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Ed Brandman.
Co-Founder · ToltIQ

Ed Brandman is the Co-Founder of ToltIQ.

Prior to founding ToltIQ, Ed served as Partner and Chief Information Officer at KKR from 2007-2018 where he had responsibility for global technology, data strategy and Operations supporting all firm-wide activities. During his tenure he was a member of the firm’s Operations/Risk management committee and served on the Boards of Ipreo and Artivest.

Before joining KKR, Ed served as Managing Director in the Financial Services Advisory group at PwC, where he focused on best-in-class technology strategies to support capital markets and trading activities for clients. He had previously been a Managing Partner at OMT Partners, a consulting firm servicing the brokerage industry.

Earlier in his career, Ed co-founded and served as CEO of Renaissance Trading Technologies, a technology firm that developed trading software for Nasdaq market makers. Before founding RTT, he served as the Chief Information Officer, Head of Electronic Trading and Director of Operations of Robertson Stephens & Co. Ed was also an early Member of the NASDAQ Advisory Board. Prior to joining Robertson Stephens, Ed was responsible for equity trading technology at J.P. Morgan, where he began his career. While there, Ed helped launch FIX connectivity with institutional clients and designed the first touchpad-based trading platform.

Guest
Ed Brandman
Co-Founder
Company:
ToltIQ
Location:
New York, New York, United States
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How ToltIQ Built a Referral Engine That Ran for Two Years Before They Hired a Single Outbound Rep

Ed Brandman retired in 2018. He bought a truck, loaded a tent and a Belgian Malinois into the back, and spent five years driving 4,000-mile loops across the country visiting 27 national parks. He was done with financial services.

Then his son called.

Running cybersecurity at Duolingo and tracking the early ChatGPT models, he had one question: what was the most painful part of working at KKR? The answer was immediate — due diligence. Manual, document-intensive, and untouched by technology. That conversation became ToltIQ, an AI-native platform built for private markets due diligence.

In a recent episode of BUILDERS, Ed Brandman, Co-Founder of ToltIQ, walked through the GTM decisions that took the company from eight founding clients to 8–10 inbounds per week with no outbound motion — and what he learned selling AI into one of the most skeptical industries in the world.

Positioning against the obvious play

Most AI vendors in financial services pitch into operations — workflow automation, back-office efficiency. Ed went the other direction from day one, and the reasoning was precise.

"People gravitate towards the operational processes of firms," he said, "and that actually isn't the biggest bang for the buck right now. The biggest bang for the buck is on the front end of businesses — the diligence process, the capital raising process, how you interact with your investors, the sourcing side of the business."

This wasn't a hypothesis. It came from Ed having been brought over the wall at KKR on diligence activities in addition to running technology and operations — a vantage point that showed him exactly where the most labor-intensive, high-stakes decisions were made. That knowledge became ToltIQ's positioning before they had a product to demo.

The hiring policy that did the trust-building

Before ToltIQ had a sales playbook, Ed made a structural call: 70% of the team — including engineers, the CFO, and the client organization — would come from inside the private markets industry they were selling into.

When Ed sat across from investment professionals, his opening wasn't a product pitch. It was: "Not only do I understand your pain, I sat on your side of the fence."

Crucially, that fluency wasn't founder-dependent. Clients encountered the same domain credibility at every stage of the relationship — from the first call through implementation — because the engineers and client team spoke the same language they did.

Transparency as a GTM strategy, not a value

ToltIQ entered the market when the underlying models were genuinely limited, and Ed named those limits explicitly to every client.

"Two and a half years ago, I had to say to clients: I can't read and understand a chart or a graph, I can't process a 300-page credit agreement."

Every time the models improved, he updated clients explicitly rather than quietly expanding capabilities.

"We're very transparent and honest with them about what we can and can't do. They're on the journey with us, we're learning alongside them."

In a market where buyers have accumulated real scar tissue from AI overpromising, being the vendor who names what doesn't work yet is a harder discipline than it sounds — and a more durable advantage than most features.

The referral engine they never designed

Ed's first eight clients were former KKR colleagues who'd moved on to run their own firms. That network had a ceiling. What happened next, he said, he didn't see coming.

"I think because of the time we spent with clients, the way we onboarded firms, all of a sudden we started getting eight to 10 inbounds a week without a cold calling plan. And everyone kept saying, 'I was referred by X, I was referred by Y.'"

The referral engine — built entirely on the quality of how ToltIQ engaged with early clients — sustained the company through all of 2025 and into 2026. ToltIQ didn't launch an outbound campaign until late 2025, and not because referrals had peaked. They added outbound because they wanted to go from 10 inbounds a week to 20.

"If we really wanted to go 2x, we needed to develop the outbound campaign as well."

That outbound team is two people. Running Claude Enterprise, ChatGPT Enterprise, and Gemini Enterprise — plugged into HubSpot, Gmail, Slack, and Google Drive — they've driven a 50% increase in results. Ed estimates the same output would require a team three to four times the size without AI. ToltIQ has 30 people in total. Their closest competitor runs more than 100.

What 50% unplanned usage actually tells you about onboarding

One of the more operationally useful findings from ToltIQ's growth: roughly half of what clients do on the platform wasn't what Ed anticipated when they built it.

"Really only about 50% of what a client is doing on our platform are what I thought they would be doing. And that's because the other 50%, they learn through discovery."

ToltIQ's response was deliberate — provide guidance on use cases without locking clients into prescribed workflows. Power users found the highest-leverage applications. Episodic users found consistent time savings. The implication for founders building onboarding: designing too much control into early adoption actively narrows the ceiling of how clients will use the product.

What selling to CIOs actually requires

Ed was specific here in ways that generic sales advice isn't. Three rules from someone who used to be the buyer:

Security is not a checkbox — it's a reputation variable. "If you get it wrong, you'll damage your reputation in the industry." In private markets, where the buyer community is small and interconnected, that damage compounds fast.

Empathy before pitch. "People have AI exhaustion given the transition that's happened over the last two years. Have empathy for the challenges that the CIOs and CTOs have been experiencing." CIOs are managing board pressure to adopt AI while keeping existing systems running — a constraint most founders selling to them have never operated under.

Know the vertical at workflow depth. "If you're going to sell in private markets, don't think that private equity and private credit are the same thing. The business knowledge and the workflow matters."

His closing point was the most forward-looking: OpenAI and Anthropic are building platforms that will erode the edges of SaaS solutions layered on top of them. "Think long and hard about what your moat is." For ToltIQ, part of that moat is a vector database infrastructure Ed didn't know existed when the company was founded — and client relationships built through two years of high-touch, referral-driven growth that no model update can replicate.

Listen to the full conversation with Ed Brandman on BUILDERS.

Eight takeaways from this conversation.

Actionable for Fintech and Banking founders

  1. The highest AI leverage in financial services isn't where most founders look.
    Ed's conviction from the start — drawn directly from his time inside KKR — was that the front end of investment workflows (diligence, capital raising, investor relations, sourcing) would yield far more from AI than operational back-office processes. That's the opposite of where most AI vendors pitch. If you're building for a specialized vertical, time spent inside the industry isn't just helpful for credibility — it's how you identify where the real leverage is before you build anything.
  2. Hire the domain, then train for the tool.
    70% of ToltIQ's team — including engineers and the client-facing org — came from inside private markets. Ed's view: if clients can sit across from your team and feel understood before the demo starts, you've already cleared the biggest hurdle in enterprise sales. This wasn't incidental. It was a deliberate hiring philosophy from day one, and it scaled the business before there was a sales playbook.
  3. Referral growth at this scale requires earning it, not engineering it.
    ToltIQ had no outbound motion for more than two years and was still fielding 8–10 inbounds per week by the end of 2025. Ed's explanation: the time they invested in onboarding clients — working through problems with them, being transparent about limitations, iterating in the open — made clients want to refer peers. In tight-knit professional networks like private markets, the quality of the relationship drives referrals more than the quality of the product alone. The referral engine sustained the company through 2025 and into 2026 before they felt the ceiling.
  4. Transparency about product limitations is a GTM strategy, not just ethics.
    Two and a half years ago, Ed was telling clients directly: the model can't read charts, can't process a 300-page credit agreement. And every time the underlying models improved, he updated clients explicitly. The result: clients stayed on the journey because they trusted they were getting the real picture. In a market suffering from AI hype fatigue, being the founder who tells buyers what your product can't do yet is a credibility move that compounds.
  5. 50% of your clients' use cases will surprise you — build for discovery, not prescription.
    Only about half of what ToltIQ's clients do on the platform matches what Ed originally expected. The rest emerges as users — especially power users — find their own applications. Ed's approach is to provide strong guidance on use cases without being prescriptive, letting clients explore the edges of what the platform can do. The unexpected use cases are often the stickiest. Over-engineering the onboarding to control usage is how you leave the most valuable adoption patterns on the table.
  6. Two outbound reps, 50% pipeline growth — only possible because of AI-on-AI.
    When ToltIQ finally did launch outbound, they did it with two people. They plugged HubSpot, Slack, Gmail, and Google Drive into Claude Enterprise, ChatGPT Enterprise, and Gemini Enterprise — using AI to research prospects, map relationships through past email history, and surface warm paths that would otherwise require a much larger team. Ed estimates the same output would require 3–4x the headcount without it. The lesson isn't just that AI enables efficiency — it's that you should pressure-test whether you've actually exhausted your referral ceiling before you build out a sales team.
  7. Three rules for selling AI into financial services CIOs — from someone who was one.
    Ed's framework, stated directly: First, don't treat security as table stakes — in financial services, getting it wrong damages your reputation permanently and fast. Second, don't oversell. CIOs are experiencing AI exhaustion after two years of vendor noise; empathy for their situation opens more doors than a feature list. Third, and most important — don't show up talking technology if you haven't done the work to understand the business. Private equity and private credit are not the same buyer. The workflow, the language, and the pain are different. Founders who can't speak to that distinction don't make it past the first conversation.
  8. Know where the Frontier model providers end and your moat begins.
    Ed closed with a warning that's easy to miss: OpenAI and Anthropic aren't just model providers anymore — they're building platforms, and those platforms are going to erode the edge of many SaaS solutions built on top of them. Founders need to have a clear, honest answer to where their defensibility lives independent of the model layer. For ToltIQ, it's the vector database infrastructure, the domain-specific training, and the depth of the client relationship. For founders still treating the model as the moat — that's a fragile position.

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