< Date >

< Customer >

A short pre-read for our call

Ship AI agents you can trust

TL;DR On Harvey LAB, we optimized a DeepSeek V4 Flash verifier:
as accurate as the default per-criterion grading, about 430x cheaper.

Ahmad Beirami · Co-founder / CEO

We have deep expertise in
frontier model research & enterprise AI

Fidian is a research-driven product company with decades of combined experience building/shipping AI.

Gemini model alignment via controlled decoding and InfAlign; 2x inference speedups via speculative decoding

Led cross-org prototype of first tool-using LLM agent (2020); responsible AI research, adversarial evaluation, red teaming

A decade-plus of AI/ML engineering building production-scale ML systems

Delivered first RL-driven play testing (bug discovery, balance tuning) plus production ML engineering

Architected AIOps for NGFW (now Strata Cloud Management); 30+ years backend systems, SRE leadership

First enterprise-grade data pipelines, ML-driven dependency mapping, regression frameworks

Educational background (PhD)

The future of development

Fidian builds a self-evolving agent platform by solving reliable verification first

1 · Verifiers

Frontier-level accuracy at a fraction of the cost.

Today

2 · Maintainable evals

Your evaluations regenerate from your product specs, so they stop rotting as your product evolves.

3 · Harness learning

Your harness improves automatically against those evaluations without overfitting. The optimized harness is distilled into a new model, and the loop continues.

Fidian’s self-evolving agents platform

You maintain the spec; the platform evolves evals and the harness.

Why verification first

A flawed verifier
lets a worse agent outscore a perfect one

Interactive · dragHarvey LAB · 1,251 tasks · all-pass scoring (all criteria must pass) · simulated error sweep

Verifier accuracy

verifier accuracy = 100%

Perfect agent (true 100%) Bad agent (true 20%) true all-pass rate

Optimizing against flawed verification leads to reward hacking.

Measured results

We reached near-ceiling accuracy
at a fraction of the cost

Verifier quality: share of real failures caught vs share of correct work accepted

Verifier cost per 1,000 criteria (USD)

As accurate as the default per-criterion grading at about 430x lower cost.

Verifiers measured against silver labels (frontier consensus) on Harvey LAB.

Collaboration proposal

01

We improve Harvey’s evaluation lifecycle: faster, cheaper, more accurate.

02

This reliable signal enables model post-training and learning better harnesses.

03

Harvey’s product itself becomes faster, cheaper, and more accurate.

Ship AI agents you can trust

We have deep expertise infrontier model research & enterprise AI

Fidian builds a self-evolving agent platform by solving reliable verification first

A flawed verifierlets a worse agent outscore a perfect one

We reached near-ceiling accuracyat a fraction of the cost

Collaboration proposal

We have deep expertise in
frontier model research & enterprise AI

A flawed verifier
lets a worse agent outscore a perfect one

We reached near-ceiling accuracy
at a fraction of the cost