Work that shipped.

B2B companies use 10–30 tools across marketing, sales, CS, and finance — but nobody has a single, accurate view of how a customer moves from first touch to advocate. Revenue teams make decisions on gut feel and spreadsheets.

Existing CDPs and RevOps platforms either require $200K+ implementations or lock you into their vendor's data model. Neither works for growth-stage startups.

Most founders would integrate a unified API vendor (Merge, Apideck) for speed. I chose to build a proprietary Canonical Data Structure Layer (CDSL) — more upfront work, but full ownership of the data model, residency control, and a genuine competitive moat.

Architecture: Medallion model — Bronze (raw API ingestion) → Silver (Pydantic-validated canonical models) → Gold (analytics and AI-ready). A Global Entity Map handles cross-platform deduplication.

Sales and CS reps were losing 30–45 minutes after every customer call: writing summaries, extracting action items, updating CRM fields manually, drafting follow-up emails. The data was inconsistent, often missed, and never reached the right place.

Leadership had zero visibility into what was actually being said in customer conversations — the most valuable data in any B2B company.

Owned end-to-end product definition: user research with Sales Execs and CS managers, persona creation, journey mapping, PRD writing, and engineering handoff across a 10-member team.

Defined the full product specification: bot behaviour, transcription pipeline, LLM prompt architecture for structured insight extraction, CRM field mapping, and follow-up email generation logic.

The company ran four CRMs in parallel across different teams. One customer could exist as four different records with conflicting data: different names, deal stages, health scores. Nobody could confidently answer "who is our best customer?"

Reporting was manual and monthly. Leadership decisions were based on whichever CRM the presenter happened to use that day.

Contributed to the design of a Canonical Data Structure Layer (CDSL) that normalised customer lifecycle data from all four CRMs into a unified platform model. Defined entity relationships, field mapping standards, deduplication rules, and conflict resolution logic.

Managed CRM structuring, data mapping, and workflow configuration across all four platforms. Coordinated phased rollout with engineering and per-team stakeholder alignment.

Revenue teams were context-switching constantly — pulling data from 6+ tools to answer questions that should take seconds: "What's pipeline health this week?", "Which deals are at risk?", "What happened on the last 3 customer calls?"

A single monolithic AI assistant wasn't the answer — it would be too slow, too generic, and impossible to test. The solution was a specialised agent network, each expert in one domain.

Designed the agent taxonomy: identified 25+ distinct contexts where AI replaces manual lookup. Wrote product specs for each agent — defining inputs, outputs, data sources, edge cases, and inter-agent handoff contracts.

Led sprint planning and dependency management across the 10-member engineering team to ship agents iteratively without blocking each other.

Enterprise SaaS platforms don't share data cleanly. Each has its own API schema, authentication model, rate limits, and webhook behaviour. Building a product that unifies them requires carefully designed integration specs — not just API calls.

I owned the integration requirements layer: defining what data flows where, in what format, with what fallback behaviour, and how conflicts between systems get resolved.

Integration workflow and requirements documents for each platform. Auth flow designs (OAuth, JWT, SSO, RBAC). Data field mapping specs. Error handling and retry logic definitions. Coordinated with engineering across Dev → Staging → Production environments via Azure DevOps.

Also contributed to SOC 2 and ISO 27001 readiness: audit trail requirements, access control planning, and security workflow reviews for compliant integration architecture.

Jalsa Events ran multiple concurrent events with no centralised visibility system. Vendor coordination happened over WhatsApp, CRM updates were manual, and execution status lived in the event manager's head.

This was where I first built systems for managing people, timelines, and information across multiple concurrent workstreams — the same skills that later powered AI product execution.

Centralised execution tracker for concurrent events. Operational reporting system giving leadership real-time visibility. Vendor communication frameworks. CRM update workflows. Cross-team coordination processes.

The systems reduced time-to-decision on vendor issues and gave leadership a single view of event health across all active projects.