AI Models, Ranked for Business
Most business owners have no real way to know if they're using the right AI model or overpaying for the wrong one. The information exists, but it's written by engineers for engineers.
So we built a version for business owners. It checks the newest benchmarks every morning and tells you which model fits your work and what it costs to run.
Turn a chaotic, engineer-facing benchmark landscape into one honest, business-legible answer, refreshed daily with zero manual curation.
Cherry-Picked Scores
Every model is the best at something, if it picks the test. The numbers are real. They also don't compare to each other and weren't written for anyone but engineers.
Wrong Kind of Good
Topping a leaderboard and being good at your work are two different things. A model can win every benchmark and still botch the job you'd actually hire it for.
Name Chaos
One model, six names, a dozen versions, depending on where you look. Match the wrong two and the comparison is already junk.
Outdated on Arrival
Models get swapped and prices drop every few weeks. Last month's pick is this month's mistake, and nothing tells you it changed.
Every project starts with one question:
buy, automate, build, or wait?
We find the software that already solves it, vet it against how you work, and get your team running on it. Live in days.
We connect the tools you already use so the manual steps disappear and data moves on its own.
We design and develop something custom from the ground up. Built for how your business runs, owned by you.
We tell you when holding is smarter. You leave with a clear reason and a date to revisit.
Why We Choose Build
- ✗ Buy failed: every existing leaderboard is built for ML engineers, with no business lens and no way to combine sources.
- ✗ Automate failed: no amount of connecting existing tools produces a defensible cross-source score. The scoring engine itself had to exist.
- ✗ Wait failed: the landscape moves weekly. Waiting is how a recommendation goes stale.
Before we recommend anything, we score the project on three things: how unique the work is, what a mistake costs, and how much of it runs on human judgment.
The pipeline computes; the human approves.
Everything is computed from live data. Nothing is hardcoded.
Ingest
Adapter frameworkEvery morning, 11+ source adapters pull fresh data: official APIs, HuggingFace CSVs, leaderboard JSON, even a Markdown table inside a GitHub README. Each source carries a trust weight and health telemetry.
Resolve
Model-identity matrixEvery source identifier resolves against an explicit, auditable alias table. Unmatched identifiers land in a candidate queue with AI-suggested mappings, and a human clicks Accept. It grew from 61 hand-seeded rows to 1,500+ resolved identifiers with zero manual entry.
Normalize
Multi-source scoring engineElos, percentages, and hallucination rates are min-max normalized to a single 0-100 scale, then combined by per-source trust weights. Popularity signals are structurally excluded. Popularity is not competence.
Weight
Business lens in the databaseCompetency is a weighted average of per-use-case sub-scores, and the weights live in the database, per industry. Change a weight in the admin and every public chart recalculates with zero code change.
Approve
Pending-models queueNever-seen models wait in a queue for human approval, merge, or rejection. On approval, the prior version in the same family is automatically demoted, so "latest only" filters stay truthful without curation.
Publish
SSR public siteThe interactive Competency-vs-Cost chart, model detail pages, side-by-side compares, and the Find Your Model wizard all render from the same live dataset. Pick an industry and the chart redraws instantly, client-side.
Generate
Scribe content pipelineAutomated topic discovery and article generation produce the day's AI news, then pass automated QC: near-duplicate merging, link restriction, and a symmetry check that blocks one-sided comparison pieces.
Before vs. After
| Metric | Manual research | With the AI Hub |
|---|---|---|
| Answering “which model for my task?” | Hours across contradicting leaderboards | One business-weighted chart, filtered by industry |
| Data freshness | Weeks-old blog posts and reports | Rebuilt from 11+ primary sources every morning |
| Score comparability | Elos, percentages, and rates that cannot be averaged | One normalized 0-100 score with a per-source breakdown |
| Model identity | Same model, six different names | 1,500+ identifiers resolved to a ~58-model catalog |
| Landscape coverage | Whatever someone last wrote up | 209+ benchmark-grounded use-case scores, 90+ QC-passed articles at launch |
| Adapting to change | Rewrite the report | Weights, sources, categories, and copy all editable without a deploy |
The Technical Build
PHP 8.3, PostgreSQL 16, JWT auth. Owns the schema (35+ migrations), the scoring engine, all ingestion, and the content pipeline. 89 backend tests; admin endpoints built TDD.
App Router with a hybrid strategy: one full-dataset fetch per request seeds a React context, then all interactivity is client-side with zero further round-trips. Custom SVG chart engine, no charting library. Zod contracts on every API response.
17+ admin surfaces rendered from one declarative registry: one grid component, one dynamic form, routes generated from config. Adding a surface is a configuration entry, not a feature build.
An abstract SourceAdapter with shared primitives, one short focused class per source, trust weights and health telemetry editable from the admin. A source dying is a data event, not an engineering event.
Laravel Scheduler orchestrates the full cascade: scoring, source sync, pending queue, topic discovery, article generation, and QC. Human review only where judgment is genuinely required.
Have a workflow that changes faster than you can keep up?