Target Problems5 constraints
Frontier AI is hitting three limits at once — inference cost, long context, and interpretability.
As models scale, today's architectures stay constrained on major fronts:
[01]inference cost eroding frontier-lab marginsUNRESOLVED
[02]serving cost rising rapidly with scaleUNRESOLVED
[03]slow, expensive, inaccurate long contextUNRESOLVED
[04]context windows that can't run long enoughUNRESOLVED
[05]no inherent explanation or interpretability of answersUNRESOLVED
Solutioncapabilities + advantage
SharpEleven is a high-performance architecture that resolves all three at once.
The architecture delivers:
[A1]substantially lower annual inference costENABLED
[A2]longer context, faster and more accurateENABLED
[A3]answers explained inherentlyENABLED
Against other models:
[B1]processes more tokens than any modelBENCHMARKED
[B2]cheaper per token at long contextBENCHMARKED
[B3]faster long-context inferenceBENCHMARKED
[B4]more accurate as inputs growBENCHMARKED
Deployment + AcquisitionTesting + transfer
Deployed for testing as a retrieval system. Architecture transfers in full to a single acquirer.
In market today:
[01]deployed as a retrieval systemLIVE
[02]tested on real long-context workloadsLIVE
On acquisition:
[03]full proprietary architectureON EXIT
[04]transferred to a single acquirerON EXIT