Custom models engineered for ROI

Predictive, vision and NLP models built around a business metric — not a benchmark — and shipped to production where they earn their keep.

every model targets a KPI — and ships to production

/ what actually counts

Accuracy isn’t all.
Your number is.

A model can top every benchmark and still move nothing. We engineer each one around a business metric you already track — so it earns its keep, not a leaderboard rank.

/ what it’s worth

Every $1 in AI,
$3.70 back

The return is real — but it only shows up when the model moves a metric your business already lives by. That’s the only kind we build.

0.0×

return on every $1 invested in AI

IDC / Microsoft, 2024

$1 in3.7× out

…but only when the model is tied to a number you already track. A leaderboard win returns nothing.

/ the model’s P&L

What a model
is worth

Churn is one example — drag in your numbers and watch the revenue a retention model protects.

50,000
15%
15%
$1,200

Revenue saved / year

$1.4M
1,125

customers retained / year

Cutting churn 15% keeps ≈ 1,125 customers — ≈ $1.4M a year.

/ how a model learns

Signal out of the noise

We don’t chase leaderboard accuracy. We engineer the model that separates the signal that drives your KPI from the noise — and prove it on your real, held-out data.

proven against a baseline, before rollout

/ how we ship it

Built to ship,
from line one

Most models die in a notebook because no one planned for production. We work backwards from the KPI, and every step earns its place on the way to a live, monitored model.

1

Frame the KPI

Start from the number you want to move — not a model type.

2

Baseline

Measure today's cost and performance, so impact is provable.

3

Engineer & train

Features, model and tuning — on your real, messy data.

4

Validate

Held-out, real-world tests against the baseline before rollout.

5

Ship & monitor

Into production with drift detection and a retraining path.

/ what we build

Three families, one standard

/ where it pays

Models that move
real numbers

The same engineering, pointed at the decisions that cost or earn the most. A few of the places it lands hardest:

01

Forecasting & demand

Right stock on the shelf — less waste, fewer stock-outs.

02

Churn & retention

Spot at-risk accounts early and save the revenue.

03

Fraud & anomaly

Catch the bad transaction before the loss lands.

04

Vision & quality

Find the defect a tired human eye would miss.

05

Docs & language

Turn hours of reading into an instant answer.

06

Pricing & risk

Price each deal to the margin it actually carries.

/ why it pays

Outcomes you can measure

92%

model accuracy

Predictive models

Forecasting, churn, demand and risk models tuned to your decisions and data.

10×

faster inspection

Computer vision

Detection, inspection and OCR that automate what humans can't do at scale.

60%

less manual review

NLP & search

Classification, extraction and semantic search over your unstructured text.

AMDIM delivery · McKinsey 2024 · AMDIM engagements — verified, not invented.

/ what we deliver

End-to-end, not half-built.

Predictive & forecasting models
Computer vision
NLP & document AI
Recommendation & personalisation
Model evaluation & validation
Production deployment

/ before you commit

Fair questions

Whatever ships fastest and safest — we fine-tune proven open models when we can, and build bespoke only when the problem demands it.

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