Nov 13, 2024 · 23m · the-pitch
AI Needs Feedback Intelligence. Will Chinar’s Pitch Seal the Deal?
Chinar Movsisian · 6m spoken Elizabeth Yin · 3m spoken Josh Muccio · 3m spoken Mac Conwell · 1m spoken Jesse Middleton · 1m spoken Cyan Banister · 1m spoken Charles Hudson · 56s spoken
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
On this episode of The Pitch, founder and machine learning expert Chinar Movsisian pitches Feedback Intelligence, an LLM observability platform designed to diagnose generative AI errors. Following an extensive evaluation of her company's pivot history, burn rate, and valuation, investor Elizabeth Yin extends a $150,000 investment offer at a $10 million cap.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Josh holds 16.6% of the talking time here. How this is scored →
Josh as informed peer 6.2 Guest teaching 4.0 Guest disagreement 2.7 Josh pushing back 5.2
speaking balance: gold is Josh, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 9:53 Chinar pushes back on valuation resistance
When Mac states that her valuation expectation is too pricey, Chinar immediately pushes back by asserting that he can negotiate.
Hardest push from Josh ▶ 13:51 Elizabeth challenges rapid increase in burn rateElizabeth directly challenges Chinar on why she intends to increase burn to 100K so quickly after burning through nearly an entire seed round on a failed initial product.
Biggest teaching moment ▶ 5:32 Chinar explains technical feedback orchestrationChinar educates the investors on her novel system combining LLMs and unsupervised learning to convert raw thumbs down feedback into automated engineering tickets.
Josh holds their own ▶ 15:48 Elizabeth calculates path to ramen profitabilityElizabeth uses detailed financial modeling on the fly to dismantle Chinar's 100K burn projection and show that the company could actually reach breakeven.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | Josh as informed peer | Guest teaching | Guest disagreement | Josh pushing back | Why |
|---|---|---|---|---|---|---|
| Pitch Presentation: Feedback Intelligence for AI Applications | 5 | 6 | 2 | 4 | Elizabeth and Jesse press Chinar on how her technology practically handles AI thumbs down feedback. Chinar educates them on her novel LLM orchestration and unsupervised learning architecture. | |
| Chinar's Technical Background and Academic Pedigree | 5 | 5 | 1 | 3 | Mac inquires about Chinar's pedigree and customer acquisition strategy. The room responds very favorably to her impressive credentials and high initial contract value of 90K. | |
| Company Pivot History and Current Fundraising Terms | 6 | 4 | 4 | 6 | Mac scrutinizes Chinar's previous round and pivot, expressing resistance to her implied 13.5 million valuation expectation. Chinar stands her ground and invites active negotiation. | |
| Elizabeth Yin's Analysis of Burn Rate and Path to Breakeven | 8 | 3 | 3 | 7 | Elizabeth aggressively examines Chinar's burn rate expectations, pointing out that jumping from 15K to 100K monthly burn is risky after burning through a previous round. Elizabeth maps out a path to breakeven on the fly. | |
| Elizabeth's Investment Offer and Pitch Conclusion | 7 | 3 | 2 | 5 | Elizabeth makes a disciplined offer of 150K at a 10M valuation cap, which Chinar accepts for further discussion. In post-pitch debriefs, the investors analyze her technical strengths versus weak command of financial projections. | |
| Post-Pitch Follow-Up Call Between Elizabeth and Chinar | 6 | 3 | 4 | 6 | In a post-pitch check-in, Elizabeth warns Chinar about surviving market cycles and skepticism around unconverted waitlists, while Chinar defends her product-market fit. |
Statements from this episode (18)
Movsisian: No analytics solution exists for generative AI user feedback
“There is no solution to handle this for AI slash generative AI.”
Movsisian: Feedback Intelligence secures three pilot customers and ten waitlist signups
“We've validated this problem solution feed, and we locked three pilot customers. We have also 10 customers on the wait list, and it's growing.”
Movsisian: Feedback Intelligence projects $1.5M ARR by the end of next year
“We are going to start the paid pilots first of July, aiming to convert them to annual contracts end of this year with 300 to 60 K ARR. And we are going to onboard 14 more customers aiming to have 1.5 million ARR by the end of next year.”
Movsisian: Pilot customer Cogniz receives over 1,000 AI feedback messages daily
“So, one of the pilot customers that we have, they called Cogniz. They are providing conversational AI, LLM-based solutions to financial institutions, and these, like, financial analysts are using those, like, solutions for querying business transaction in Q-IV…”
Movsisian: Feedback Intelligence converts unstructured feedback into actionable engineering steps
“We have developed a novel solution. It's an orchestration of LLMs plus unsupervised learning where we are able to turn this unstructured thumbs up, thumbs down to a list of actions.”
Movsisian: Feedback Intelligence commands an average $90K annual contract value
“So right now it's like on average where we charge them 90 K annual contract.”
Middleton: Generative AI observability startups typically hope for only $9K ACV
“Most people in this space are hoping to get 9000.”
Movsisian pivoted to LLMs despite landing Fortune 500 computer vision client
“A year and a half ago, I started to build computer vision evaluation tool. And then with this generative AI market adaption, even though I got one of the Fortune 500 media companies as a customer, I saw like, this is not being scaled as I want. That's why we s…”
Movsisian: Feedback Intelligence has $1M in soft commitments toward $2M round
“We have one million soft commitment already.”
Movsisian targets $2M raise for 15% dilution, roughly a $13.3M valuation
“Yeah, I mean, we are comfortable to be diluted, like, around 15% for this two million.”
Conwell: Feedback Intelligence's target valuation of $13.3M is a bit pricey
“That is a bit pricey for me.”
Conwell: AI feedback intelligence is an overlooked problem poised for growth
“I can believe this is a problem that not many people are paying as much attention to just yet, but people will.”
Hudson: Precursor rarely backs infrastructure unless the non-technical value is clear
“We don't do that many things on the infrastructure side. And when we do, it's usually like something that I can, where I can understand the value prop as someone who's not deeply technical.”
Yin offers $150K at a $10M valuation to Feedback Intelligence
“For me, I would want to invest a 150 K at 10, but I realize that may not be interesting to you.”
Hudson: Burning initial capital with low results raises the burden of proof
“It certainly raises the burden of proof. Like, you were given a million, and this is what you did. Now I have to believe that two million will be invested differently than the original million.”
Hudson: Precursor avoids AI infrastructure due to technical complexity and intense competition
“So like, just as a front level, we're just not doing much in AI infra, just, I don't have the technical ability to analyze them, and I haven't found many interesting categories where I don't instantly meet two dozen very credible teams.”