We build you private models — combining the economics of open source with the precision of fine-tuning to deliver frontier-level results on your highest-volume AI tasks. A model that's yours, trained on your data, on infrastructure you control.
01 / The Reality
Our specialty is high-volume, structured work where you already know the expected output shape — one fixed prompt, short outputs, millions of calls.
Open-ended conversation, coding, research, agentic reasoning — anything where every request is different — stays with the frontier models. They win there, and we'll tell you to keep them. Our router even escalates your unusual cases to them automatically.
"Use frontier models for exploration. Use ModelForge for production execution."
02 / Value Projections
Pick the API model you use today — its published token prices, a matched open-weights replacement, and that model's GPU prefill automatically. Every field stays editable, and the math updates live.
Projected Impact
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Estimated monthly savings
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Annualized savings
Directional, not a quote. The token prices and GPU rates above are real and current; the throughput and utilization figures are honest estimates. Every workload is different — prompt sizes, traffic shape, accuracy bar — which is exactly what the fixed-price pilot is for: it replaces every estimate on this page with numbers measured on your data, before you commit to anything.
03 / The Blueprint
We tune a small open model on your task using labeled examples you choose to share — your historical labels, or answers generated by the API you already pay for. Hours of GPU time, not weeks.
Head-to-head against your current model on a held-out test set you define, tuning knobs only on a separate dev split. Per-class results and confusion matrices before anything ships.
The model answers with a calibrated confidence score; the unsure slice escalates to your frontier API automatically. Served behind an OpenAI-compatible endpoint — switching is a one-line change.
04 / The Method
Frontier-level is a claim we prove, not assert. Every engagement begins with a benchmark you can interrogate.
Head-to-head, on your data Your fine-tuned model against the API you use today — identical prompts, held-out test set your team defines.
Honest methodology Tuning decisions are made on a separate development set, never on the data we report. Baselines run the way a skeptic would run them.
The report is yours either way If your current setup wins, the benchmark says so and you keep the full analysis. Deployment only happens after the numbers do.
A 30-minute call is all it takes to identify one high-volume pipeline and prove the cost savings on your actual data — a fixed-price pilot, and the report is yours either way.
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