Three ways I can help

Productized offers with clear scope, timelines, and deliverables. No vague "AI consulting." You know exactly what you get.

From $7,500
AI Evaluation Sprint
10–14 days
Define failure modes, build your first evaluation set, create rubrics, and leave with a repeatable scorecard for your AI product.

What you get

  • Failure mode analysis across your AI workflows
  • Custom evaluation dataset with judge rubrics
  • Pass/fail scoring logic and regression baseline
  • Repeatable quality scorecard you can run on every release

Teams about to launch an AI feature and need to know if it actually works.

From $15,000
Agent Benchmark Harness
4–8 weeks
Custom benchmark workflows, MCP/tool-use scenarios, pass/fail logic, judge rubrics, and a dashboard for regression testing your AI agents.

What you get

  • Scenario-based benchmark suite (multi-step, tool-use)
  • MCP server or API harness with deterministic validation
  • LLM-as-a-Judge rubric scoring for open-ended tasks
  • CI-compatible regression testing and comparison dashboard

Teams shipping AI agents with tool-use, multi-step reasoning, or complex orchestration.

From $5,000
Rapid AI MVP
2–4 weeks
For founders who need to ship a working AI product fast — but with testing and quality instrumentation built in from day one.

What you get

  • Production-ready AI-powered application
  • Built-in evaluation and quality monitoring
  • High test coverage (94%+ target)
  • Deployed with CI/CD and observability

Solo founders and small teams who need speed without sacrificing quality.

Free: AI Launch Readiness Checklist (26 questions to answer before you ship)

Not sure which offer fits?

Book a free 20-minute call. I'll listen to what you're building and tell you honestly whether I can help.

78
benchmark scenarios designed
43
tools in one MCP harness
100K+
LLM-judge evaluations/day
CL-bench
published benchmark delivery

Frequently asked questions

How much does an AI evaluation sprint cost?

An AI Evaluation Sprint at fazeel.ai starts at $7,500 and takes 10–14 days. It delivers a representative eval dataset, LLM-judge rubrics calibrated against human labels, pass/fail scoring logic, and a regression baseline you can run on every release.

How long does it take to build an agent benchmark harness?

A custom agent benchmark harness typically takes 4–8 weeks. A reference build: a 43-tool MCP harness with 78 scenarios across four difficulty tiers, deterministic validation of every tool call, and CI-compatible regression gates.

Has your benchmark work been published?

Yes. fazeel.ai delivered benchmark tasks included in Tencent’s published CL-bench (arXiv:2602.03587) and CL-Bench Life (arXiv:2604.27043), and has merged contributions in inspect_ai (UK AI Safety Institute) and EleutherAI’s lm-evaluation-harness.

Do you work remotely with US companies?

Yes. fazeel.ai is based in Hyderabad, India and works async-first with US teams, with regular overlap into US Eastern and Pacific hours. Engagements are fixed-scope and fixed-price, governed by a written agreement.

What do I own at the end of an engagement?

Everything: the eval datasets, judge rubrics, harness code, dashboards, and documentation are handed over in your repositories. There is no vendor lock-in and no proprietary runtime dependency.

Still deciding? Start here

Deep dives on each service and honest guides for the build-vs-buy-vs-hire decisions behind them.

AI Evaluation Consultant

What an evaluation consultant does, deliverables, and when to bring one in.

Agent Benchmarking Services

Custom benchmark harnesses: scenarios, deterministic validation, CI gates.

Hiring Guide: AI Evals Engineer

In-house hire vs. marketplace vs. specialist — honest costs and timelines.

DeepEval vs. Custom Harness

When an open-source eval framework is enough, and when it is not.

The AI Agent Evaluation Handbook

The full map: scenarios, tool-use benchmarking, judges, CI gates, and staffing — every deep-dive linked.