Chief Product Officer - HonestInsurance.ai

A Continuum Core venture

Location: Gurugram (in-office; the founding team is co-located)

Reports to: CEO, Continuum Core

Scope: Customer acquisition - end to end, from first impression to policy bound

Type: Full-time, founding leadership; meaningful equity

About Continuum Core:

Continuum Core is an AI-first company. We build production AI agents for regulated, high-friction industries - cross-border trade, insurance and infrastructure. The founding team has shipped AI at scale in Indian BFSI and built and run product at India's largest insurance marketplace. We do not build demos; we build systems that carry revenue and regulatory risk.

About HonestInsurance.ai:

HonestInsurance.ai is Continuum Core's consumer-facing, AI-native insurance broker for India. Launch lines: health, motor and term life. Stealth, pre-launch.

The thesis: Indian insurance distribution is optimised for commission, not for the customer. We replace the incentive-driven human salesperson with an AI advisor that recommends transparently, explains its reasoning and discloses what it earns.

How we are built:

AI-first, deliberately small. We are not building a large product org. The team stays small and senior; agents carry the volume. Every function that can be run by an agent will be - research synthesis, creative production, analytics, QA, support. Headcount is the last resort, not the default. If your instinct when scope grows is to hire, this is the wrong role.

You will use AI in your own work daily and hold the team to the same standard.

The mandate:

One job: acquire customers profitably, at scale, in a category where trust is the binding constraint.

You own everything between a stranger's first exposure to HonestInsurance.ai and a bound policy. Servicing, claims and renewals sit elsewhere. Your remit is narrow by design and deep by necessity - traffic, trust, advice, quote, conversion, payment, and the behavioural science that connects them.

You own acquisition economics, not a feature backlog: CAC by channel, quote-to-bind conversion, contribution margin on the first policy, CAC payback.

What you will own:

- Acquisition strategy and channel portfolio: Build and sequence the channel mix: performance marketing, SEO and comparison-intent content, WhatsApp, referral, embedded and partner-led distribution, organic and brand.

- Kill channels that do not clear payback. Concentrate spend where the model is proven.

- Own the acquisition model - spend, volume, mix, contribution - and defend it in board reviews.

- The conversion journey: Own discover - needs analysis - advise - quote - buy as one system, across web, WhatsApp and app.

- Design for India: vernacular, mobile-first, low-trust first-time buyers, UPI and EMI, high drop-off at KYC and payment.

- Landing pages, calculators, comparison views, quote forms, pre-issuance checks - every surface between intent and bind is yours.

- Behavioural instrumentation and the insight loop: Instrument every screen before the first user arrives. Session replay, heatmaps, rage-click and dead-click analysis, scroll depth, form-field abandonment - Microsoft Clarity, Hotjar, PostHog, FullStory, Amplitude or equivalent, your call on stack.

- Run a weekly loop: watch real sessions, isolate where users hesitate, drop off or misunderstand, ship the fix, measure the delta. This is the operating rhythm of the role, not a research function you delegate.

- Fuse replay with funnel analytics and advisor conversation logs - three views of the same drop-off.

- Use agents to synthesise replay and transcript volume at a scale a human research team could not cover.

- Experimentation velocity: Install the testing system: hypothesis, sample, guardrail metrics, decision rule. High test throughput, low ceremony.

- Landing page, messaging, pricing presentation, advisor script and funnel-step tests running continuously.

- The AI advisor as a conversion instrument: Product-own the pre-sale advisor: needs elicitation, recommendation, explanation, objection handling, abandonment recovery.

- Define guardrails - deterministic on eligibility, disclosure and suitability; generative on conversation and explanation.

- Own evaluation: hallucination rate, recommendation accuracy, suitability compliance, escalation thresholds, and conversion lift against a human-assisted baseline.

- Trust and compliance at the point of sale: Embed IRDAI broker regulation, suitability norms and disclosure requirements into the acquisition flow itself.

- Make transparency convert: commission disclosure and comparison integrity should raise conversion, not depress it. Prove it with tests.

- Quality of acquisition: You are measured on the customers you bring, not just the count. Mis-sold, mis-matched or low-intent buyers who lapse are a failure of acquisition. Hold a line on early-lapse and first-renewal quality even though you do not own renewals.

Outcomes we will hold you to:

- Acquisition thesis: Channel sequencing and target unit economics approved by the board. Full behavioural instrumentation and experimentation infrastructure live before first user. Advisor v1 conversion spec with guardrails and evals.

- Public launch: Baseline CAC, quote-to-bind conversion and contribution margin established per channel. Weekly insight-to-ship loop running with a documented record of fixes and measured deltas.

- Scaling: At least one channel at target CAC payback and scaling. Quote-to-bind conversion materially above the Indian category benchmark. Advisor outperforming human-assisted conversion in controlled tests. Delivered by a team still in single digits.

Who you are:

- 12+ years in product; 5+ owning consumer acquisition or growth product at a consumer fintech or insurtech at scale (PolicyBazaar, Acko, Digit, Ditto, Plum, Turtlemint, or equivalent).

- Have owned an acquisition funnel end-to-end and moved its economics - you can quote your CAC, channel mix and conversion numbers from memory.

- Demonstrated product improvement driven by user-behaviour insight. You have used Microsoft Clarity, Hotjar, FullStory, PostHog or similar to find where real users broke down, shipped against it, and can name the metric that moved and by how much. Bring two or three specific examples.

- High experiment throughput as a personal standard, with a track record of compounding wins.

- Working fluency with IRDAI broker regulation as it governs solicitation, disclosure and suitability at the point of sale.

- Have shipped LLM- or agent-based product to real users and can speak to evals, failure modes and guardrails from experience, not reading.

- Have delivered outsized output with a small senior team.

Strong signals:

- Founder or founding-team experience; zero to launch.

- Built vernacular or WhatsApp-native acquisition journeys for India.

Not a fit if:

- You optimise features, not acquisition economics.

- Your answer to more scope is more headcount.

- You have never watched a session replay of your own product.

- You need a mature engineering org, PRD templates and a quarterly planning ritual to operate.

How we work:

- Small, senior, co-located. Founders are hands-on in product, engineering and growth.

- Decisions on data and speed; disagree, decide, commit.

- Deterministic where the regulator requires it, generative where the customer benefits.

- Money-back accountability with clients on the enterprise side; the same bar applies to ourselves.

Compensation:

Cash: 18 to 24 LPA

Equity: founding-leadership band, standard 4-year vest, 1-year cliff.

Reviewed against outcomes above, not tenure.

Process:

- 30-minute screen with a co-founder.

- Working session (2 hours, founders): present an acquisition thesis for an AI-native insurance broker in India, plus a teardown of one live Indian insurance funnel with the behavioural evidence behind your critique.

- Deep dives: acquisition economics and channel strategy; AI advisor design and evals; references.

- Founder round and offer. Target: decision within 1 week of first conversation.