A decade closing enterprise B2B SaaS — $5M+ across 100+ deals — then building the GTM engine from the inside. Now I ship the agents and workflows that do the work: prospecting, call intelligence, launch packages, even the server this site runs on.
Not advice decks. Working systems, built on your stack and your sales motion — by someone who has carried the number, built the GTM engine, and shipped the agents. Every engagement starts with a conversation and ends with something running.
A structured diagnostic of where your pipeline leaks — across ICP, outbound, CRM hygiene, messaging, and sales process — and where AI agents actually belong in the fix (and where they don't).
Agents like the ones below — prospecting orchestration, call intelligence, executive briefs — built for your team, your tools, your motion.
The PRD→GTM pipeline, adapted to your product: positioning, messaging hierarchy, battlecards, and enablement generated from one source, in one pass.
Everyone in revenue has an opinion about AI. Far fewer have shipped an agent that survived contact with a real pipeline. These are in active use — figures are as measured; treat them as directional and ask me to show my work, live.
Manages a production VPS entirely through Telegram. Deploys sites, monitors health, manages DNS — with an exact-phrase approval gate and a git audit trail. No terminal needed.
Idea → live site in 10 min Inspect buildDecodes buyer intent, product gaps, and competitive pressure from call transcripts. Not a note-taker — a signal-intelligence layer feeding a continuous PMM and sales loop.
100+ calls decoded Inspect buildTurns a product requirements doc into a full launch package — positioning, messaging, battlecard, enablement — generated end to end from one source.
100–120 min vs. 2–3 days Inspect buildDiagnostic that finds where pipeline leaks across ICP, outbound, CRM, messaging, and process — with a ranked roadmap to more qualified meetings.
Pipeline leak diagnosis See the offerEnd-to-end product listing: visuals, copy, optimization, and loading, run as a single automated flow across 50–200 SKUs per batch.
~75% time saved on bulk uploads Inspect buildInvestment-bank stock research built for the retail investor: 40+ quality and valuation checks, a DCF, a mandatory bear case — every parameter graded.
40+ graded checks per stock Inspect buildConsolidates company research, executive priorities, industry trends, and recommended angles into concise, ready-to-use meeting briefs.
Prep −70% · conversion +50% Inspect buildAutomates account research, pain-point mapping, personalized outreach, discovery planning, and objection handling. The AE reviews and sends.
~70% time saved per lead Inspect buildMost operators have one of the three. A few have two. The edge is all three pointed at the same problem — which is why everything on this page is a working system, not a slide.
I'm Agnishwar. Most people call me AB.
I've spent over a decade on both sides of B2B SaaS revenue. I closed $5M+ across 100+ enterprise deals, selling compliance and legaltech SaaS to CXOs and the kind of multi-stakeholder buying committees that treat a signature like a hostage negotiation — at Unilever, Siemens, Tata Group, Mahindra, Vodafone, P&G, L&T, and Brookfield. Then I built the GTM engine from the inside: positioning, competitive battlecards, and enablement that lifted competitive win rate by 40% and supported $5M+ in new ARR at CleverTap.
For the past year I've been rebuilding how I operate — AI as the core operating layer for revenue and GTM work, not a productivity add-on you bolt on and forget. Agentic workflow design, prompt architecture, and tool orchestration across Claude, ChatGPT, and Gemini, instead of politely asking a chatbot for help.
The point isn't the tooling. It's the combination: quota-carrying sales instinct, GTM systems fluency built from the inside, and AI-native build capability most operators in sales or marketing don't have. It's the difference between describing what AI should do for a revenue team and having built it, shipped it, and measured what it changed.
Legaltech, GRC, and customer engagement SaaS — full-cycle quota-carrying sales through enterprise GTM leadership.
Published work on cyber risk, compliance, AI-driven customer engagement, and owning your own infrastructure.
What the SEC's finalized cybersecurity disclosure requirements actually demand of public companies, and where most compliance teams are still behind.
ReadWhat changed in the 2.0 update to the NIST Cybersecurity Framework, and what it means for risk and compliance programs.
ReadOn pulling Snowflake, BigQuery, and Redshift data into live campaigns in real time, so AI agents act on current customer data instead of stale exports.
ReadOn adding up the subscriptions, migrating to a self-hosted VPS, and why the case for owning your infrastructure is practical, not ideological.
Readmore → MetricStream archive | CleverTap archive | ayebee.me blog
Blunt, mildly quirky, and allergic to taking myself too seriously even when the work is serious. I compulsively check the wifi wherever I land (occupational hazard of running your own stack) and have been known to hug a tree mid-hike without warning. I'm the friend people call for decisions — trip planning, career pivots, the "should I actually do this" ones — because I'll bring the same evidence-gathering instinct to a friend's dilemma as to a client's deal.
Off the clock: chess, space, photography, film, an unreasonably serious coffee ritual, music, and travel built around character and walkability over luxury. I also run a fully self-hosted digital stack and a podcast, AyeBeeTalks. I like owning my infrastructure — literally and professionally.
If you're figuring out where AI agents actually belong in your revenue org — or you want one of these systems walked through live on your own data — reach out. Worst case, you leave with a sharper diagnosis than you came with.