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Cent Capital

Cent Capital

Democratizing Financial Intelligence for a More Equitable World.

Published Oct 11, 2025

Introduction: Seeing the Patterns from the Inside

By Shivam Singh, Founder & CEO of Cent Capital, Former Head of Go-to-Market Strategy, Generative AI at Amazon Web Services

I remember a meeting in a glass-walled conference room, overlooking a sprawling corporate campus. I was sitting across from the CIO of a Fortune 500 industrial giant, a company that moves literal tons of steel and concrete around the world. We were there to talk about generative AI. The public conversation at the time was dominated by chatbots writing sonnets and AI generating fantastical images. But this CIO wasn’t interested in poetry. He leaned forward and said, “I have 30 years of proprietary engineering data locked in PDFs and legacy systems. My competitors would kill for it. Your AI can’t see it, I can’t risk it leaking out, and my regulators will crucify me if it produces a single inaccurate safety specification. Forget the art. How do you solve that?”

That conversation crystallized the chasm between the public spectacle of generative AI and the pragmatic reality of enterprise adoption. It was a gap I lived in every day. As Shivam Singh, former head of go-to-market strategy for Generative AI at Amazon Web Services (AWS), my job wasn’t to sell flashy demos. It was to navigate the complex stakeholder dynamics within the world’s largest companies and translate cutting-edge AI into secure, scalable, and ROI-positive business outcomes.1 My team and I were responsible for driving awareness, adoption, and revenue by building trust with customers who measured risk in billions of dollars and timelines in decades.

After years on those front lines, I saw the same patterns emerge in meeting after meeting, across every industry. The core challenges enterprises faced were universal, and the gaps in the market were becoming glaringly obvious. The decision to launch Cent Capital, a fintech startup, wasn’t a career change; it was the logical continuation of that work. The most effective way to accelerate the AI revolution wasn’t to build another feature at a hyperscaler, but to build the focused, agile products that solve the critical missing pieces of the enterprise AI stack.

This post deconstructs the three most critical lessons from my time in the AWS crucible. These lessons, learned from marketing one of the world’s most comprehensive AI platforms, directly shaped the product vision and go-to-market strategy of Cent Capital. They are a playbook for any entrepreneur looking to build an enduring company in the age of enterprise AI.

Part 1: The AWS Crucible — Forging an Enterprise AI Go-To-Market Strategy

Selling a revolutionary technology to a market that is inherently risk-averse and relentlessly focused on ROI is a unique challenge. At AWS, we had to build a strategy that addressed enterprise fears before we could even begin to speak to their ambitions. This experience provided a masterclass in what it takes to win in this market.

Lesson 1: The Enterprise Buys Solutions, Not Spectacles

The single biggest mistake in the AI market today is conflating a consumer-facing “wow” demo with an enterprise-ready product. Enterprises are not buying large language models (LLMs); they are buying solutions to business problems that are secure, compliant, scalable, and deeply integrated into their existing workflows. This was the absolute cornerstone of our entire go-to-market strategy at AWS.

Our messaging never led with abstract capabilities. It led with trust. The famous quote from AWS’s marketing chief, Julia White, “ChatGPT is great, but, you know, you can’t use it at work,” wasn’t just a clever competitive jab; it was the entire enterprise marketing strategy distilled into one sentence.3 It spoke directly to the primary anxieties of CIOs and Chief Information Security Officers (CISOs) who were grappling with the terrifying specter of proprietary data leakage, intellectual property (IP) infringement, and a complete lack of governance over a powerful new technology.4 Our success depended on turning this fear into a clear value proposition.

This marketing message was a direct reflection of a product portfolio meticulously designed to be a suite of solutions, not just a collection of tools. It was a tiered offering engineered to meet customers at every stage of their AI maturity.

Technical Deep Dive: The AWS Gen AI Toolkit as a Solution Portfolio

• Amazon Bedrock: The Secure Gateway to a Multi-Model World The primary problem we heard from enterprise customers was a dual fear: the terror of being locked into a single model provider (like OpenAI) and the non-negotiable requirement to keep their proprietary data within their own secure environment.4 They saw the power of models like Claude and Llama 2 but were paralyzed by the integration complexity and security risks of calling third-party APIs. We positioned Amazon Bedrock as the definitive solution. It is a fully managed service that provides access to a diverse range of foundation models—from Anthropic, Cohere, Meta, Stability AI, and Amazon’s own Titan family—all through a single, secure API. Our marketing was built on three pillars that directly addressed these customer pain points:

1. Choice & Future-Proofing: The message was simple and powerful: “Choose the best model for the job, and seamlessly swap it out as better ones emerge”. This transformed the fear of lock-in into a strategic advantage.
2. Security & Privacy: We hammered this home relentlessly. “Your data is never used to train the original base models. All data is encrypted in transit and at rest, and everything can be run within your own Amazon Virtual Private Cloud (VPC)”. This was a direct countermeasure to the number one enterprise objection.
3. Managed RAG & Agents: We understood that model “hallucinations” were a deal-breaker for any serious business use case.4 So, we didn’t just market features; we marketed solutions to this core problem. Capabilities like Knowledge Bases for Amazon Bedrock were positioned as a managed Retrieval-Augmented Generation (RAG) workflow, providing a clear path to building trustworthy, accurate AI applications grounded in a company’s own data.6

• Amazon SageMaker: The Industrial-Grade AI Factory
• While Bedrock was designed for broad accessibility, we knew that our most sophisticated customers—in sectors like financial services, pharmaceuticals, and automotive engineering—had needs that went far beyond API access. They don’t just want to use models; they need to build, train, fine-tune, and govern them with the same rigor they apply to any other piece of mission-critical software. For this high-end market segment, Amazon SageMaker was our answer. We marketed SageMaker not as a tool, but as a comprehensive, end-to-end platform for serious machine learning development. The message was about control, maturity, and industrial-grade MLOps. We highlighted features that addressed the entire ML lifecycle: SageMaker Ground Truth for data labeling, automatic hyperparameter tuning, SageMaker Debugger for deep visibility, and, critically, SageMaker Clarify for bias detection and model explainability.8 This directly addressed the enterprise demand for accountability and transparency, a major weakness of opaque, “black-box” AI systems that keep regulators and compliance officers up at night.4
• Amazon Titan: The First-Party Option for Trust and Optimization
• Finally, we recognized that for some enterprises, particularly in heavily regulated industries or the public sector, using any third-party model carried a perceived risk. They wanted a powerful, general-purpose model from the same provider they already trusted with their core infrastructure. The Amazon Titan family of models (including Titan Text for generation and Titan Embeddings for semantic search) was our strategic answer to this need. We marketed Titan as a high-performance, enterprise-safe model, pre-trained on vast datasets and built with responsible AI principles from the ground up. The message was one of assurance: “A powerful, secure starting point, fully integrated and supported by AWS.“12 This provided an essential on-ramp for customers beginning their Gen AI journey.

This deliberate, multi-layered product strategy was not an accident. It was a sophisticated approach to market segmentation. A company could begin its journey with a simple, low-risk application on Bedrock using a Titan model. As its needs matured, it could leverage Bedrock’s RAG capabilities to connect to its own data. And if it eventually needed to build a highly custom, proprietary model for a core competitive advantage, it could “graduate” to the full power of SageMaker. This tiered approach allowed AWS to capture workloads and create a sticky, defensible ecosystem at every stage of a customer’s AI journey.

Lesson 2: The Ecosystem is the Engine

At the scale of AWS, a direct sales force can only do so much. The true engine of growth, the force multiplier that enables exponential scale, is the partner ecosystem. My role in partner marketing was a daily lesson in this reality.1 Our goal was not just to co-brand with partners; it was to weaponize them with the tools, knowledge, and incentives to build thriving businesses on top of our platform. We scaled adoption by enabling thousands of consulting partners, systems integrators (SIs), and independent software vendors (ISVs) to become our extended sales and implementation force.

The flywheel was a deliberate, multi-part strategy:

• Systematic Partner Enablement: A significant portion of my team’s effort was dedicated to creating and disseminating “partner activation playbooks”. These weren’t just marketing brochures. They were comprehensive guides on messaging, solution architecture, and best practices for selling and implementing Gen AI solutions. We weren’t just selling to our partners; we were teaching them how to sell through to their own customers. This systematic enablement turned a finite internal sales team into a global army of motivated evangelists.
• Strategic Market Seeding: Programs like the AWS Generative AI Accelerator were far more than just a corporate social responsibility initiative.14 Strategically, they were a masterstroke. By providing up to $1 million in AWS credits, deep technical mentorship, and go-to-market support to promising early-stage AI companies, we were achieving several critical business objectives simultaneously:

1. Platform Loyalty: We ensured that the next wave of category-defining AI companies was built natively on AWS from day one.
2. Marketing & Social Proof: We cultivated a rich pipeline of high-value case studies and success stories that we could use to prove the platform’s value to larger enterprise customers.
3. Market Intelligence: We gained invaluable early insights into emerging use cases and market trends, which served as a real-time feedback loop into our own product strategy.
4. Customer Pipeline: We were incubating our own future high-growth customers.

This approach reveals a profound truth about platform strategy. For a company like AWS, the most valuable marketing isn’t about what AWS can do, but about what others can do with AWS. The strategy is a deliberate shift from being a “tool provider” to becoming an “economy creator.” AWS understands that the platform with the most vibrant, innovative, and successful ecosystem ultimately wins. Their go-to-market motion is less about selling their own services directly and more about fostering a thriving marketplace of solutions built upon those services. For entrepreneurs, the lesson is clear: your ability to become part of, or create, a larger ecosystem is a primary determinant of your long-term success.

Part 2: The Cent Capital Blueprint — Translating Market Signals into a Product Strategy

Every day at AWS was a firehose of market intelligence. I was on the front lines, listening to the unfiltered pain points of enterprise customers. They weren’t asking for more creative chatbots or faster image generators. They were asking the hard, unglamorous questions that define real-world adoption.

From Customer Pain Points to Investment Pillars

The questions were relentless and consistent:

• “How do I stop this thing from confidently making up facts in a legal brief?” 4
• “How do I integrate this with my 20-year-old SAP system without a two-year, multi-million dollar project?” 4
• “How do I prove to my regulators that our AI-driven loan approval process isn’t biased against protected groups?” 4
• “Our proof-of-concept was amazing, but the projected cost of running this at scale would bankrupt us. How do we manage inference costs?” 4

Listening to these challenges day after day led to an epiphany: the most valuable and defensible startup opportunities were not in the model layer itself, which was rapidly becoming a commoditized arms race between a few tech giants. The real, durable value was being created in the application and infrastructure layers that solved these painful, universal enterprise problems. This insight is the absolute bedrock of the Cent Capital product thesis.

To systematize this understanding, we developed the Enterprise Gen AI Adoption Matrix. It serves as both a diagnostic tool for founders and a clear illustration of our product focus, mapping the most common adoption blockers to the types of solutions needed.

The Cent Capital Mission: Building the Essential Infrastructure for Enterprise AI

This matrix directly informs our product strategy, which is focused on three core pillars that address the foundational needs of the AI-powered enterprise.

• Pillar 1: The Trust and Safety Layer As the matrix clearly shows, the biggest hurdles to widespread enterprise adoption are not about model capability but about mitigating risk. Therefore, our first and most important product pillar is building the “seatbelts and airbags” for enterprise AI. We are actively building solutions for AI security (detecting prompt injections, data poisoning, and model denial-of-service attacks), governance and auditability (creating immutable logs and clear explanations of model behavior), and compliance (building tools to ensure AI systems adhere to regulations like GDPR and HIPAA). These are the products that get a CISO to say “yes” to an AI project.4
• Pillar 2: Verticalized AI Agents & Workflows Generic AI is a commodity. Durable value is created by applying AI to solve specific, high-value business problems with deep domain expertise. Enterprises don’t need another generic chatbot; they need AI that understands the unique language, processes, and regulatory constraints of their industry.16 We are not just an “AI company” at our core, but a “fintech company that uses AI.” We are obsessively focused on a single, critical workflow, such as automating the post-call summarization and action-item extraction for financial services contact centers 18 or generating compliant, SEO-optimized product descriptions for regulated e-commerce sectors.16 Our defensible moat isn’t the underlying model; it’s the deep domain expertise and the proprietary data flywheel we build around a specific business process.
• Pillar 3: AI-Native Operations (Practicing What We Preach) We believe that to build a credible, cutting-edge product in the AI space, you must be an AI-native firm yourself. We are building Cent Capital from the ground up to leverage AI across our entire operation, giving us an edge and deeper empathy for our customers. We use AI-powered platforms for market analysis, leveraging predictive analytics to identify emerging trends and opportunities.19 We use AI tools during product development to analyze vast datasets on competitive landscapes and patent filings.20 And we use AI to support our customers, providing them with data-driven strategic recommendations on everything from GTM strategy to operational efficiency.

Case Study: Building FinLLM on Amazon Bedrock To truly practice what we preach, we are building Cent Capital as an AI-native company from the ground up. A prime example of this is our internal development of FinLLM, a specialized Large Language Model purpose-built for the financial services industry.22 We recognized early on that generic, off-the-shelf models, while powerful, lack the nuanced understanding of financial jargon, regulatory constraints, and the high-stakes accuracy required for tasks like compliance monitoring or investment analysis.24

Building FinLLM was a direct application of the lessons learned at AWS. Instead of starting from scratch, which is resource-intensive and unnecessary, we leveraged the powerful, managed services of Amazon Bedrock to build our specialized model.

• Foundation and Flexibility: We started by selecting a high-performance foundation model from the diverse options available through the single, secure Bedrock API.25 This gave us a state-of-the-art base without the overhead of managing complex infrastructure.
• Secure Customization with Fine-Tuning: The key to specialization was customization. We used Bedrock’s fine-tuning capabilities to train our chosen base model on our proprietary, curated financial datasets.27 This process, conducted within our secure AWS environment, allowed us to imbue the model with deep domain knowledge, teaching it to understand complex financial documents and follow specific industry-compliant response formats.29
• Accuracy through Retrieval-Augmented Generation (RAG): To ensure our model’s outputs are not just fluent but factually grounded and verifiable—a non-negotiable in finance—we implemented a RAG architecture. Using Bedrock’s native capabilities, we connected FinLLM to our internal knowledge bases, allowing it to retrieve and cite real-time information from trusted sources before generating a response.25
• Scalable and Cost-Effective Deployment: By building on a serverless platform like Bedrock, we architected a solution that is both highly scalable and cost-effective, allowing us to serve real-time insights without the cost of provisioned infrastructure.32

FinLLM is now the core intelligence layer for our own next-generation agentic AI framework, powering everything from our internal analytics to our customer-facing products.22 This project is a living testament to our thesis: the future of enterprise AI lies in securely customizing powerful foundation models to solve specific, high-value vertical challenges.

Part 3: The Playbook for AI Entrepreneurs

My experiences, both as Shivam Singh, former head of go-to-market strategy for Generative AI at Amazon Web Services, and now as the founder of the fintech startup Cent Capital, have given me a clear perspective on what it takes to build a successful enterprise AI company. For founders navigating this complex landscape, here is my direct, actionable advice.

Guidance for Building an Enterprise AI Company

• 1. Solve a Workflow, Not Just a Task Too many startups are building features, not companies. Don’t build a slightly better “summary generator.” Instead, build a platform that automates the entire “quarterly board report preparation” workflow for enterprise finance teams. This means building deep integrations with financial data sources like ERPs and accounting software, understanding the context of financial reporting, generating text and visualizations, and producing a final output that fits a specific, high-stakes business process.16 The durable value is not in the text generation; it’s in reducing the friction and error rate of the entire end-to-end workflow.
• 2. Build for the CISO and the CIO First, the Business User Second Your brilliant, innovative product is utterly useless if it cannot pass a rigorous security review or be integrated into the existing, often archaic, tech stack.4 From day one, you must have clear, compelling answers to the questions every CISO and CIO will ask: Where does my data live? Who has access to it? Can you provide immutable audit logs? What are your integration APIs? Your first sales deck should have a slide titled “How We Keep You Safe, Compliant, and Integrated.” This proactively addresses the very issues that kill the vast majority of enterprise AI pilots before they ever see the light of day.10
• 3. Your Moat is Your Data Flywheel, Not Your Model In a world with increasingly powerful open-source models like Llama and Mistral, and easy access to state-of-the-art models through APIs like Bedrock and OpenAI, the specific model you use is not a long-term differentiator.15 Your defensibility—your moat—comes from creating a product that captures unique, proprietary data through its very use. This could be user feedback on the quality of generations, interaction data from a complex workflow, or domain-specific information that no public dataset contains. You must have a clear strategy for how you will use this data to continuously fine-tune your models, improve your product, and create a virtuous cycle of improvement that competitors cannot easily replicate.

Conclusion: The Next Act of the AI Revolution

The first, explosive wave of the generative AI revolution was about demonstrating the raw, almost magical, power of the technology. It captured the world’s imagination. But that was just the opening act.

The next, far more valuable wave will be about the hard work of applying that power to solve real-world enterprise problems in a way that is secure, reliable, integrated, and cost-effective. The opportunities for founders in this new era are immense, but they lie not in chasing the hype of ever-larger models, but in building the essential, often unglamorous, infrastructure and domain-specific applications that will power the AI-enabled enterprise for the next decade.

At Cent Capital, we are building for the enterprises that understand this distinction. If you are a business leader looking to solve these critical, foundational problems of trust, security, workflow, and cost—we want to talk to you. The future of enterprise AI is being built today, and we are here to build it.

Works cited

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