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Aceleron Intelligence
07Results

Honest numbers. Defensively engineered systems.

Almost everyone has adopted AI. Almost no one sees the payback. We exist to close that gap, with systems that reach production and keep their own score. We frame proof as what the category achieves and what your build can target, then publish your own results once your system is live.

Market-level data. Your own results measured and published once live.

01What the category achieves

What your build can target.

These are published market and category numbers from comparable AI deployments, labeled clearly as representative outcomes, not Aceleron client claims. Your own results get measured by the Aceleron Dashboard and published here once your system is live.

01Representative outcomes
Published failure-rate studies, AI in production
Category average, contract and document tools
Published category data, 12-week boutique window
Published category data, same delivery window

Representative - market and category level

02The method

Defensive engineering: we control for the top causes of failure.

Roughly 88% of AI projects fail entering production. The top causes are scope creep (34% of failures) and data quality (27%). Neither is a surprise if you look for them early. Aceleron is built to look for them early.

Cause · 0134% of failures

Scope creep

We lock scope in process mapping before the build begins. If a new idea surfaces, it queues for the next engagement cycle.

Cause · 0227% of failures

Data quality problems

We assess data health during discovery. If the input is dirty, we fix it or design around it explicitly, not at deploy time.

Cause · 03

No human checkpoints

Every automation specifies by design which outputs require a human review. Consequence, not the team's mood, drives when people stay in the loop.

Cause · 04

Accuracy compounds badly

A step at 85% accuracy chained with another at 85% delivers 72% overall. Each step in an Aceleron build targets 95% or above. The difference stacks fast.

03The accuracy rule

Step accuracy compounds. The gap is wider than it looks.

When steps are chained, accuracy multiplies. A 3-step workflow at 85% per step delivers 61% overall. The same workflow at 95% per step delivers 86%. That 9-point gap per step becomes a 25-point swing in your outcome.

Aceleron designs each step to hit 95% or above, which is why we invest in sandboxed testing, adversarial inputs, and grounding in your own verified data before anything touches production.

03Multiplicative accuracy
  • 3-step workflow at 85% per stepPoor result: 61% overall
  • 3-step workflow at 95% per stepGood result: 86% overall
  • 5-step workflow at 85% per stepPoor result: 44% overall
  • 5-step workflow at 95% per stepGood result: 77% overall

Illustrative - multiplicative accuracy math

04The method in the work

The numbers come from how we build, not from luck.

The same defensive steps run on every engagement. These are the four where the failure causes above get controlled, drawn from our full 7-step delivery process.

02Step 02

Process mapping

We document each workflow in plain language before a line of code is written. Scope is locked, integration points are identified, and data quality is assessed up front.

03Step 03

Design with safeguards

Each automation is designed with verification checkpoints and human-in-the-loop steps at the points where consequences are highest. No guesswork in production.

04Step 04

Sandboxed build

We build in an isolated test environment first. The workflow runs against a copy of your data so errors are caught before they touch a real client file or live record.

05Step 05

Adversarial testing

We deliberately throw edge cases, bad inputs, and off-topic questions at the system to verify it fails gracefully and escalates to a person instead of guessing.

05A note on our stage

Early adopters get senior attention and a public case study.

Aceleron is early-stage. We do not have published client case studies yet. That means early clients work directly with the partners, get the most careful build attention we offer, and their results become the first published proof on this page once the system is live.

If you want to be an early client and have your results on this page before others, that conversation starts with the free audit.

05Where we stand
  • Published client case studiesNot yet
  • Numbers on this pageRepresentative
  • Who you work withThe partners
  • Your resultsMeasured, then published

Honest by default

06See where the hours are going

Book a free audit. We run the payback math with your numbers.

We show you the category benchmarks specific to your workflows, then measure your own results from day one.