Moov 37
the agentic job
search experience
Saas
Product Strategist
Product designer
UX Researcher
Interaction Designer
Ai in hr
Recruitment technology
see moov 37
Imagine a world where discovering
the perfect candidate and landing
your ideal job, is as effortless as a
conversation.
About Moov 37
MOOV 37 IS A NEXT GEN AGENTIC HIRING
PLATFORM DESIGNED TO REVOLUTIONIZE
hiring for recruiters and job seekers.
the app leverages ai agents that
reason, act and adapt aiming to
radically simplify the process and
making it intelligent, interactive,
and real-time.
a glimpse of the landing page.
The platform was conceptualized for a Y-combinator pitch and built
as a working prototype. While the current live version is a minimal v1
without ai agents due to budget constraints, this case study focuses
on the agentic-first design vision that powered the prototype.
Business Problem
traditional platforms (linkedin,
naukri, uplers) suffer from manual,
fragmented workflows. the global
market is sluggish and competitive—
especially for international students
and early-career professionals.
Job seeker problem
/ form fatigue from entering repititive
data across platforms.
/ long onboarding processes with
low reuse of resume content.
/ unclear application flows and
little match-based guidance.
Recruiter problem
/ time-consuming setup before
receiving useful candidate matches.
/ over-reliance on filters that often
produce low-quality results.
/ difficulty generating compelling
job posts efficiently.
Opportunity
Reimagine the hiring process
by simplifying onboarding,
reducing form clutter, and
enabling AI-assisted, guided
workflows on both sides.
Deep-Dive
Into The Problem
to understand current gaps in the
hiring journey, I conducted a
competitive analysis of leading job
platforms like uplers, eightfold, and
linkedin recruiter, Identifying
painpoints across both candidate
and recruiter flows.
according to a 2023 glassdoor
study, candidates abandon 60% of
applicantions midway due to long or
repetitive forms. Meanwhile,
recruiters report spending nearly
30% of their time filtering
irrelevant profiles.
Candidate-side
/ long, repititive forms as they fill in
the same information across
multiple platforms.
/ while job portals expect them to
"Upload resume" and also fill in the
same details again, most platforms
do not parse based on uploaded
content.
/ no clear feedback on how well
they matched job roles.
/ little sense of progress or
personalisation during onboarding.
Recruiter-side
/ high time spent on setup before
seeing meaningful candidate results.
/ Quality of applicants is often
poor unless filters are used
strategically.
/ No time or expertise to write
strong job posts that attract the
right talent.
/ recruiters are starting to
experiment with chat-GPT to write
job posts or review profiles which
shows a behavioural shift.
️TL;DR gist of my research
candidates are tired of repeatedly
filling out the same data across
multiple platforms, facing long
forms and poor matching
experiences.
recruiters, on the other hand
spend time manually entering jobs
and navigating low quality filters.
there is a need to reduce
redundancy, guide users and make
hiring feel less like paperwork
and more like progress.
Solution Hypothesis
Approach 1: dashboard-first
approach inspired by uplers
wireframe: a control center like dashboard with stats.
What
candidates and recruiters view an application similar to a control center.
Users would see real-time stats (eg.: Number of views, status updates,
recruiter response) and manage everything from a centralised dashboard.
Pros
/ empowers recruiters to quickly scan, filter, and follow up without
digging into systems.
/ encourages accountability both sides with better visibility.
/ works well with power users managing multiple jobs or candidates.
Cons
/ dashboard with multiple things showing up at the same time can become
overwhelming.
/ doesn't fully solve for the friction of form filling or discovery.
/ could mirror existing ATS dashboards if not differentiated visually and
functionally.
Approach 2: form-like structure
with contextual suggestions
wireframe: a control center like dashboard with stats.
What
the idea leaned into making form-filling as painless as possible, no fluff, but
heavy ux optimisation. it relies on inputs and clever contextual suggestions.
it takes inspiration from well-designed onboarding flows like notion or
superhuman.
Pros
/ familiar ui pattern that requires no learning curve.
/ low implementation effort with maximum usability impact.
/ easier to validate future ux improvements.
Cons
/ does not differentiate from competitors, might feel too normal.
/ makes it look like—"just another job application".
/ Lacks the excitement or "wow factor" for an early stage pitch/vision.
/ does not tap into the potential of future proof patterns.
Approach 3: ai Agent-powered with
natural language and fallback ui
wireframe: a control center like dashboard with stats.
What
the idea focussed on reducing form fatigue by letting users describe their
needs conversationally—"I'm looking for hybrid tech roles in mumbai under
10lpa", and allowing the agent to handle the rest. the agent could also assist
recruiters and candidates with setting up roles and interestes and reviewing
matched jobs or candidates.
Pros
/ drastically reduces manual input and makes the process feel fluid.
/ aligns with rising user expectations from tools like chat gpt, gemini, etc.
/ can adapt to both recruiters and candidates, creating a unified experience.
/ highly scalable with the right backend logic and model integrations.
Cons
/ high implementation cost; required real-time nlp, training and logic.
/ might create ambuiguity if guardrails or fallback ui isn't tight.
/ needs thoughtful handling of edge cases, especially for recruiter-side
complexity.
/ can confuse users expecting traditional form-based interface.
Explorations—
Agentic architecture
Concern 1: long job forms
discouraged completion
We aligned with an ai-powered chat
interface to replace the long form.
Information would be collected
gradually through conversation,
making the process feel lighter and
more personal. the ai could also
adapt questions based on earlier
responses, helping users focus only
on relevant details.
Pros
/ reduces intimidation from large
forms.
/ feels more personal and
interactive.
/ can adapt over time as ai learns.
Cons
/ can be slower for users who have
all information ready.
/ interface might feel unfamiliar for
users used to forms.
/ higher build cost than simple forms.
Concern 2: users felt lost when key
details scrolled out of view in chat
We decided on a split-screen layout
where the left 70% is the chat
interface, and the right 30% is a
persistent profile or job-posting/
profile panel. as the user chats, the ai
Updates this panel in real-time so
that information doesn't get lost in
scroll. recruiters and candidates
can directly edit any section by
calling out that section in the chat.
Pros
/ prevents info from feeling buried in
conversation.
/ gives users an always visible
progress snapshot.
/ makes it easier to correct mistakes
in the new ai job portal.
Cons
/ takes up screen space, reducing
chat area.
/ requires careful ui balance so
panel does not feel cramped.
Concern 3: recruiters don't have
time to filter candidates
we introduced a match percentage
with supporting reasons. the ai
displays a score (e.g., 78% match)
alongside key factors influencing
that score (such as location fit,
relevant experience). this would
help recruiter and candidates
quickly gauge suitability without
looking here and there.
Pros
/ provides instant clarity on fit.
/ saves time by focussing only on high
potential matches.
/ builds trust by showing reasoning.
Cons
/ match accuracy depends on quality
of data input.
/ may give a false sense of certainty
if reason context is incomplete.
Outcome
and MVP
we had ambitious plans for an
agent-driven assistant, but for MVP
we had to balance the vision with
pragmatism. the shipped product
was a strutured, form-like interface,
not glamorous, but a deliberate step
to validate adoption and workflows
before investing in agentic
complexity.
this is What shipped as mvp.
this is where were heading.
Key outcomes
/ Recruiters published posting faster,
from ~30 minutes to <10 minutes.
/ candidates often abandoned long
registration portals. the mvp broke
onboarding into 3 progressive steps,
cutting major effort.
/ every input in the form was designed
to "feed the agent" (skills, roles,
preferences) that an agent ui in v2
could later query and edit in
real-time.
/ we aligned with the client who
needed something shippable and
testable for market entry.
/ by choosing the simpler mvp, we
derisked launch timelines while
keeping the door open for
high-impact agentic features in v2.
Why this mattered
on the surface, yes—the mvp looks
like just another form-based
portal. but the value was in what
it enabled:
we needed to prove that
recruiters actually got better
matches and candidates finished
profiles fully—two things many
portals fail at.
the form gave us structured,
reliable data—the raw material
the agentic system we envisioned
for v2 would need.
jumping straight to an ai-first
experience would've been costly
and risky if the basics weren't
solving the problem. the form
de-risked the investment.
Reflections
designing moov 37's agent-powered
interface forced me to abandon my
usual "screen-by-screen" mindset.
instead, i had to design for a living
system that responded to user
intent, kept context and adapted
in real time.
Adaptive over static thinking
Our early dashboard-like concept
fell short because it locked users
into fixed flows. shifting to an
agentic approach meant thinking in
states—example, keeping the
recruiter's job posting details pinned
and editable white chat continued.
Progressive inputs beat long forms
Recruiters hated filling long job
posting forms. we learned to let the
agent collect details piece-by-piece,
while showing how each input
improved candidate match quality.
Explainability builds trust
early "match %" suggestions felt
arbitrary. we improved them by
showing 'why' a candidate matched,
skills, location fit, education, etc—
so recruiters didn't feel ai was
guessing.
Trade-offs are inevitable
the agent was to be robust, but
real-estate was tight. we had to
balance chat space with the
persistent profile view, which meant
cutting secondary features that
cluttered ui.
I can address the elephant in your room
Let’s Connect
HRUDAY KULKARNI©