The Question

“Should we use R&D credit software, or work with a firm?”

This question almost always comes up once a company decides the R&D credit is worth exploring.

And it’s a fair question.

Software promises speed, lower cost, and simplicity.
Firms promise expertise, judgment, and defensibility.

The problem isn’t that one option is “good” and the other is “bad.”
The problem is that most comparisons stop at price, instead of starting with complexity.

The Short Answer

R&D credit software and human-led studies solve different problems.

Software works best when:

• the facts are clean,

• the work is repetitive,

• the structure is simple,

• and the risk tolerance is low.

Human-led studies become more valuable when:

• judgment matters,

• facts require interpretation,

• documentation needs context,

• or the credit will be scrutinized.

The mistake is treating them as interchangeable.

What R&D Credit Software Gets Right

R&D software didn’t emerge by accident. It exists because it does several things very well.

Structure and Consistency

Software enforces a framework.
It asks the same questions every time, applies the same logic, and produces consistent outputs.

For companies with straightforward development cycles, this structure can be an advantage — especially if no one internally knows where to start.

Speed and Efficiency

For teams with clear records and well-defined projects, software can dramatically reduce the time required to calculate a credit.

There are fewer interviews, fewer iterations, and fewer moving parts.

Lower Upfront Cost

Software is typically less expensive than a human-led study, at least on the surface. For companies with modest credits and simple facts, that tradeoff can make sense.

Repeatability

Once set up, software can be reused year over year with relatively little friction — assuming the underlying work doesn’t change much.

Where Software Starts to Break Down

The strengths of software are also its limits.

Judgment Is Hard to Automate

R&D credits are not purely mechanical. They require judgment about:

• what constitutes technical uncertainty,

• where experimentation actually occurred,

• and how to distinguish qualifying work from execution.

Software can ask questions — but it can’t challenge assumptions or sense when answers don’t align with reality.

Nuance Gets Flattened

Early-stage teams, hybrid roles, internal tools, and evolving architectures don’t fit neatly into standardized inputs.

When nuance is forced into predefined boxes, the result is often:

• overstated qualification, or

• understated opportunity.

Neither is ideal.

Audit Defense Is Not Just a File

If a claim is reviewed, the issue isn’t whether a report exists — it’s whether the story behind the numbers holds up.

Software can generate outputs, but it can’t explain why decisions were made or how uncertainty was resolved. That explanation usually lives outside the platform.

What Human-Led Studies Do Better

Human-led studies shine where interpretation matters.

Contextual Understanding

A human can ask follow-up questions, notice inconsistencies, and understand how the business actually operates — not just how it fills out a form.

This matters most when:

• work spans multiple teams,

• roles overlap,

• or projects evolve over time.

Defensibility Over Optimization

Experienced practitioners tend to focus less on maximizing percentages and more on building claims that hold up under scrutiny.

That often results in credits that are:

• slightly smaller,

• but significantly more durable.

Interpretation of Gray Areas

Many R&D decisions live in gray space. Humans can evaluate tradeoffs, weigh facts, and document rationale in a way software simply can’t.

This becomes critical for amended claims, multi-state credits, acquisitions, or companies with prior claims history.

Where Firms Can Miss the Mark

Human-led studies aren’t automatically better.

Firms can:

• overcomplicate simple situations,

• apply heavy processes where lightweight ones would suffice,

• or introduce unnecessary cost for straightforward claims.

In some cases, firms default to depth when breadth would be more appropriate.

That’s not a failure of people — it’s a mismatch between approach and complexity.

The Real Decision Isn’t Software vs. Humans

It’s simplicity vs. complexity.

Software tends to work best when:

• projects are well defined,

• teams are stable,

• documentation already exists,

• and risk tolerance is low.

Human-led approaches become more valuable when:

• facts require interpretation,

• documentation must be reconstructed or contextualized,

• the credit is material,

• or the company expects scrutiny (audits, diligence, amended claims).

Choosing based on price alone almost always leads to regret — in one direction or the other.

The Takeaway

R&D credit software and human-led studies aren’t competitors in the way most people think.

They’re tools for different situations.

The right question isn’t:

“Which one is cheaper?”

It’s:

“Which approach matches the complexity and risk profile of our business?”

When companies choose based on that lens, outcomes improve dramatically — regardless of which path they take.

That’s the difference between claiming a credit and understanding what you’re claiming.