Rajeev Chandran
Rajeev Chandran — AI Engineer | FDE | AI Researcher
What I do: I ship production AI systems as a Forward Deployment Engineer — embed with teams, ship weekly, harden through go-live.
Who I help: startups and enterprises needing AI-native SaaS, RAG, agents, Text2SQL, CRM modernization, or hybrid DSS.
Location: Bangalore, India & Boston, US. Email: rajeev@rajeevchandran.me. LinkedIn: https://www.linkedin.com/in/rajeevchandran.
Featured Projects
Anonymous Behavioural Modelling Program
Privacy-preserving LTV, churn, segmentation, and retention — scored from behaviour, not identity.
Problem: Growth and CX teams needed LTV, churn risk, and retention targeting, but every model ran on named customer records. Segments went stale, churn showed up after the fact, and privacy constraints blocked the data science loop.
Solution: Implemented an anonymous behavioural modelling program: event-level features with no PII payload, custom LTV and churn models, live behavioural segmentation, and retention playbooks that fire on propensity — not on a named profile.
Impact: Retention and GTM could score value and risk without identity in the pipeline — earlier churn interventions, LTV-aware acquisition, and segments that refresh as behaviour changes.
Hybrid DSS
Palantir-style DSS — text, numerals, and domain knowledge fused into leadership-grade insights.
Problem: Leaders had dashboards of numbers, folders of docs, and years of domain judgment — but no system that fused unstructured text, quantitative signals, and expert rules into one decision surface.
Solution: Built a Palantir-like hybrid DSS that jointly reasons over text corpora, numerical KPIs/time series, and encoded domain knowledge — producing cited, auditable recommendations for leadership.
Impact: Leadership moved from swivel-chair analysis across spreadsheets and docs to a single insight layer — grounded answers that blend narrative evidence with hard numbers and domain policy.
CRM Refactoring
AI-assisted CRM modernization — schema, workflows, and APIs rebuilt without freezing the business.
Problem: A brittle CRM blocked new GTM motions: schema debt, slow releases, and every new field or automation risked production breakage.
Solution: Embedded as FDE to strangler-migrate modules, AI-assist code/schema moves, add parity tests, and cut over intake/booking/notify paths to a cleaner stack.
Impact: Unlocked weekly shipping on CRM surfaces that previously took multi-week change windows — with safer cutovers and cleaner handover.
AI Native Cloud Ops
AI-native cloud operations — observability, runbooks, and agent-assisted remediation.
Problem: Incidents and routine cloud toil burned engineering time: noisy alerts, tribal runbooks, and slow diagnosis across AWS/Azure/K8s stacks.
Solution: Stood up an AI-native ops layer: log/metric RAG, runbook agents, safe tool calls for common remediations, and production guardrails on every action.
Impact: Shorter MTTR, fewer repetitive tickets, and ops playbooks that stay executable — not buried in docs nobody opens.
Text2SQL
Natural-language → SQL agent with schema guardrails, live charts, and anomaly alerts.
Problem: Directors needed enrollment, utilization, and revenue answers weekly — but every question became another SQL ticket for analysts.
Solution: Shipped a Text2SQL decision surface: NL → validated SQL, live charts, anomaly digests, and role-aware access over production Postgres.
Impact: Leaders self-serve critical KPIs; analysts focus on exceptions instead of recurring report pulls.
Agentic Social Media GTM
Agentic social GTM — research, draft, schedule, and learn from engagement loops.
Problem: GTM and content teams juggled research, drafting, approvals, and posting across tools with no closed-loop learning from what actually performed.
Solution: Built agentic GTM workflows: audience/research agents, brand-safe draft generation, approval gates, scheduler connectors, and engagement feedback into the next cycle.
Impact: Consistent multi-channel cadence with fewer manual handoffs — and a measurable feedback loop from post → engagement → next brief.
Frequently Asked Questions
How long does an AI project take to build?
Most production-ready AI SaaS MVPs and workflow platforms take between 2 to 4 weeks. Because I combine pre-built full-stack SaaS scaffolding with advanced AI code generation workflows, we go from architecture blueprint to live cloud deployment in a fraction of traditional development cycles.
Can AI integrate with our existing software and databases?
Yes, absolutely. Every system I build is designed for enterprise integration. Whether your data lives in PostgreSQL, Snowflake, Salesforce, HubSpot, Notion, Google Drive, or custom internal REST APIs, I engineer secure API bridges, webhooks, and MCP connectors.
Can you build a complete MVP from scratch?
Yes. I specialize in taking raw concepts or wireframes and turning them into complete, market-ready SaaS MVPs—including responsive frontend UI, multi-tenant database backend, authentication, Stripe payments, AI logic, and automated cloud hosting.
Can you modernize an existing legacy SaaS platform with AI?
Definitely. Modernization often yields the fastest ROI. I can inject RAG semantic search, natural language command bars, AI-driven summaries, and autonomous background tool agents into your existing platform without disrupting your core infrastructure.
How do you handle data privacy, security, and AI hallucinations?
Data privacy is a non-negotiable cornerstone. I implement strict zero-retention API configurations, encrypted vector databases, role-based access control (RBAC), and deterministic guardrail layers (like cross-encoders and validation agents) to guarantee zero hallucinations and complete compliance.
What is your engagement structure and working model?
Most engagements are Forward Deployment Engineering: I embed with your team, ship weekly into production, and own cutover through handover. Alternatively, I run a fixed-scope 2–4 week MVP sprint or act as fractional Lead AI Engineer for ongoing product development. Every project includes documentation, 100% IP code handover, and post-launch tuning.
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