Insights
Patterns from building AI products across industries. What works, what doesn't, and why most AI projects fail at the system level — not the code level.
28 articles
AI Sprint Handoff: The Template We Send Every Client
The exact template we use for AI development sprint handoffs: five sections covering model performance, data findings, and client decisions. Copy it.
Dharini S
The Weekly Demo Template Clients Actually Look Forward To
The exact invite copy, agenda card, and follow-up note I use for every AI sprint demo, plus why clients actually look forward to ours.
Dharini S
Why I Don't Use the Word 'AI' in Discovery Calls Anymore
Our PM stopped saying 'AI' in the first 15 minutes of discovery calls. Here's what she says instead, and why the proposals that follow are sharper.
Dharini S
The 30-Min Discovery Call: Scoping Before a Proposal
What I need to surface in 30 minutes before I'll write a proposal vs. a brief. The 6 decisions that have to land, and why most discovery calls miss them.
Dharini S
AI Maintenance Costs in Year 2: What Most Founders Miss
Five maintenance costs that don't appear in Year 1 AI budgets: model deprecations, prompt drift, monitoring, human review queues, and reliability work.
Dharini S
How Fertilia Hit 5,000 Weekly Impressions With Zero Ad Spend
A PM's account of the 5-week Fertilia Health AI content engine build: 102 posts, zero ad spend, 109 consultation clicks. Five months later: 50,000+ weekly impressions, 9× the week-5 milestone. Week-by-week delivery story.
Dharini S
Day 1 Discovery: 5 Questions Before I Quote an AI Project
The five questions our PM asks every founder before quoting an AI project. How each one shapes scope, timeline, and a number you can stand behind.
Dharini S
How We QA AI Products (It's Not Like Normal Testing)
LLM testing requires a different mindset than normal QA. The 4-part framework our PM uses before every AI product demo and production launch.
Dharini S
The AI Project Status Update Our Clients Actually Read
We've sent 200+ AI project status updates. Here's the exact template our clients read, respond to, and forward to their teams — and why the standard engineering update format fails with non-technical stakeholders.
Dharini S
How I Run an AI Kickoff Meeting (Template Included)
My full template for running an AI project kickoff: the pre-read questions, 5-part agenda, and what I do in the 48 hours after.
Dharini S
Why AI Projects Run Over Budget: Lessons From 20+ Builds
Five real reasons AI development projects exceed budget. Patterns from 20+ builds, a budget allocation template, and the estimation gaps we stopped making.
Dharini S
How We Estimate AI Projects: The Formula We Use
Two-stage AI project estimation: four cost components, three multipliers, and the template our PM uses on every build. With real numbers.
Dharini S
We Audited 57 AI Blog Posts to Google's Quality Rater Rubric
Full audit of 57 AI-assisted blog posts against Google's January 2025 Quality Rater Guidelines. Rubric, scores.csv, and what failed all public.
Anil Gulecha
What Good AI Delivery Looks Like: Our Definition of Done
The 5-dimension checklist our PM uses to define done in every AI sprint, with real examples of what trips teams up and how to recover.
Dharini S
Client Communication Template for Every AI Sprint
Four structured messages our PM sends in every AI sprint. Kickoff, mid-sprint signal, demo prep, and decision capture templates you can copy.
Dharini S
Scope Creep in AI Projects: How I Manage It
How to handle scope creep in AI development without saying no. Conversation scripts, sprint structure, and the change-request process that preserves trust.
Dharini S
How a Finished Project Turned Into a Second One
A PM's account of what makes clients return for a second AI project: the delivery patterns, the trust moments, and what actually gets remembered.
Dharini S
Discovery Call Checklist: Scoping AI Projects in 30 Min
How our PM structures a 30-minute discovery call for AI projects, what questions are unique to AI builds, and what the brief looks like after.
Dharini S
SEO for Therapists: How Private Practices Get Found on Google
Most therapists rent their Google presence through Psychology Today. Here's how an AI content engine produces clinical-grade SEO that ranks for condition + approach queries, ethically. Same architecture we ship for healthcare clients.
Abraham Jeron
SEO for Doctors: How We Got a Practice to #2 on Google
How we helped a women's health practice go from zero Google presence to 5,000 weekly impressions and 109 consultation clicks in 5 weeks, then 50,000+ weekly impressions by week 10. No ad spend.
Dharini S
How We Run Weekly Demos (And Why Clients Love Them)
Every AI sprint ends with a live demo, not a status update. The exact 30-minute format we use, what we prepare for, and why live software beats slides every time.
Dharini S
What Clients Underestimate About AI Product Costs
Token bills, API charges, and the hidden costs that surprise first-time AI builders. A PM's breakdown of what shows up on the invoice at scale.
Dharini S
Why I Don't Commit to Timelines on New Requirements
Our PM's process for handling timeline questions on new AI work: why she waits 24 hours, what she analyzes, and how to keep clients confident during the pause.
Dharini S
5 Questions I Ask Every Client Before We Write a Single Line of Code
The five questions our PM asks every client before engineering starts. What they reveal about scope, data readiness, and project success.
Dharini S
The Handoff Document We Send After Every Sprint
The exact handoff document Kalvium Labs sends after every sprint: the 5 sections, a real example, and the 15-minute rule that keeps AI projects aligned.
Dharini S
First 48 Hours of an AI Build: The PM Perspective
From 'let's go' to first sprint: the hour-by-hour reality of kicking off an AI development project, what goes wrong, and how we handle it.
Dharini S
200 AI Engineers: What It Means for Delivery Speed
What 200 AI engineers and 6,000 engineering hours per week means for your project. Pod structure, trade-offs, and delivery math explained.
Dharini S
Why Most AI Products Fail: It's Not a Technology Problem
We've seen 50+ AI projects fail. Here are the 5 patterns that kill products before launch: scope creep, wrong model selection, no evals, premature scale, and integration debt.
Rajesh Kumar