LLMOps
LLMOps boring on purpose.
Most AI work fails not at the demo but at the second deploy. The prompt that worked Friday breaks Monday because the upstream API drifted, the input distribution shifted, or the new model release subtly changed the contract. LLMOps is the discipline of catching those before customers do.
- Golden datasets + automated evals on every prompt / model change.
- Drift detection: input distribution, output distribution, refusal rate.
- Canary deploys with cost + quality gates before full rollout.
- Prompt registry with semantic version + rollback in one click.
“A demo is a promise. A production system is a balance sheet. My job is to close the gap — in weeks, not quarters.”
§ 02 / Authority
From the roomsI've been in — and the feeds that came out of them.
Live shorts. LinkedIn originals. Unedited.
Read all 11 field notes →
PMI Gujarat · Keynote · Dec 2024
I had the incredible opportunity to be part of the PMI Gujarat Chapter — NEXTGEN Project Management, elevating our world through emerging technologies.
Live · this quarter
48,000+
LinkedIn impressions
- Field notes11
- Industries20
- Years shipping15+
Karnavati · AI Conclave 2025
Unlocking the Power of AI: it's all about the right prompt. At Karnavati University, I had the privilege of sharing a key insight…
Rajkot · Keynote · 2025
What happens when AI meets India's social and rural future? You get a room full of students, academics, and leaders in Rajkot asking the questions that actually matter.
PM Forum · Off-script · 2025
I walked into a room of 45+ project managers. No slides. No deck. No script.
- PMI Gujarat
- Karnavati University
- Atmiya AI Summit
- HP WeRise · Ahmedabad
- Project Managers' Forum
- NIFT Gandhinagar
- MeitY · Digital India
- UIT Karnavati
- Ahmedabad founder circle
- PMI Gujarat
- Karnavati University
- Atmiya AI Summit
- HP WeRise · Ahmedabad
- Project Managers' Forum
- NIFT Gandhinagar
- MeitY · Digital India
- UIT Karnavati
- Ahmedabad founder circle
What this means in practice
We instrument first, optimise second. Every change to a prompt, model, or tool runs through the eval suite before it touches production. Failed evals halt the deploy.
- 01
Eval framework: 50–500 golden tasks per critical agent, regression-tested every PR.
- 02
Observability stack — Langfuse / Arize / Helicone / custom (picked by your data-residency posture).
- 03
Cost dashboard — token-spend per feature, drift alerts when a feature's cost-per-request creeps.
- 04
Deploy pipeline — canary → 10% → 50% → 100%, automatic rollback on quality regression.
Engagement shapes
2 ways to engage on LLMOps. Pick the closest fit; we calibrate scope on the diagnostic call.
01
2 wk
LLMOps audit
Read the current state of evals, observability, and deploy hygiene. Output: a prioritised remediation plan + risk register.
02
6–8 wk
Embedded LLMOps build
Embedded with your team. Set up evals, observability, deploy pipeline, runbooks. Hand off the keys when the first regression catches itself.
For
Who this fits
- Teams already running ≥ 1 LLM-powered feature in production who keep getting bitten by silent regressions.
- Engineering leads accountable for an AI feature's SLA who don't have an eval framework yet.
- Compliance / risk owners who need a defensible audit trail for AI behaviour.
Not for
Honest gates
- Teams that haven't shipped an LLM feature yet — start with the build sprint, then bolt LLMOps on.
- Anyone hoping evals will magically write themselves — golden datasets are a human craft.
FAQ
About this service.
Do we need to switch our observability tool?
No. We work with Langfuse, Arize, Helicone, OpenTelemetry, Datadog, or your own. The eval framework is more important than the dashboard — we set that up first and pipe traces wherever your platform team already operates.What does the eval harness actually look like?
TypeScript / Python project, version-controlled with the prompts. Each agent or feature has 50–500 golden tasks (input + expected output rubric). CI runs the full suite on every prompt change; failures block the deploy. Probabilistic checks for fuzzy outputs — score-based, not pass/fail.How long until we have golden datasets?
Rough first pass in week 1 (10–30 tasks per critical agent, drawn from the worst recent failures). Production-grade in 2–4 weeks (50+ tasks, including adversarial / edge cases). Continuous additions thereafter — every production incident becomes a regression test.Can you work with us if we use OpenAI / Anthropic / both / Bedrock?
Yes — model-agnostic. The harness layer hides the differences. We've shipped against Anthropic Claude, OpenAI, Google Gemini, AWS Bedrock, Azure OpenAI, and self-hosted (vLLM / Ollama). Multi-provider routing is a real production pattern, not a maybe.What about cost optimization?
Token-spend dashboard per feature, drift alerts when cost-per-request creeps. Common wins: prompt compression, context summarisation between turns, semantic caching, prompt routing to cheaper models for simple tasks. We've seen 40–80% spend reductions without quality loss when the dashboard exists.Do you cover prompt-injection defences?
Yes. Eval suite includes red-team prompt-injection tests as a baseline. Runtime: input sanitisation at the boundary, tool-call allow-lists, output validation. We don't claim 100% coverage; the goal is to make injection unprofitable, not impossible.
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Voices from the rooms
48,000+ impressions · 11 notes · 9 venues
PMI Gujarat · Keynote · Dec 2024 Karnavati · AI Conclave 2025 Rajkot · Keynote · 2025 PM Forum · Off-script · 2025
Next step
Tell me what you’re trying to ship.
One brief, one inbox, one reply within a business day. Either a calendar slot, a referral, or a straight no — we don’t bench-fit work that isn’t a fit.