Lyzr helps enterprises automate complex workflows and entire job functions by building reliable, private, self-learning AI agents. The platform is an enterprise AI agent infrastructure (B2B SaaS and…
AI AgentsAutomationCloud DeploymentEnterprise AIGenerative AIHybrid AIOrganizational General IntelligenceResponsible AIlyzr.ai
Lyzr AI
Lyzr helps enterprises automate complex workflows and entire job functions by building reliable, private, self-learning AI agents. The platform is an enterprise AI agent infrastructure (B2B SaaS and…
AI AgentsAutomationCloud DeploymentEnterprise AIGenerative AIHybrid AIOrganizational General IntelligenceResponsible AIlyzr.ai
HQJersey City, US
Team Size169
Open Jobs8
Total Funding$25M
Latest Fundraiselast month
TL;DR
What they do: Enterprise AI agent infrastructure platform for building, deploying, and governing production AI agents
Headquarters: Jersey City, New Jersey
Founded: 2023
Notable investors: Rocketship.vc, Accenture Ventures, GFT Ventures, Firstsource, Plug and Play, BGV, Partnership Fund for New York City, Arka
Total funding (reported): ≈ $10.5M–$14.5M (multiple sources report rounds in this range)
Company Overview
Problem Domain
Automating complex workflows and end-to-end job functions in regulated enterprise environments (BFSI, telco, BPO, content, GTM functions).
Founded
2023
Industry
Technology, Information and Internet
Tech Stack
Agent Development Kit (ADK)
Low-code agent studio
SaaS and on-prem / customer VPC deployment models
Responsible AI / compliance integrations (GDPR, SOC 2, HIPAA)
Funding Track Record
Series A- 2025-10-29
8,000,000
Company blog post dated Oct 29, 2025 announces Series A led by Rocketship.vc with participation from multiple investors.
- 2026-02
10,500,000
Dealroom reported a $10.5M raise (Feb 2026).
Investor Signal
“Participation from Accenture Ventures and a syndicate including GFT Ventures, Firstsource, Plug and Play, BGV, Partnership Fund for New York City, and Arka indicated on company materials.”
About LyzrLyzr builds enterprise automation with AI agents. We’re moving beyond traditional SEO and need content and retrieval systems that win the answer on AI-first surfaces like Perplexity, ChatGPT, and Google Gemini while still driving organic discovery and pipeline.Role Overview
**You’ll own optimization of our content, knowledge sources, and retrieval pipelines so our product pages, docs, and knowledge assets become the preferred, high-quality answers surfaced by Perplexity, ChatGPT, Gemini, and other AI copilots.
What you’ll own**
AI-answer optimization: Design and execute strategies that increase the presence, fidelity, and click-through of Lyzr answers in Perplexity, ChatGPT, Gemini, and other LLM outputs.
Prompt & snippet engineering: Create prompt templates, answer templates, and canonical snippets that reliably produce correct, concise, and citable outputs across target models.
Success metrics
Increase in being surfaced/cited by Perplexity/ChatGPT/Gemini for target queries (presence share).
Reduction in hallucination/citation errors for Lyzr-sourced answers.
Qualifications & Skills (must-haves)
3+ years in SEO, content ops, or growth with hands-on experience in SEO tools
Demonstrable experience optimizing content for LLM/AI-answer surfaces (Perplexity, ChatGPT, Google/Gemini, or similar). Describe past wins.
Strong technical skills and experience designing prompt templates for multiple model families.
Understands terminologies like embeddings, retrieval, Pinecone/Weaviate
Solid analytics background: Python + SQL to run experiments, parse results, build dashboards.
Familiarity with SEO tooling: Ahrefs/SEMrush, Screaming Frog, GSC, GA4.
Excellent editorial judgment for concise, authoritative answers; able to write canonical 1–3 sentence answers that scale.
Nice-to-haves
Experience with Lyzr, LangChain, LlamaIndex, or any other RAG frameworks.
Prior work implementing JSON-LD optimizations for answer engines or rich results.
Experience with model evaluation frameworks and human-in-the-loop annotation for hallucination reduction.
Background writing for developer or enterprise audiences (BFSI, SaaS).
Why join Lyzr?**
You’ll define how an AI-first enterprise product gets answered across the web and LLM platforms. Work directly with founders and engineers, ship technical content + retrieval features that impact pipeline, and build the company’s voice inside the next generation of search.
Startup jobs. A lot of them.
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RAG & retrieval tuning: Architect and iterate on retrieval pipelines (chunking, chunk size, embeddings similarity thresholds, hybrid retrieval) to minimize hallucinations and surface canonical sources.
Structured data & schema: Implement and optimize JSON-LD, FAQPage, QAPage, HowTo, and custom schema that improve LLM citation and snippet quality.
Source authority & canonization Maintain canonical versions of docs, one-sentence answers, and source mappings so LLMs cite Lyzr sources (and cite them correctly).
Evaluation & instrumentation Build tests/metrics for “answer quality”: presence rate, citation rate, hallucination rate, citation fidelity, and downstream conversion (clicks → demo requests). Automate periodic evaluation across model families.
Content & editorial ops Produce SEO/LLM briefs, microcopy, and short canonical answers for product pages, docs, and FAQ hubs. Train writers on answer-first writing.
Cross-functional partnership Work closely with Product, Docs, Growth, and Engineering to ship retrieval improvements, on-site features, and content changes.
Rank & traffic lift for answer-first pages; improved demo-signup conversions from LLM-driven sessions.
Productionized at least one RAG/LLM feature powering docs/product help or on-site search.
Lead generation target (new): Drive 150–200 MQLs per month originating from generative search/answer engines (Perplexity, ChatGPT, Gemini and equivalent platforms) within the 6–12 month window.
Definition / attribution: MQL = validated demo request or form-fill that meets our inbound qualification criteria. Attribution should be measured via a combination of:
UTM/parameterized landing pages linked from canonical answer snippets or LLM-cited URLs.
Redirect & landing page telemetry for “LLM referrals” (unique landing tokens included in canonical answers).
Server-side logs and model-response scraping that map LLM-cited links to session starts, and GA4 + CRM matching to confirm MQL status.
Acceptance criteria: 150–200 MQLs/month with a verified attribution method and a conversion funnel that shows at least X% demo-booking rate from those sessions (X to be agreed with Growth/Rev Ops).
Comfortable collaborating with product and engineering teams to ship retrieval and schema changes.
