Hi, it’s Rick Koleta. Welcome to GTM Vault, a breakdown of how high-growth companies design, test, and scale revenue architecture. Read inside OpenAI, Anthropic, Meta, and Google, and by 27,000+ operators across 150+ countries building GTM systems that compound.
In 2023 the industry standard for revenue AI was to pass an entire call transcript into a language model and hope. Gorish Aggarwal tried it. It failed at producing something as basic as next steps unless you massaged the input first, feeding the model context about the output you wanted, the customer, and the deal. So he stopped treating the transcript as the product and started treating it as raw material. Six to eight months later the rest of the market was still dumping full transcripts into models and calling it AI.
That gap became the company. His read was that as models improve, the information you pass them improves alongside them. If that is true, the durable asset is not the model and it is not the recording. It is the structured understanding of your business that decides what gets passed in.
This is not an argument about transcription quality. It is an argument about which layer of the revenue stack is being repriced to zero, and Sybill spent eighteen months building the other layer while better-funded competitors shipped features.
Gorish Aggarwal is co-founder and CEO of Sybill. Sybill launched as an AI sales assistant for account executives, then spent eighteen months building an org-level context graph underneath it, a self-evolving layer connecting customers, team, and company process that updates daily from every new conversation. The bet was made with a fraction of the capital his category raised, which is the part that shaped the architecture rather than just the burn rate. Gorish now argues the next Cursor-scale company in GTM tech emerges from exactly this layer inside twelve to eighteen months, and says he is betting on it.
In GTM 48, Gorish breaks down why passing the full transcript into a model was never the product, what a context layer means operationally across its three pillars, why Sybill shipped an MCP connector into Claude’s directory instead of defending its own dashboard, why incumbents cannot bolt a context layer onto platforms architected before language models existed, where agents with complete context still produce garbage, what identity resolution is quietly doing to every CRM in the market, and the single roadmap question that let a smaller team out-build the field for eighteen months.
This is not a conversation about note takers. It is a conversation about which half of your revenue stack is the commodity and which half is the asset, and what that means before your next renewal cycle.
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Inside this episode
This episode maps the structural difference between recording what happened and understanding it, and what happens to a category when the first one becomes free.
Gorish opens with the 2023 realization. Passing a full transcript into a model did not work, and it failed in a specific way that mattered. Even getting usable next steps required feeding the model context about the desired output, the customer, and the deal alongside the raw text. The transcript was an input, not a signal. He assumed the market would catch on quickly. It did not. Six to eight months later the standard was still transcript in, output out. That lag is where the head start came from, and he is candid that he expected it to be much shorter.
We go deep on what a context layer means operationally, because the phrase has been diluted into meaninglessness over the last year. Gorish defines it as three pillars. Customers, meaning the prospects, accounts, and opportunities. Team, meaning every person from rep to CRO, their strengths, weaknesses, objectives, and preferences for how they work. Company process, meaning product lines, pricing, the objections that come back, market verticals, and the competitors you keep running into. Most revenue tools capture the first pillar and treat the other two as configuration. The graph only compounds when all three evolve daily against new conversations, and that daily evolution is the part that cannot be retrofitted.
We cover why he does not consider this a repositioning. Gorish pushes back on the framing directly. The technology moved and the product moved with it, and the visible change was that a second buyer appeared. Sybill started as an automation layer over an AE’s deals, helping one rep write a better follow-up and save time. As the org-level graph came together, the CRO and the VP of Sales became buyers of the same asset for entirely different reasons. He is honest that this made the offering muddier before it made it stronger.
We go into the MCP decision, which is the counterintuitive move in the episode. Most vendors Sybill’s size were building another dashboard. Sybill exposed its context layer through an MCP connector in Claude’s directory, then built an ingest MCP running the other direction so website data, Notion, Drive, and Slack flow in, get systematized against the graph, and become available downstream. Giving up the screen cost them depth of insight into how the work gets done. What they gained was a builder persona in RevOps creating agents on top of the layer, and those agents scale across an entire organization. One builder’s workflow becomes a thousand reps’ default. Gorish is clear that the interface still matters for a typical seller who wants the job done rather than a system to build, so this is a segmentation call rather than a religion.
We cover the demo Gorish says people love to see, which is not a demo of Sybill. It is running Claude twice against the same deals and the same calls, once with the Ask Sybill MCP connected and once without. The delta in output is the pitch. Nothing about that comparison requires the buyer to trust a vendor claim, and Gorish says MCP usage skyrocketed in a way that surprised the team.
We go into personal agents versus GTM agents and where they collide, which is the architecture question most teams have not thought about yet. Gorish runs five personal agents through an OpenClaw setup covering fundraising, people ops, GTM, product, and a personal assistant he calls Dobby. A personal agent knows his preferences and his relationships, so a LinkedIn message referencing this podcast gets connected to this conversation without being told. A GTM agent is organizational. It knows process, product lines, pricing, and objections, it ingests data sources that actively compete with each other, and it scales across hundreds or thousands of reps. His resolution of the ownership fight is the useful part. The company’s benefit is that no knowledge walks out the door when a strong rep leaves. The individual’s benefit is a preference layer they carry from company A to company B. No sane org graph imports a single rep’s perspective. It triangulates across fifteen to fifty and picks the best pattern per domain.
We cover the honest failure mode, and it is not the one people expect. Give an agent all the context in the world and it still fails at creativity. Genuinely novel choices, the strategic decision unique to this deal in this organization, remain human work. Gorish points at the LinkedIn auto-commenter era as the market learning this in public. Everything templated or close to templated, agents handle. The line sits exactly where critical thinking begins.
We go into why incumbents cannot ship this as a feature, and Gorish gets specific about architecture rather than hand-waving about innovator’s dilemma. Revenue platforms were designed before language models existed. Their data pipelines, storage models, indexing strategies, and application architecture were all optimized for capturing structured data and querying it. Continuously connecting people, conversations, emails, meetings, CRM changes, and outcomes into a living model of how a company win…
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