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Predictive Systems

AI made for business

Not vibe-coded. Human Engineered.

Enterprise AI trained on your data, measured against your metrics, and kept private within your network.

EdTech exampleWhich model writes the best question?

Select a dimension to compare the models

DimensionModel 1Model 2Model 3shipped
97.797.597.7
100.085.0100.0
94.695.0100.0
97.597.9100.0
96.598.3100.0
93.192.893.1
95.0100.080.0
100.099.295.0
92.395.697.9
98.899.299.3
98.8100.0100.0
100.0100.0100.0
100.0100.0100.0
99.0100.0100.0
100.0100.0100.0
100.0100.0100.0
51s91s35s

We shipped Model 3. Best or tied on thirteen of fifteen, and 1.5× faster. All three land within 0.2 on the overall score, and up to 20 points apart underneath it.

All fifteen dimensions · Recitation eval, three-way comparison, 8 Jul 2026

Challenges Businesses Face Today

Businesses struggle with AI because it’s difficult to trust.

Nearly 9 in 10 AI pilots never reach production. It is rarely the model that fails. It is everything built around it: results nobody can explain, data that cannot leave your network, and users left to judge it alone.

Why enterprise AI stalls after the pilot
Results nobody can explain
Sometimes it works, sometimes it doesn’t, and nobody can say why. No auditor, regulator or client will sign off on an answer like that.
Data that cannot leave
Privacy and security is the top barrier to enterprise AI, named by 71% of organisations. Most assume your records can go to someone else’s cloud.
Users left to judge it
The tools arrive; the training doesn’t. Spotting a hallucination is a learned skill, and without it nobody can tell a good answer from a confident one.

How we build

Measure it so you can improve it.

Nothing ships on a hunch. We define what “correct” means before we build, in terms specific enough to test. Agentic tools made writing code cheap. Knowing whether it’s right did not, and that judgment stays human.




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