the first place. On flat-fee work, the same recovered capacity shows up as margin.
05How much does it cost?+
The AI Readiness Audit runs $3,500–$8,500; an AI Implementation Sprint runs $12,000–$45,000, quoted fixed in writing before work begins.
06How long does it take?+
An audit is 2 to 4 weeks; a build is 4 to 12.
07Do you work remotely or on-site?+
We run two hubs — Denver, Colorado and Phoenix, Arizona — for in-person work, and we work remotely with businesses across the US. Same method, same fixed prices either way.
08Is there a real example?+
The closest published case is MARCUS — 14 AI agents we built for B:Side Capital, a regulated lender.