For Agencies
ARC helps finance teams at marketing, creative, digital, PR and software development agencies see utilization, profitability and forecasts clearly, with less time spent assembling reports and more time on the decisions behind them.
Book a Discovery CallA 30-minute call. No pitch, no pressure.
Utilization by practice
Monthly revenue, $M
Client profitability, year to date
| Client | Revenue | Margin | Hours over scope |
|---|---|---|---|
| Client A | $1.42M | 31% | +4% |
| Client B | $1.10M | 24% | +11% |
| Client C | $0.86M | 18% | +19% |
| Client D | $0.74M | 9% | +27%Overserviced |
| Client E | $0.52M | 22% | +6% |
Problems We Solve
Renewals priced on the hours the work really takes, value achieved, and client willingness to pay.
Your pricing, revenue per head and discounting against firms of your size and mix.
Margin by client and retainer, while the work is still running.
One forecast tied to pipeline and renewals, updated as the data changes.
Billable hours by person, team, practice and client, against target.
The Result
Your most experienced finance people make key financial decisions in minutes, where it used to take days or weeks.
Services
Pick an engagement to see what it includes.
Included deliverables
Scoping & Success Criteria
Define the question, the decision it informs, and what success looks like before any work starts.
Research & Data Synthesis
Market, company, and internal data pulled into one clean, sourced fact base.
Analysis & Modeling
The core analysis and models behind the answer, built to be traceable and easy to update.
Executive Readout
A decision-ready deck and walkthrough for leadership, with a clear recommendation.
Discovery Workshop
Working sessions with your team to map how the work gets done today and where the time goes.
Use-Case Prioritization
Candidate AI use cases ranked by value, feasibility, and data readiness.
Data & Systems Audit
What data you have, where it lives, how clean it is, and what it will take to use it.
ROI Business Case
What it costs to build and run, what it saves, and whether it is worth doing.
AI Roadmap
Which use cases to tackle first, in what order, what each one needs, and a realistic timeline for each.
Working Prototype
A usable version your team can test with real inputs, built in weeks.
Evaluation & Testing
Outputs checked against real examples, with accuracy measured and failure cases documented.
Implementation Roadmap
What it takes to run this use case in production: a go or no-go call, the integrations and data work still needed, governance, cost to build and run, and a timeline.
Workflow Automation
Repeatable AI workflows that take a process from data pull to finished output.
Systems Integration
Connections to the systems you already run, like your CRM, ERP, data warehouse, or document store.
Knowledge Base Setup
Your documents and data organized so AI can search and cite them reliably.
Agent & Prompt Design
The instructions, guardrails, and handoffs that make AI behave consistently.
Dashboards & Reporting
The views your team actually uses, connected to live data.
Governance & Data Privacy
Access controls, review steps, and data handling rules that fit how your firm manages risk.
Documentation & Handoff
Runbooks and walkthroughs so your team can run and extend what we built.
Weekly Working Sessions
A standing session with your team to keep priorities, numbers, and decisions moving.
On-Demand Analysis
Quick-turn analysis and small builds when a question comes up between meetings.
Monitoring & Iteration
Ongoing tuning as usage grows, so accuracy and adoption keep improving.
Building AI Agents
Design and ship agents that pull data, run the analysis, and draft the deliverable, with the right tools and guardrails.
Agent Teams & Orchestration
Multi-agent workflows where specialist agents research, model, and review each other's work in parallel.
Agent Harnesses & Evals
The harness around the model: context, tool permissions, test sets, and evals that prove an agent is ready for client work.
MCP & Data Connectors
Connect agents securely to your CRM, ERP, data warehouse, and document stores through MCP servers.
Context Engineering & Skills
Turn your firm's methods, templates, and know-how into reusable skills every agent and analyst can call on.
Agentic Coding for Analysts
Use coding agents like Claude Code to build models, dashboards, and automations without waiting on engineering.
Pick at least one deliverable.
Email my selectionTimelines are suggested. Each engagement is adapted to its scope.
Your Systems
This is not a complete list. We work with most time tracking, PSA and accounting systems.
Case Study
Mid-market PR agency
Built an AI application that produces the board financial performance report. Built-in tests and cross-checks verify every number against the source data, so the figures the board sees are never hallucinated. Reporting that took about 15 hours now runs in 30 seconds.
FAQs
Under hourly billing or hours-based retainers, revenue falls by about the same share as the hours AI removes, unless the freed time is resold. Under fixed or output-based fees, the same efficiency becomes margin. We map which of your services AI is actually speeding up, model revenue and margin under your current pricing, and identify which offers to reprice first, before clients raise it at renewal.
Be direct about where you use AI, and move the conversation from hours to outcomes. Under hourly billing, every hour AI saves comes straight out of revenue, so a blanket discount is the wrong answer. We help you identify which work AI is actually speeding up, reprice it on deliverables rather than time, and decide whether clients see the savings as more scope or a lower fee, so faster delivery shows up as margin.
Most firms should run a mix rather than switch wholesale, matched to how predictable each service’s scope is. Repeatable work suits fixed fees or output-based retainers, open-ended work often stays time and materials, and value-based pricing fits only where outcomes can be measured and you have real pricing power. We reprice your last 12 to 24 months of projects under each model, so you can see the margin effect by service before you change anything.
A service is becoming a commodity when clients compare it line by line, competitors quote similar prices, or AI can now do much of the work. We look at your win rates, discounting and hours per deliverable over time to find the ones losing pricing power. Those usually move to fixed or packaged fees priced on the output, while senior, judgment-heavy work such as strategy and senior client counsel keeps premium pricing.
Judge your rates by what you realize, not by your rate card, because write-offs and overservicing often hide the real gap. That gap is usually uneven: some services are underpriced while others sit at the ceiling. We benchmark your realized rates by role and service against firms of similar size and mix, so you know where you have room to raise prices and where you don’t.
Tie the increase to the work, not the calendar: show the client how scope, hours and seniority have grown against the current fee, and pair the increase with a clearer definition of what the retainer covers. We compare each retainer’s fee with the hours actually delivered, find where the gap is largest, and help you build an annual increase clause into renewals so the conversation doesn’t start from zero each year.
Only if the price steps back up by contract; otherwise it becomes your new rate. Retainers tend to renew from the discounted base, so a 15% cut in year one can carry through every renewal while scope keeps growing. We model the client over three years at the discounted and full rate, using your actual delivery hours and renewal history, so you can see the margin you are giving up, set a floor and a step-up date before you negotiate.
Often, usually on retainers, and standard reports rarely show it. Senior people fill gaps on retainers that were priced for junior teams, and margin erodes quietly. We match timesheet hours by role against what each engagement was priced to carry, to show where your staffing mix and your fees are out of line, and whether to fix the price, the team or the scope.
Managing Partner
I spent close to a decade in management consulting at Grant Thornton's strategy arm, advising companies from the Fortune 500 to the mid-market.
Clients have included Microsoft, Cisco, Moody's, Aon, Gallagher, Truist Bank, Hub International, AssuredPartners and Maximus.
I also built my own consulting SaaS platform, a knowledge management system for professional services firms, and served as a Board Director for Cause Effective, a New York City nonprofit consulting organization.
ARC brings that experience together with hands‑on AI implementation, so your team gets senior judgment on a timeline measured in weeks and walks away with workflows it can run on its own.
Book a discovery call with Ken
AI & Data Science
I’ve spent a decade building AI and data science systems in regulated industries, most recently as AVP of Data Science at MetLife and before that at Revantage, a Blackstone company.
At MetLife, I turned around a claims auto-adjudication program, more than doubling the auto-adjudication rate, and reviewed 300+ global AI use cases on the enterprise AI Architecture Review Board.
At Revantage, I built a real estate data science practice from scratch, including forecasting work presented to Blackstone’s board.
I put together the agentic systems and machine learning behind workflows, so the analysis your team relies on is tested, reliable, safe, and ready to run in production.
Get Started
A 30-minute discovery call. Bring your current reporting and the questions your partners keep asking. We'll talk through what a diagnostic would cover and whether ARC is the right fit. No pitch, no pressure.
Book a Discovery Call