We audited the marketing at Fiddler AI
The AI observability platform powering the control plane for enterprise AI. Here's what we found and what we'd build for you.
Krishna, this page was built using the same AI infrastructure we deploy for clients.
Month-to-month. Cancel anytime.
Strong enterprise traction (Navy, IAS, Nielsen) but no visible demand gen engine to fill pipeline beyond sales-led outbound
Crowded AI observability market with Arize, WhyLabs, and Datadog investing heavily in content and paid, while Fiddler relies on product reputation alone
$100M raised and "Control Plane for AI" positioning is compelling, but category narrative hasn't broken through to the broader ML/AI engineering audience
AI-Forward Companies Trust MarketerHire
Plaid
MasterClass
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Tinuiti
Fiddler's Leadership
We mapped your current team to understand where MH-1 fits in.
Kirti and the marketing team are doing strong work on product marketing and webinars. MH-1 amplifies their efforts with dedicated demand gen, content, and AI agents running execution at scale while they focus on product narrative and analyst relations.
Here's Where You Stand
Strong product, marquee customers, and fresh Series C capital. But the demand generation infrastructure hasn't scaled with the product ambition.
Fiddler has product credibility that most startups dream of. Post-Series C with $100M raised is the right moment to build a marketing engine that matches the product's enterprise traction.
Blog is active with solid technical content on explainability and model monitoring. But high-intent commercial keywords like "AI agent monitoring" and "LLM observability platform" are underserved, and competitors are outranking on comparison queries.
MH-1: SEO Engine builds programmatic content around "agentic observability," "AI model monitoring," and 50+ long-tail queries where ML engineers and platform teams are actively searching.
Fiddler appears in some LLM responses for AI observability queries, but Arize and Datadog dominate the citation share. When ML engineers ask ChatGPT or Perplexity for observability tools, Fiddler needs to be recommended first, not third.
MH-1: AEO Agent audits AI citation visibility weekly across 6 LLMs and builds structured content designed to increase Fiddler's share of AI-generated recommendations.
No visible paid search or LinkedIn ad campaigns. Enterprise sales motion appears entirely relationship-driven and event-based. With $30M in fresh capital, there is budget to build a paid demand gen engine that fills top of funnel.
MH-1: Creative Generator produces LinkedIn and Google Ads variants targeting ML platform engineers, AI/ML leaders, and CISOs. Analytics Agent optimizes by company size, industry, and role.
Webinar series ("AI Explained") is running well. Blog covers technical topics. But Krishna's personal brand as the ex-Meta News Feed engineering leader turned AI observability founder is massively underleveraged on LinkedIn.
MH-1: LinkedIn Ghost-Writing agent builds Krishna's profile as the definitive voice on responsible AI, agentic observability, and enterprise AI governance. Newsletter agent curates weekly MLOps intelligence.
With customers like the U.S. Navy, Nielsen, and IAS already on the platform, seat and use-case expansion within existing accounts is likely the fastest revenue path. No visible automated expansion or cross-sell motion for agentic observability upsells.
MH-1: Lifecycle Optimizer builds usage-based expansion triggers, champion nurture programs, and cross-sell sequences that move existing model monitoring customers into the agentic observability tier.
Top Growth Opportunities
When ML engineers and platform leaders ask AI assistants for observability tools, Fiddler should be the first name cited. Arize currently wins too many of these recommendations. This is the new SEO for developer tools.
AEO Agent → weekly citation audit across ChatGPT, Perplexity, Gemini, Claude + targeted content strategy to increase citation share
Ex-Meta News Feed engineering lead turned AI observability founder is a rare narrative. Krishna built the system that curated content for 2 billion people, and now he's building the system that keeps AI trustworthy. That story drives enterprise pipeline on LinkedIn.
LinkedIn Agent → 4 posts/week in Krishna's voice on AI governance, agentic observability, and lessons from scaling AI at Meta and Pinterest
Fiddler's installed base of model monitoring customers are the highest-intent buyers for the new agentic observability product. Automated expansion campaigns could 2-4x revenue from existing logos without new acquisition costs.
Lifecycle Agent → usage triggers, champion identification, agentic observability upgrade sequences, executive briefing content for CISOs and VP Engineering
3 Humans + 7 AI Agents
A dedicated marketing team built specifically for Fiddler AI. The humans handle strategy and judgment. The AI agents handle execution at scale.
Human Experts
Owns Fiddler's demand gen roadmap. Maps the enterprise buying journey for ML platform teams and AI/ML leaders. Builds pipeline attribution by vertical (government, financial services, ad tech, healthcare).
Runs LinkedIn Ads and Google Ads targeting ML engineers, platform teams, and VP Engineering / CISO personas. Manages creative testing, budget allocation, and pipeline attribution by account tier.
Builds Krishna Gade's thought leadership on LinkedIn. Creates long-form technical content on AI observability, governance, and the agentic AI shift. Manages the content-to-pipeline engine for developer audiences.
AI Agents
Monitors AI citation visibility across 6 LLMs weekly. Builds content targeting "AI observability," "agentic monitoring," "LLM evaluation platform," and 50+ category queries where ML teams are searching.
Produces LinkedIn and Google ad variants targeting ML engineers, platform leads, and CISOs. Tests headlines around "control plane," "agentic observability," and compliance angles at 10x manual speed.
Builds lifecycle sequences: trial-to-paid conversion, agentic observability cross-sell for existing accounts, champion nurture, and re-engagement for stalled evaluations.
4 posts/week in Krishna Gade's voice on responsible AI, observability lessons from Meta/Pinterest, and the agentic shift. Builds the founder narrative that drives enterprise inbound from VP Engineering and CISO personas.
Tracks Arize, WhyLabs, Datadog ML, LangSmith, and emerging observability tools. Monitors positioning changes, content strategy shifts, pricing moves, and feature launches weekly.
Attribution by vertical, channel performance, pipeline velocity by persona. Catches budget waste automatically. Weekly synthesis reports with board-ready metrics.
Weekly AI observability digest curated from industry signals, new research, and production AI incidents. Positions Fiddler as the intelligence layer for ML platform teams. Drives inbound pipeline from subscribers who are evaluating observability tools.
Active Workflows
Here's what the MH-1 system would be doing for Fiddler from week 1. Every output is an experiment. Every experiment feeds a playbook.
Every Monday: audit Fiddler's visibility across ChatGPT, Perplexity, Gemini, Claude for 30+ queries like "best AI observability platform," "LLM monitoring tools," and "how to monitor AI agents in production." Track citation share vs. Arize, WhyLabs, and Datadog.
4 posts/week from Krishna's profile. Signal-driven topics: AI governance news, production AI failures in the news, new model releases and their observability implications, lessons from scaling the Meta News Feed. Each post builds authority with the ML engineering audience.
Generate 20+ ad variants per sprint targeting ML engineers, VP Engineering, and CISOs across LinkedIn and Google. A/B test "control plane" messaging vs. "observability" messaging vs. compliance angles. Automatically promote winners and kill underperformers.
Track usage patterns across existing model monitoring customers. Trigger upgrade sequences when teams deploy agents or LLM workflows. Build champion programs targeting the ML engineers who already trust Fiddler to expand their use case.
Weekly scan of Arize, WhyLabs, Datadog ML, LangSmith, and emerging observability startups. Track ad spend changes, messaging pivots, feature launches, and developer community sentiment. Inform Fiddler's positioning response.
Weekly synthesis: which channels drive pipeline, which content converts ML engineers vs. CISOs, where budget is wasted. Board-ready metrics with AI-generated recommendations. The system gets smarter every cycle.
Traditional Marketing vs. MH-1
Traditional Approach
MH-1 System
Audit. Sprint. Optimize.
3 phases. Real output every 2 weeks. You see results, not decks.
AI Audit + Growth Roadmap
Full diagnostic of Fiddler's marketing infrastructure: SEO, AEO visibility across LLMs, paid, content, lifecycle, competitive positioning. Prioritized roadmap tied to pipeline and ARR targets. Delivered in 7 days.
Sprint-Based Execution
2-week sprint cycles. Sprint 1 ships AEO content targeting "AI observability" queries + LinkedIn thought leadership for Krishna. Sprint 2 adds paid LinkedIn campaigns targeting ML platform engineers and launches the agentic observability cross-sell sequence. Real campaigns, not presentations.
Compounding Intelligence
AI agents monitor your channels 24/7. They catch budget waste, detect creative fatigue, track AI citation changes vs. Arize and WhyLabs, and run A/B experiments autonomously. Week 12 is measurably better than week 1.
AI Marketing Operating System
3 elite humans + AI agents operating your growth system
Output multiplier: ~10x output at a fraction of the cost. The system gets smarter every week.
Month-to-month. Cancel anytime.
Common Questions
We already have a CMO and marketing team. How does MH-1 fit?
MH-1 amplifies what Kirti and the team are already doing well. Your existing team handles product marketing, analyst relations, and brand narrative. MH-1 adds the demand generation layer: paid acquisition, founder thought leadership at scale, AI citation optimization, lifecycle expansion campaigns, and competitive monitoring. Think of it as the execution engine that turns your product-marketing strengths into measurable pipeline.
What kind of results can we expect in the first 90 days?
Week 1: Full audit and roadmap. Weeks 2-4: First sprint ships AEO content targeting "AI observability" queries and LinkedIn thought leadership for Krishna. Weeks 4-8: Second sprint adds paid LinkedIn and Google campaigns plus agentic observability cross-sell sequences for existing customers. By day 90, all AI agents are running, you have measurable pipeline attribution by vertical and persona, and the system is compounding.
How do you handle marketing to a technical developer audience?
We don't write generic marketing copy for ML engineers. MH-1 pairs human experts who understand developer tooling with AI agents that research your market deeply. Content is technical, specific, and grounded in real observability workflows. We know the difference between model drift detection and agentic trace analysis, and our content reflects that. The AI agents also monitor developer communities and forums to inform what resonates.
Can we cancel anytime?
Yes. MH-1 is month-to-month with no long-term contracts. We earn your business every sprint. That said, compounding effects kick in around month 3 as the AI agents accumulate data and the system learns what works for Fiddler's specific audience segments and buying patterns.
How is this page personalized for Fiddler?
This page was researched, audited, and generated using the same AI infrastructure we deploy for clients. The channel scores, team mapping, growth opportunities, competitor analysis, and recommended agents are all based on real analysis of Fiddler's current marketing presence. We researched your leadership team, funding history, customer base, competitive landscape, and content strategy. This is a live demo of MH-1's capabilities.
You built the control plane for AI. Let's build the growth engine to match.
The system gets smarter every cycle. Let's talk about building it for Fiddler.
Book a Strategy CallMonth-to-month. Cancel anytime.