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.
| Dimension | Model 1 | Model 2 | Model 3shipped |
|---|---|---|---|
| Overall scoreThe average across all fifteen dimensions below. The one number that hides everything under it. | 97.7 | 97.5 | 97.7 |
| Follow-up fitDoes the follow-up match what the student actually did: affirm a correct answer, clarify a partial one, re-engage a wrong one? | 100.0 | 85.0 | 100.0 |
| ConversationalDoes it read like a tutor speaking aloud, rather than a written exam paper? | 94.6 | 95.0 | 100.0 |
| No answer leakCan the answer be read straight off the question? One leak fails the whole case. | 97.5 | 97.9 | 100.0 |
| Objective alignmentIs the question about the objective it was tagged with, rather than something next to it? | 96.5 | 98.3 | 100.0 |
| Probe qualityDoes a deeper follow-up stay on the same idea, or wander to a different one? | 93.1 | 92.8 | 93.1 |
| ScaffoldingWhen a student stumbles, does the follow-up break the question into a smaller step, without handing over the answer? | 95.0 | 100.0 | 80.0 |
| Cultural alignmentDo the everyday references, money, names, places, units, fit the locale the assessment was written for? | 100.0 | 99.2 | 95.0 |
| Procedural, not computationalIn maths and science, does it ask the student to talk through the method rather than compute a number? Recitation is spoken. | 92.3 | 95.6 | 97.9 |
| Answer plausibilityDoes the model answer sound like a student saying it out loud, rather than a textbook paragraph? | 98.8 | 99.2 | 99.3 |
| Standalone contextDoes each question make sense at the point it is asked? The first one has no earlier question to lean on. | 98.8 | 100.0 | 100.0 |
| Age appropriateAre the vocabulary and sentence length calibrated to the grade it was written for? | 100.0 | 100.0 | 100.0 |
| Difficulty accuracyDoes the real difficulty match the easy, medium or hard label the question carries? | 100.0 | 100.0 | 100.0 |
| Language consistencyIs every part of the question in one language, and the language the source material is in? | 99.0 | 100.0 | 100.0 |
| Subject styleDoes the question fit how the subject is actually asked about? History invites cause, literature invites interpretation. | 100.0 | 100.0 | 100.0 |
| Bloom accuracyDoes the question demand the level of thinking its objective asks for, rather than settling two levels below? | 100.0 | 100.0 | 100.0 |
| Time per caseWall clock to generate one full assessment, averaged over the run. | 51s | 91s | 35s |
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.
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.
- 01
Define the goal
What is the problem? What does success look like?
- 02
Set the metrics
What should we measure? What is the ground truth?
- 03
Build
Agentic AI builds and iterates. An engineer decides what ships.
- 04
Measure
Results are scored against real work. Detailed and explainable.
- 05
Improve
Analyze the patterns, fix the weak dimensions.
Repeat
Every change is re-scored for performance gains before it ships.
Who we are
We build. We deploy. We teach.
We build
CUSTOM AI SYSTEMS
AI systems whose decisions you can audit, on infrastructure you control. Built for institutions that have to answer for the outcome.
Learn moreWe ship
OUR OWN PRODUCTS
better-ed and BRAD are in daily use across schools, companies and law firms. We run what we build.
Learn moreWe teach
AI ENGINEERING TRAINING
Hands-on training for teams adopting AI. The same evaluation-first methods we use on our own products.
Learn moreWe are proud partners of
Case studies
Four systems, four industries, in production.
Site selection
An AI-driven decision support system that helps businesses choose optimal branch locations by analyzing market demand, competitor data, and operational feasibility.
Read the case study
Document extraction
An intelligent OCR-based solution that automates expense tracking, financial reporting, and document management, reducing manual work and human errors.
Read the case study
Oral assessment
An automated grading assistant that evaluates answers, provides instant feedback, and simplifies quiz checking and results tracking for educators.
Read the case study
Legal drafting and research
A smart legal system that automates document drafting, case tracking, and research, ensuring accurate and efficient workflows for legal teams.
Read the case study
Supercharge Your Business with AI!
Stay ahead of the competition with AI-powered solutions. Whether you’re just starting or looking to scale, our cutting-edge technology can help you achieve more.