Results at a glance
- 1 marketing position closed
- ₹30,000 total recruitment fee
- Payment linked to releasing the offer
- No annual recruitment-platform subscription
- No percentage-based agency commission
- A focused, relevance-led candidate search
About Flapico
Flapico is an LLMOps platform that helps teams build more reliable applications powered by large language models.
Developing an LLM application involves more than connecting to a model and writing a prompt. Prompts evolve, model behaviour changes and outputs that perform well during development may fail when exposed to real users and production data.
Flapico gives AI product and engineering teams a structured environment to manage, version, test and evaluate prompts. It enables teams to compare prompt and model configurations, run tests against datasets, analyse outputs and replace subjective experimentation with measurable evaluation.
By separating prompts from the application codebase and enabling collaboration around testing and evaluation, Flapico helps teams improve LLM applications without relying entirely on engineering deployments or manual trial and error.
Its mission is straightforward: help teams make LLM-powered applications more reliable before those applications reach customers.
The challenge: finding a marketer who could understand LLMOps
Flapico did not simply need someone who could run digital campaigns.
It needed a marketer capable of understanding a technically complex product and translating its value into language that would resonate with AI founders, product leaders and engineering teams.
The person needed to understand why prompt versioning matters, how LLM evaluation differs from conventional software testing and why AI teams need a repeatable way to measure application quality.
This required an uncommon combination of capabilities:
A generic search for marketing candidates would have generated a large number of profiles without necessarily producing the right person.
Flapico needed a marketer who could enter technical product discussions, understand the customer’s problem and turn that understanding into positioning, content and demand-generation programmes.
Why Flapico chose Huzzl.ai
Huzzl.ai is an agentic AI recruitment platform built around candidate relevance.
Instead of matching candidates using only job titles and resume keywords, Huzzl interprets the complete hiring requirement. Its agents evaluate skills, experience, career history, seniority, industry context and evidence of ownership to identify candidates with stronger alignment to the role.
This approach was especially relevant to Flapico’s requirement.
The ideal candidate might not have previously marketed a product carrying the exact label “LLMOps.” What mattered was whether the person had demonstrated the ability to understand technical software, market to sophisticated buyers and create a compelling narrative around a new category.
Huzzl could therefore evaluate candidates against the substance of the requirement rather than relying only on matching terminology.
The commercial model was equally important. Flapico could engage Huzzl for a flat ₹30,000 recruitment fee linked to the hiring outcome, without purchasing an annual recruitment subscription or agreeing to an agency commission calculated against the candidate’s compensation.
How Huzzl approached the search
1. Converting the requirement into a relevance model
Huzzl worked with Flapico to define what the marketing hire would need to accomplish.
The requirement was converted into structured evaluation criteria covering areas such as:
This created a shared definition of relevance before candidate sourcing began.
2. Looking beyond marketing job titles
Marketing titles often conceal significant differences in responsibility.
Two candidates carrying the title “Product Marketing Manager” may have worked with entirely different buyers, owned different parts of the funnel and operated with very different levels of independence.
Huzzl looked beyond the designation to evaluate:
This helped distinguish broad marketing experience from experience relevant to Flapico’s product and stage.
3. Ranking candidates by contextual fit
Candidate profiles were evaluated and organised around their alignment with Flapico’s actual requirements.
Rather than asking the Flapico team to review a large collection of loosely matched resumes, the process concentrated attention on candidates with stronger role-match signals.
Huzzl’s agents supported the repetitive work of organising and evaluating candidate information. Flapico’s team remained responsible for interviews, cultural assessment and the final hiring decision.
4. Supporting the process through offer release
Huzzl supported the recruitment journey beyond candidate discovery, helping move relevant candidates through screening, evaluation and interview coordination.
Once Flapico selected its preferred candidate and released the offer letter, the agreed ₹30,000 recruitment fee became payable.
The outcome
Flapico successfully closed its marketing position for a total recruitment fee of ₹30,000.
The flat commercial structure allowed the company to make a critical hire without committing to an annual recruitment platform or paying an agency commission linked to the candidate’s salary.
More importantly, Flapico could focus its internal time on assessing the strongest candidates rather than manually administering sourcing and early-stage screening.
| Traditional recruitment model | Huzzl outcome-linked model |
|---|---|
| Annual platform or database subscription | No long-term subscription |
| Agency commission linked to compensation | Flat ₹30,000 fee |
| Costs may begin before an outcome | Fee linked to offer release |
| Candidates matched primarily by title | Candidates evaluated for contextual relevance |
| Large volumes of loosely matched resumes | A focused, ranked candidate pipeline |
| Manual sourcing and initial screening | Agent-assisted search and evaluation |
| Multiple disconnected hiring activities | One coordinated recruitment workflow |
Why the model suited Flapico
Flapico has built its product around replacing guesswork with structured evaluation.
AI teams should not decide whether one prompt or model configuration is better based purely on intuition. They need repeatable tests, measurable outputs and clear evidence.
Huzzl applies a similar philosophy to hiring.
A resume containing the right keywords does not necessarily mean that a candidate can succeed in the role. Huzzl examines the evidence behind the profile and evaluates it in the context of what the company actually needs.
Flapico brings measurable evaluation to LLM applications. Huzzl brings structured relevance to recruitment.
Both companies share a simple belief: AI should improve the quality of important decisions—not merely automate existing tasks.
Proposed Flapico quote—subject to approval: “We needed a marketer who could understand an engineering-focused AI product and communicate its value clearly. Huzzl helped us concentrate on candidates who matched that context and gave us a straightforward commercial model: ₹30,000 when we released the offer.”
One important hire. One clear price.
For Flapico, the objective was not to acquire another recruitment tool. It was to find the right person to take a technical AI product to market.
Huzzl aligned the recruitment process and its pricing with that outcome.
The result was a successfully closed marketing position, a predictable ₹30,000 recruitment fee and no long-term platform commitment.
About Flapico
Flapico is an LLMOps platform for managing, versioning, testing and evaluating prompts. It helps product and engineering teams collaborate on LLM applications, run quantitative evaluations and improve the reliability of AI-powered products before and after they reach production.
About Huzzl.ai
Huzzl.ai is an agentic AI recruitment platform that helps companies understand roles, discover candidates, evaluate relevance and move suitable people through the hiring process.
Huzzl combines AI-assisted sourcing and evaluation with human-controlled hiring decisions. Companies can use individual recruitment services on a pay-as-you-go basis or engage Huzzl through an outcome-linked hiring model.
Have a technology role to fill?
Give Huzzl the ₹30K Challenge.
Share the role, required capabilities and hiring context. Huzzl uses AI-powered search and recruitment expertise to source, screen and rank relevant candidates.
Your team makes the final hiring decision. The flat ₹30,000 recruitment fee becomes payable when you release the offer letter, subject to role eligibility and the agreed engagement terms.