Dhavan Shah

Production AI agents, under control.

Fail-closed budgets, approval gates, evaluations, and grounded company memory — installed before agents run unsupervised.

UZH MSc · Kaggle Top 0.5% · Independent practice

Engagements

Fixed-scope work for Series A to C teams who already feel agent cost or trust risk. You get written deliverables, not open-ended billing.

Short engagement

Agent Reliability Audit

$3,500–$7,500  ·  1–2 weeks

Architecture and workflow review. Cost and loop risk. Tool permissions. Eval and failure-mode report. Budget and approval recommendations. Prioritised 30-day roadmap and a walkthrough.

Ask about Audit

Delivery

Fail-Closed Agent Pilot

$10,000–$25,000  ·  4–8 weeks

One controlled production workflow: pre-call budget enforcement, permission policy, human gates, claim checks, audit trail, evaluation, and deployment notes.

Ask about Pilot

Ongoing

AI Reliability Retainer

from $3,000/mo  ·  typically $3–8k

Architecture reviews, evaluation maintenance, cost incident analysis, policy updates, and limited implementation when new agent workflows appear.

Ask about Retainer

Fit check

Best when the agent risk is already real.

Scoped work is strongest when there is a production or near-production workflow, a technical owner, and enough signal to audit behavior.

Good fit
  • Agents are touching spend, customer-facing answers, internal memory, or operational workflows.
  • You can share architecture, traces, prompts, policies, or failure examples under an agreed scope.
  • You want a written audit, one hardened workflow, or a small reliability retainer.
Not a fit
  • Idea-stage demos with no workflow owner or production path.
  • Open-ended staff augmentation without a defined reliability outcome.
  • Requests that need invented client logos, compliance claims, or inflated impact numbers.

Approach

How engagements run

A clear sequence so you know what you are buying before any code lands in your stack.

1. Fit call

Thirty minutes on production risk, stack, and whether an audit or pilot is the right first step.

No obligation

2. Audit

Written findings on spend loops, gates, evals, and memory. The product is the report, not a slide deck.

1–2 weeks

3. Pilot

Optional. Harden one workflow with fail-closed budgets and evidence. KazenAI patterns when they fit your stack.

Scoped delivery

4. Retainer or product

Only when the same need repeats: ongoing reliability work, or design-partner modules (FinOps, Brain).

Optional

Ready? Start with a fit call.

I reply within one business day.

Why teams trust this work

Top 0.5% Kaggle Expert
UZH MSc Data Science
DevRev Applied AI · LLM workflows
Zurich AXA XL · Sulzer
Dhavan Shah
Background

I run an independent practice on agent reliability: cost control, approval gates, evaluation, and grounded institutional memory.

Agent FinOps, Agent Brain, and Agent Lens are the reference systems behind that work. Academic foundation: MSc Data Science, University of Zurich; Kaggle Competition Expert (top 0.5%).

I take a small number of scoped engagements with teams who already feel the cost or trust risk of agents in production.

shah.dhavan09@gmail.com  ·  LinkedIn  ·  GitHub

Request a fit call

I take a small number of engagements. Typical first step: a 30-minute fit call. I reply within one business day.

Thanks. I will reply within one business day.
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