An accelerator is a working system built for how your business operates. It’s yours. It solves one problem exceptionally well and is built to deliver measurable business value.
Most AI is sold as a platform: buy the license, adapt your business to the software, hope adoption follows.
An accelerator is the opposite. It starts from something specific and expensive that your business does badly or slowly today, and it removes that constraint. It’s built on the best available technology, โ often Microsoft and Anthropic, โ but it’s shaped entirely by your operations.
You own it. It does one thing very well. And you know exactly which line on the P&L it’s supposed to move.
Your board is asking about AI agents. Here’s the honest answer.
An AI agent is software that takes a multi-step task, works out how to carry it out and does the work across your systems. When it reaches something, it shouldn’t decide alone, it stops for a person.
Two distinctions worth making, because the market blurs both:
Not Automation
An agent is not the automation you already have. Traditional automation follows a fixed path someone defined in advance. An agent can handle cases nobody anticipated and knows when to ask for help.
Not a Chatbot
An agent is not a chatbot. A chatbot answers a question. An agent does the work.
Where agents earn their keep
An agent is not the automation you already have. Traditional automation follows a fixed path someone defined in advance. An agent can handle cases nobody anticipated and knows when to ask for help.
Where they don’t
Low-volume, high-stakes decisions; anything where being wrong occasionally is unacceptable and there’s no practical review step. We will tell you when you’re in that category.
Agents are a building block. Accelerators are the outcome
Some of what we build is agentic. Some is far simpler, because the problem didn’t need an agent. We choose based on what the work requires, not on what the market is currently excited about โ and part of being a steward of your investment is not selling you the expensive version of a cheap solution.
Where accelerators
pay off fastest
What Changes
BEFORE
14 days
avg. close cycle
Quarter-end bottlenecks delay reporting, leaving leadership without timely data for critical decisions.
AI-Powered Close Engine
Continuous reconciliation with intelligent anomaly detection
AFTER
Real-time
continuous close
Finance teams shift from data gathering to strategic analysis, closing books on demand.
BEFORE
3โ4 days
per document batch
Document queues accumulate faster than teams can process, creating compounding operational debt.
Intelligent Document AI
Multimodal extraction with confidence scoring
AFTER
< 12 mins
end-to-end processing
Processing velocity scales with volume โ thousands of documents handled with zero headcount increase.
BEFORE
100% Manual
collections workflow
Revenue leakage from delayed collections strains cash flow and requires constant human oversight.
Autonomous AR Intelligence
Predictive collection with dynamic escalation paths
AFTER
92% Auto
fully automated
Collections run continuously across every account โ humans focus only on high-value exceptions.
How we decide what to build
We don’t build everything possible. We build what pays
Every candidate accelerator is assessed on four questions your FDE answers during the AI Opportunity Assessment:
What is this costing you today?
In hours, errors, delay, or lost revenue.
What will it take to build?
Honestly, including the data work.
Will people use it?
The best system nobody adopts is worth zero.
What’s the payback period?
If we can’t answer this, we don’t recommend it.
You see the list ranked by return. You choose.
The AI agent questions worth asking
Whatโs the difference between an AI agent and automation?
Automation follows a script. An AI agent can figure out what comes next, work across systems and handle the exceptions. When it hits something that needs a human, it knows to stop and ask.
What can AI agents actually do for a business?
Start with the work nobody wants to do. Process invoices. Build quotes. Move orders. Chase information. Prep the close. The best AI agents aren’t always flashy. They just give your people a lot of time back.
Should we build a custom AI agent or buy something off the shelf?
Whichever makes more sense. We don’t build custom just to build custom. If a product already solves the problem well, buy it. If your workflow is what makes the problem unique, that’s when custom starts to earn its keep.
How do we know if an AI agent is worth building?
Do the math before the build. What does the problem cost today? What will it cost to solve? Will people use it? How fast does it pay back? If the numbers don’t work, neither do we.