A forward-deployed software practice .

Software and systems that support judgment.

We embed with your team for short, focused engagements on the front line of the work, then build the systems that make complex decisions clearer. More and more of that is machine learning and AI. The model surfaces the call. Your team makes it.

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Clarity for complexity.

See the applications

Why we exist .

Most organizations aren't short on software.
They're short on software that understands the work.

Critical decisions still run through fragile spreadsheets, scattered documents, ad hoc reporting, and institutional memory. It works until it doesn't: during growth, integration, restructuring, a leadership change, any moment when the stakes rise and the system has to hold.

Reasonable Machines exists for those moments. We turn messy operational reality into systems that are easier to use, easier to trust, and easier to act on.

In production .

Built here. Running in the field.

View all

M&A integration/Workforce plan/Post-close

The plan counted 1,210 exits toward synergy. HR had notified 100.

EXITS WITH NO BACKFILL, PEOPLE In the planNotifiedLeft early 1,210100~1,100 Imported from one exit label. Told by HR. These carry the synergy. Before close, or resigned. Part of a $960K gap. 04008001,200 HR lead: still exits, out of synergy.
The callHR lead
Kept every exit on the books, took the people who left before close out of synergy, and reviewed each suspected resignation one by one.
Why a person
The import read one exit label the same way every time. The HR lead knew who had actually been told.

M&A integration/Reporting lines/46,346 people

The ID says one manager. The name says another. 148 times.

WRONG MANAGER, AGAINST KNOWN ANSWERS WRONG Country onlyName distanceID and name agree 9%99%0 300/3,374314/318 Fills the line. Wrong about 1 time in 11. Fills the line. Almost always wrong. 148 disputed lines held, nothing guessed. 050%100% HR lead confirms all 148.
The callHR lead
Held all 148 on the current manager until each was confirmed. Six lines stay open rather than carry a guess.
Why a person
A looser rule would fill every line. Tested against known answers, it picked the wrong manager 300 times in 3,374.

HR assistant/968 policy documents/Europe

The rule said any pay figure was a leak. That would have cut 88 benefit policies.

EUROPEAN HR POLICY LIBRARY, DOCUMENTS 930grounded out38 KIND OF PAY FIGURE WAS NOW Benefit amounts, 88 docsEuropean base payIncentive and deal pay outoutout ininout Confidential pay out. General policy in.
The callEngagement lead
Kept confidential pay out and let general policy in: award tables, allowance caps, and European base pay. 930 of 968 documents in.
Why a person
A keyword scan finds numbers. It can't tell a glasses allowance from a bonus plan.

Your call .

Org design/12,847 people/41 countries

Nine layers against a peer band of six to eight. What do you do with L8?

The system computes every option. The choice is yours.

Layers
9
Management cost
$196.4M
Manager roles removed
0
System flag
Outside the peer band of six to eight.
HEADCOUNT BY LAYER 9 LAYERS / PEER BAND 6-8 L0L1L2 L3L4L5 L6L7L8 11396 4141,2623,105 4,9182,410628 COO chose

Real decisions, made with systems we built. Clients are anonymized and every figure is changed; the org example is illustrative.
Sectors: process engineering . pharma & bio-pharma . healthcare . asset management . manufacturing  //  Functions: human capital . finance . operations . consulting . program & project management . machine intelligence

Discuss a build like this

What we build .

Practical systems for the parts of the business where the stakes are real.

01

Human capital systems

Org design, workforce planning, talent assessment, change readiness, and synergy tracking. Full stacks for designing and executing organizational change.

02

Finance & operations

Planning, approvals, reconciliation, reporting, and cost visibility. Workflow engines for operational control where spreadsheets stop scaling.

03

Decision dashboards

Clear views of the work: what is changing, what matters, where risk is building, and where leaders need to act. Internal tools that keep human review where it belongs.

04

Machine intelligence, deployed reasonably

Machine learning and AI integrated into real operations, with judgment kept in the loop. Models that explain themselves and surface the call, not models that quietly make it.

How we work .

We deploy forward.

01 / Embed

Start in the room

We begin with the real workflow: the people involved, the decisions being made, the data you actually have, and where friction slows everything down.

02 / Build

Build around reality

A custom platform, a focused internal tool, a decision layer, or an ML and AI integration. Whatever the work needs, built to fit it, not the other way around.

03 / Hand off

The tools stay

Engagements end. The system you are left with is yours: documented, owned by your team, and built to be trusted long after we leave.

How an engagement works

Shape
Embedded with your team, on the front line of the work.
Duration
Weeks, not quarters. Scoped tight, shipped focused.
Team
Senior engineers, many with computational PhDs in physics and math. No bench, no handoff.
Output
A working system, documented and owned by your team.
After
No lock-in, no seat licenses, no dependency on us.

Engagements .

Weeks, not quarters.

We scope tight and ship working software, not slideware. Each engagement starts from a decision your team has to make and ends with a system that helps them make it.

Reasonable Machines .

We build software that understands the work.

Start a conversation See what we build