Capabilities · 04 of 04

The pilot worked. It has been a pilot ever since.

The demo was convincing. The proof of concept ran. Then it stopped, somewhere between the finance team asking who checks the output and nobody having an answer.

We take finance agents into production, with the controls, ownership and audit trail that make a finance function willing to rely on them.

Book a 20-minute review How an engagement works

The problem

The barrier is not the technology

Finance AI moves safely into production when every automated decision has a threshold, exception path, approval gate, audit trail and named human owner. TechFarben designs that operating model and builds the workflow around it.

51% of mid-market finance functions have not yet adopted AI.

CFO Alliance, surveying over 250 finance leaders. Among those who have, the reported blocker is consistent, and it is not the models.

Gartner's research on the finance function finds talent shortages and skill deficits, not technology, are now the biggest barriers to progress, with CFOs naming the acquisition of digital and AI talent as their most critical challenge, and describing teams stuck in perpetual pilot programmes.

That matches what we see. The technology usually works in the demo. what is missing is everything around it: who reviews an output the model is not confident about, what happens when it is wrong, what the auditor sees, how a controller explains a number that a system produced.

Finance is not a forgiving domain for probabilistic output. A recommendation engine that is right 95% of the time is a good product. A ledger posting that is right 95% of the time is a material weakness.

So pilots stall in a predictable place; after the capability is proven and before anyone has designed the control environment that would let it run unsupervised.

The question that kills finance AI pilots is not "does it work?" it is "who signs off on it?"

What we do

Three ways in

AI Readiness & Route Evaluation

Which workflows to automate, in what order, and whether AI is the right answer at all.

The most useful thing we do in this area is occasionally tell clients not to do it.

We assess the finance function's workflows against four things: how repetitive the task is, how structured the input, how tolerant the process is of error, and whether a control can be designed around it. That produces a ranked list, and usually a shorter one than expected.

Then, for each workflow that qualifies, we evaluate four routes rather than one:

Improve what you have. A well-configured system with a properly designed workflow solves more than most people assume, at lower cost and lower risk.

Buy third-party. For common processes with mature vendors, buying beats building. We say so, and we do not take vendor commissions.

Build bespoke. Where the process is genuinely specific to your business and no product fits.

Deploy Farben. Our own AI finance agents.

On the fourth route

Farben is our product. We say that plainly, we assess it against the same criteria as the alternatives, and we contract the evaluation separately from any deployment.

If the answer is one of the other three, that's the recommendation you get. An assessment that always concludes in favour of the assessor's product is not an assessment.

Agent Pilot to Production

Working agents inside your systems in eight weeks. Not a sandbox demo.

We build against live data in your environment from the start, because a pilot on sample data proves nothing about the messiness of the real thing.

The build covers capture, processing, decision and posting: documents and inputs ingested and read, confidence scored on every extraction, matching against existing records with configurable tolerances, exceptions routed to a person, and posting into the ledger only after the approval step your controls require.

Hypercare runs alongside go-live because the first month is when the edge cases arrive, and the difference between a pilot that becomes production and one that dies is usually who is watching during that month.

What production actually means

It runs unattended on a schedule. It handles the volume you actually have, not the volume in the demo. It fails visibly rather than silently. Your team can explain what it did. And your auditor can see the trail.

Recent workPetCubes: a support agent capturing customer complaints through chat and creating cases in the ledger with product, batch and order history attached; a pattern-recognition layer scoring complaint frequency and tracing recurring fulfilment errors to source; and a conversational analytics layer answering questions against live commerce and finance data.

In build nowPetCubes: agentic procure-to-pay. Goods receipt captured with scan, photo and weight; documents read with a visible confidence score; low-confidence items routed to a person; three- and four-way matching with configurable tolerances; a consolidated pack for finance and a final approval before anything posts. Every transaction carries an end-to-end timeline.

Controls, Governance & Human Ownership

The reason it reaches production, and the reason it stays there.

This is the part that gets skipped, and it is the part that determines whether the pilot survives contact with the finance team.

We design the control environment around the agent. Confidence thresholds, set per process rather than globally, determining what proceeds automatically and what escalates.

Human validation routing, so the exceptions reach the person who can actually resolve them. Approval gates before anything touches the ledger. Immutable audit logging of every decision, including the ones the system made without asking.

And the piece that matters most over time: making the logic legible. Rules a finance team can inspect and change, a system that reports the decisions it made and why, and no dependency on us to interpret its behaviour.

Ownership is a design question

Every automated step needs a named human owner; not a supervisor of the technology, but the person accountable for the output. Pilots that skip this stall at exactly the moment the controller asks whose name is against the number.

Recent workCharles Monat Associates: a working but opaque automation layer rebuilt so the client's own finance team could inspect and modify the rules directly, with the system reporting the decisions it makes.

How we engage

Three stages. You can stop after any of them.

01 · Diagnose

Fixed fee. Workflow assessment and route evaluation, ranked and costed.

02 · Build

Eight-week build to production, with hypercare through go-live.

03 · Run

Monitoring, tuning and control review as processes and volumes change.

Roughly half of what we assess does not warrant an agent. The evaluation is worth commissioning for that answer alone.

Proof

What this looks like in practice

Live, not piloted

PetCubes: support, pattern-recognition and analytics agents running against live commerce and finance data.

Approval before posting

Agentic procure-to-pay in build with confidence scoring, exception routing and a human approval gate.

Rules the team can read

Charles Monat Associates: automation rebuilt so the finance team can inspect and change the logic itself.

See documented client work →

Questions

Is our data used to train models?

No. And where the deployment sits inside your own environment, your data does not leave it at all.

What happens when the agent gets it wrong?

It should be caught before it matters. Confidence thresholds route uncertain cases to a person, matching tolerances flag anomalies, and nothing posts to the ledger without passing the approval step your controls require.

What will our auditor say?

Ask them early; we'd encourage it. Every decision is logged immutably, approval steps are explicit, and the rules are inspectable. Auditors object to systems they cannot examine, not to automation.

We tried a pilot and it went nowhere. Why would this be different?

Usually because the first attempt proved capability without designing the control environment. That's the work, and it is the reason pilots stall at the same point.

Do we have to use your product?

No. We assess four routes; improve what you have, buy third-party, build bespoke, or deploy Farben; against the same criteria, and we contract the assessment separately from any deployment.

How much of our finance function should be automated?

Less than the market implies. Around half of what we assess does not justify it, either because the volume is too low or because a control cannot be designed cleanly around it.

Start with a conversation, not a proposal

Twenty minutes. Tell us which process you'd automate first, and we will tell you whether we'd agree.

Book a 20-minute review