The Algebra Group
AI at TAG

We use AI heavily. We also sell the person who checks it.

AI comes up in almost every finance conversation now, so we will be direct about where we stand. We are an advanced, AI-forward firm, and we are also the reason the numbers still make sense at the end. Both are true, and together they are the whole point of how we work.

How TAG actually uses AI

An AI-forward firm, in practice, not in a tagline.

We run paid subscriptions across the leading AI tools available today. We train the whole team on them every month, and we build our own agents and bots for internal and client use. Used well, AI accelerates work: model building, data structuring, and first-pass analysis all move faster. We are transparent about that, which is why we charge for the value prepared, not the hours worked.

Where AI speeds us up
  • Building and stress-testing financial models
  • Structuring and cleaning large data sets
  • First-pass analysis and drafting
  • Commentary inside the client dashboard
  • Internal agents and bots that we develop ourselves
TAG's operating system

A real-time financial dashboard, built for CFOs.

A white-labeled, live financial ecosystem: real-time KPIs, P&L, balance sheet, cash flow, 13-week forecast, variance, and budget views, with an AI commentary and chatbot layer, connected by API to QuickBooks Online (Xero next on the roadmap) and delivered under the partner's brand.

Accounting bench

AI Agents for the Accounting Bench

Purpose-built tools for transaction categorization, reconciliations, and quality checks on books, so the team clears routine volume faster and spends its hours on exceptions.

FP&A bench

AI Agents for the FP&A Bench

Agent-driven financial modeling, research, and presentation build, including monthly models updated end-to-end by agents, with a Vice President applying final review before it reaches the client.

Dashboards

AI-Generated Dashboards and Analysis

Dashboards and analyses built by AI with a written insight layer on top, so the output arrives already explaining what moved and why.

Automation as a Launchpad for New Partner Service Lines

A dedicated AI engineer plus prebuilt agents, playbooks, and templates let a partner firm launch a new offering in weeks instead of building infrastructure from scratch.

Where AI falls short without human review

AI gets the work most of the way. Not all of the way.

A model can produce a number that is arithmetically fine and makes no sense. It takes an experienced person to catch that and flag it. That review layer is exactly what we sell.

Completeness

The last mile of a task, the edge cases, and the parts a prompt did not think to ask for.

Formatting

The client-ready polish, the house style, and the small things that signal the work was done with care.

Continuity

Preserving prior work instead of quietly overwriting it, so nothing you already got right is lost.

Sanity

The judgment to see a figure that is technically correct but obviously wrong in the real world.

Our philosophy

Machines for speed. People for judgment.

We are not choosing between AI and people, and neither should you. The value is in the combination: the tools do the heavy lifting, and an experienced person makes sure what comes out is something you can put your name on in front of your client. That is the layer that does not automate away, and it is the layer we are built around.

AI does

Speed, structure, first-pass analysis

People do

Completeness, formatting, continuity, sanity

You get

Work you can put your name on

AI and your data

Sensitive by default. AI only with your consent.

Finance is sensitive, and we treat it that way. We use AI tools only with your consent and, where relevant, your end client's, never by default on sensitive data. For the most security-conscious engagements, we work entirely inside your systems or on locked-down machines you provide, and we sign an NDA whenever you want one.

What that means in practice
  • AI is used only with your consent, never by default
  • Work inside your systems where you prefer
  • Locked-down, client-provided machines when required
  • NDA signed on request
Answering common automation concerns

New technology has never removed the work. It moves people up.

Every time a new technology arrives, it frees up bandwidth to do more analysis, more scenarios, and more perspectives. We are not in competition with that shift. We are building it ourselves.

Won't AI just build the model itself?
It will get you most of the way, and we use it to do exactly that. But it misses completeness, formatting, continuity, and sanity, and it will hand you a number that is arithmetically fine and makes no sense. The person who catches that and fixes it is what you are actually paying for.
Won't bookkeeping be fully automated anyway?
Probably a lot of the transactional layer, yes, and we agree rather than argue. Every technology shift has freed up bandwidth rather than removed the work, moving people up into analysis and advisory. We build the automation and position our people as the layer on top of it.
Won't financial modeling and FP&A work be fully automated anyway?
Probably a lot of the first-pass build, yes, and we use AI agents to do exactly that. But every model still needs someone to test the assumptions, catch what doesn't hold up under scenarios, and make sure the output actually supports the decision it's meant to inform. We build the automation and keep our people as the review layer above it.
Won't CIMs, teasers, and deal materials be fully automated anyway?
Probably a good part of the drafting and formatting, yes, and we don't fight that shift. But a CIM still needs someone who understands the deal story, the buyer's perspective, and what needs to be emphasized or left out. We use automation to move faster on the build, while our people stay responsible for the judgment behind it.
Won't screening and deal analysis be fully automated anyway?
Probably a lot of the initial screening and data structuring, yes, and we build that automation ourselves. But deciding which opportunities actually fit your thesis and which numbers deserve a second look still needs someone who understands the context. Automation moves the volume faster; our people stay the layer that applies the judgment.
Will AI replace bookkeepers?
AI will undoubtedly automate more of the transactional bookkeeping layer. But we do not see that as a reason to oppose AI. Rather, it allows finance teams to spend more time on resolving exceptions, improving accuracy, interpreting results, and supporting advisory work. This indicates that the role changes rather than simply vanishing.
How does TAG use AI in its accounting work?
We use AI across transaction categorization, reconciliation preparation, quality checks, data structuring, financial modeling, first-pass analysis, reporting, dashboard commentary, research, and internal workflows. We also build our own agents and automation for accounting and FP&A work. The purpose is to speed up execution while maintaining an experienced human review in the process before work becomes client-ready.
What tasks still need human review in automated accounting?
The areas where human review is most important are completeness, formatting, continuity, and sanity. This human review layer becomes more important as automation speeds up the process. Clients are not paying only for immediate outcomes; they're paying for trust in those outcomes.
See it for yourself

Start the trial.

The best way to evaluate the combination of speed and judgment is to see the output for yourself. Start with our trial and decide whether the final deliverable is something you would be comfortable putting your firm's name on.

Book a 30-minute discovery call