Product Manager - AI

Skills this role screens for

About the role

ABOUT SPOTDRAFT

SpotDraft is an AI-native contract lifecycle management (CLM) platform built for in-house legal teams “Draft, review, sign & renew faster, with legal in charge” The platform spans the full contract lifecycle: workflow-driven creation, negotiation and collaboration (across Word, Slack, and SpotDraft), a centralised repository with search and access controls, analytics, and native e-signatures compliant with ESIGN and eIDAS.

AI runs through the product, not beside it:

DraftMate AI turns documents into templates by auto-detecting placeholders.

Smart Data Capture extracts contract metadata so teams can search and report on any term.

VerifAI speeds up contract review inside Microsoft Word with suggestions, redlines, and automated compliance checks.

Sidebar is a team of always-on AI legal assistants (agents) that handle contract review against your playbooks, regulatory monitoring, and legal research, connected to your contract repository.

On-device AI runs VerifAI (embeddings, clause extraction, risk scoring, edits) entirely on Snapdragon processors, backed by a January 2026 Qualcomm Ventures round.

Customers include Abnormal Security, IPSY, Guideline, and Chaberton Energy, with a typical go-live in weeks rather than months. SpotDraft is venture-backed (Series A and Series B, plus a

January 2026 Qualcomm Ventures extension) and runs across Bengaluru and New York.

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Top Outcomes

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Why this role exists

Our product roadmap increasingly depends on AI doing real work inside the product: extracting structured data from documents, and running agents that take action on a user’s behalf. That only works if two things are true: the underlying extraction is accurate enough to trust, and every AI action is governed (auditable, explainable, escalated to a human when it should be, and safely isolated between customers).

This role owns that platform. You turn “the AI isn’t accurate enough yet” and “agents need guardrails” into a roadmap, a trust bar, and an interface that other product teams can build against without a standing dependency on one engineer.

The work

Day to Day

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Extraction accuracy

• Own the roadmap to improve document/data extraction accuracy against defined targets, including major build-vs-improve calls on the underlying approach, in partnership with ML/engineering.
• Define what "accurate enough to trust" means for each use case, and track progress against it on a live, shared metric.

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Agent runtime & governance

• Own product requirements for the runtime that lets AI agents take actions inside the product.
• Own the governance layer: audit trail, explainability, human-in-the-loop escalation, policy enforcement, and tenant isolation. This is what makes an AI-driven action safe to ship, not just possible to build.
• Define a policy-bounded interface for agent-triggered actions, so actions stay within defined guardrails without requiring engineering review per action.

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Platform-as-a-service discipline

What success looks like (first 2-3 quarters)

• Define and maintain a self-serve interface so other product teams can build on this platform without filing a ticket to a single engineer for routine requests.
• Partner with adjacent platform owners (e.g., core integrations/data) so teams building on top see one coherent surface, not competing conventions.
• Push back on one-off requests that would turn the platform back into a bottleneck.
• A clear, evidence-based call made on the extraction approach, with a dated accuracy trajectory behind it rather than opinion.
• Defined accuracy targets met on priority use cases, tracked on a live dashboard.
• At least one AI-driven agent live in production with the escalation/governance loop actually working, not just the underlying model.
• At least one other product team consuming the platform self-serve, with no standing ticket queue for routine requests.
• Usage and override rates on agent-triggered actions instrumented and reviewe