What SaaS Would We Build Today? A CTO's Perspective

Every year thousands of founders build products. Most of them fail before reaching meaningful revenue.

The failure is rarely about execution. It is about starting with technology instead of problems. A founder who is excited about a new AI model, a new framework, or a new platform often builds something technically impressive that nobody will pay for.

The question that matters is a different one: if we were starting from zero today, what would we actually build and why?

This article is our honest answer. Eight opportunities we would evaluate seriously, the criteria we use to assess them, and the three we would actually start building if we had 90 days.

The Criteria We Use Before Choosing Any SaaS Idea

We evaluate every opportunity against eight criteria before committing to it. Most ideas fail two or three of these. The good ones pass all eight.

Market demand tells us whether businesses are actively looking for a solution. Not passively interested actively searching, evaluating, and buying. The signal is straightforward: are companies paying for this problem to be solved today?

Customer pain measures how much the problem hurts. A mild inconvenience does not generate budget. A problem that costs a company time, money, or risk creates urgency and urgency shortens sales cycles.

Existing spending confirms that the market is real. If businesses already pay for this problem through software, outsourcing, or manual labor, you know two things: the problem exists and the budget exists.

Competition tells us how difficult the environment is. Some competition is healthy it proves the market. Too much generic competition with no clear differentiator available is a warning sign.

Time to MVP matters for cash flow and learning velocity. An MVP that takes eighteen months to build before generating customer feedback is a risky bet. We prefer opportunities where a useful version can be in front of customers in eight to twelve weeks.

Scalability tells us whether the business economics improve as we grow. Does the product get better with more customers? Does the cost structure improve at scale? Can we serve ten times as many customers without ten times the cost?

Retention potential tells us how sticky the product will be. Products embedded in daily operations are hard to leave. Products used occasionally for a task that might change are churnable. We want products that become harder to leave over time.

Distribution difficulty tells us how hard it will be to reach customers. A product that can be sold through a founder's existing network, through inbound content, or through a clear and accessible channel is a more capital-efficient business than one requiring a large enterprise sales team from day one.

Opportunity #1: AI Requirements-to-MVP Platform

The problem: translating a business idea into a software specification is one of the most expensive and time-consuming early steps in any startup or enterprise product build. Founders write vague specs. Engineers interpret them incorrectly. The result is misalignment, rework, and wasted budget.

The platform: an AI-assisted tool that takes a business idea or problem description and generates a structured product requirements document, user stories, feature list, architecture recommendations, and effort estimates. Not a finished spec a 70% first draft that a founder or product manager can review, refine, and hand to engineering.

The market: startup founders who lack product management experience, product managers at growing companies who want to move faster, and agencies who need to generate client proposals and SOWs efficiently. All three groups currently spend significant time on this problem manually.

Revenue potential: $50–$500 per month for solo founders and freelancers; $500–$3,000 per month for teams and agencies. Both segments have real willingness to pay because the time savings are quantifiable.

Challenges: the output quality depends on the quality of the input. Users who provide vague descriptions get vague specs. The product needs strong guidance on how to provide good input, and it needs human review to catch the gaps AI inevitably creates in domain-specific requirements.

Why we like it: the problem is universal across every software team, the ROI is clear, and modern AI tools make a genuinely useful first version achievable quickly. The tool can improve continuously as more requirements documents flow through it.

Opportunity #2: Technical Debt Monitoring Platform

The problem: engineering teams accumulate technical debt silently. Outdated dependencies, deprecated API usage, security vulnerabilities in third-party libraries, and architectural decisions that made sense two years ago now create risk. Most teams have no systematic visibility into this debt.

The platform: continuous monitoring of code health across dimensions that matter to both engineering teams and business leaders. Risk scoring by component, dependency vulnerability tracking, architecture quality assessment, code complexity trends, and security posture presented in a way that non-technical stakeholders can understand.

Target customers: engineering leaders at Series A through Series C companies who need to communicate code health to boards and investors, and who want to make data-driven decisions about technical investment. This is a problem that is universally felt and poorly measured.

Revenue potential: $500–$5,000 per month depending on team size and feature set. Engineering tools with a clear business case command strong prices.

Why this market is growing: the rise of AI-assisted development means more code is being generated faster. Teams that use Claude Code, Cursor, and similar tools ship features at higher velocity and accumulate quality issues at the same velocity. The need for quality visibility is growing proportionally.

Opportunity #3: AI Integration Hub

The problem: the average mid-market business uses 40 or more software tools. They are connected inconsistently, if at all. Data is duplicated, out of sync, and siloed. When integrations break and they break constantly nobody knows until a customer complains or a report produces nonsense.

The platform: an integration monitoring and automation hub that watches the health of a company's integration stack, alerts when syncs fail or data diverges, and provides a workflow automation layer that moves data between systems intelligently. Not a replacement for Zapier a layer above it that provides visibility, reliability monitoring, and smarter routing.

Market demand: every operations team at a growing company deals with this problem. The specific pain is invisible until it causes a downstream failure a customer record that does not update, a payment that does not reconcile, an order that does not fulfill. The pain is real and the cost of a missed integration failure can be significant.

The opportunity is strongest for a vertical-specific version. An integration hub built specifically for e-commerce businesses monitoring Shopify, Stripe, inventory systems, and ERP connections is more defensible and more valuable than a generic integration platform competing with Zapier directly.

Revenue potential: $300–$3,000 per month per customer, depending on the number of integrations monitored and the volume of workflows automated.

Opportunity #4: Compliance Automation Platform

The problem: SOC 2, ISO 27001, GDPR, HIPAA, and vendor security assessments have become prerequisites for selling to enterprise customers. The preparation process is manual, repetitive, and expensive. Evidence collection for a SOC 2 audit can take weeks of engineering and operations time.

The platform: continuous compliance automation that monitors required controls, collects evidence automatically across connected systems, manages policy documentation lifecycle, prepares audit packages, and tracks remediation of identified gaps.

Why businesses pay: compliance is not optional for companies that sell to enterprise buyers. Missing a renewal date or failing an audit has direct revenue consequences. The spending on compliance consultants, dedicated staff, and manual processes is substantial and it is ongoing.

The strongest starting position: pick one framework and one customer segment and dominate that combination before expanding. SOC 2 for B2B SaaS companies under 100 employees is a large, accessible market with no dominant affordable solution.

Revenue potential: $500–$5,000 per month per customer. Annual contracts are standard because compliance is an ongoing commitment, not a one-time project.

Opportunity #5: AI Customer Success Platform

The problem: B2B SaaS companies lose customers to churn they did not see coming. Customer success teams track health manually, check in inconsistently, and react to cancellations rather than preventing them. The issue is that good customer success does not scale linearly every new account requires more human attention.

The platform: AI-powered customer health monitoring that watches product usage signals, support interaction patterns, billing events, and engagement metrics to produce a continuously updated health score per account. Automated playbooks trigger based on health thresholds: at-risk accounts receive check-in sequences, healthy accounts receive expansion prompts, and churning accounts receive escalation alerts.

The gap in the market is clear. Gainsight and Totango serve enterprise customers at $50,000 or more per year. Companies under $10M ARR have nothing practical between a spreadsheet and an enterprise platform.

Revenue potential: $200–$2,500 per month per customer, growing with the customer's team and account base. Expansion revenue is built into the model.

Why we like it: the problem is universal among B2B SaaS companies, the ROI is measurable in reduced churn, and the market segment Series A through Series B SaaS companies is well-defined and reachable through targeted content and outbound sales.

Opportunity #6: AI Proposal and Estimation Platform

The problem: agencies, consulting firms, and IT service providers spend two to four hours on every sales proposal. The process involves writing customized descriptions of scope, estimating effort, researching the client, pulling relevant case studies, and formatting everything consistently. Multiply that by fifty proposals per month and you have a significant operational cost.

The platform: an AI assistant that takes a deal brief client name, project type, key requirements and generates a first-draft proposal including executive summary, scope of work, technical approach, timeline, effort estimates, and pricing structure. The sales rep reviews, edits, and sends. Time per proposal drops from three hours to thirty minutes.

The technical scope analysis feature is the defensible differentiator. Generating a project scope and effort estimate that is accurate enough to rely on requires deep understanding of the type of work being proposed. Vertical specialization proposal software specifically for software development agencies, or specifically for marketing agencies makes this accuracy achievable.

Revenue potential: $200–$2,000 per month for agencies and service firms. The ROI is straightforward: if the tool saves ten hours per week of senior staff time, it pays for itself at $200 per month within the first deal.

Opportunity #7: Internal Knowledge Intelligence Platform

The problem: company knowledge lives in Notion, Confluence, Google Drive, Slack, email, and the heads of people who have been there longest. New employees take months to become productive. Experienced employees spend significant time answering the same questions repeatedly. The information exists; finding it reliably does not.

The platform: an AI-powered search and retrieval layer that connects to existing knowledge sources, answers employee questions with source citations, identifies gaps in documentation, and surfaces relevant context at the right moment in the right tool.

Growing demand is structural. As organizations grow, knowledge management becomes more expensive and more critical simultaneously. A 15-person company manages this through conversation. A 150-person company cannot. Every company that crosses that threshold becomes a potential customer.

Internal AI copilots built on company-specific knowledge represent the most promising near-term direction. A copilot that answers HR questions using company policies, technical questions using engineering documentation, and sales questions using product knowledge and past proposals is immediately useful across multiple functions.

Revenue potential: $20–$40 per user per month. Per-user pricing grows automatically with headcount expansion, creating built-in revenue growth without additional sales effort.

Opportunity #8: AI DevOps Assistant

The problem: cloud infrastructure complexity is increasing as companies adopt microservices, containerization, and multi-cloud architectures. Engineering teams without dedicated DevOps or SRE staff which is most companies under 100 engineers deal with monitoring, incident response, deployment risk, and cost management without specialized tools or expertise.

The platform: an AI DevOps assistant that monitors system metrics and logs, provides preliminary incident diagnosis when anomalies are detected, analyzes deployment risk before releases, identifies cost optimization opportunities in cloud spending, and recommends monitoring configuration improvements.

The market: engineering teams at companies with five to thirty engineers and no dedicated DevOps function. These teams deal with production incidents reactively, manage deployments manually, and have limited visibility into cloud costs. The pain is real and well understood.

Why the timing is right: the adoption of AI coding tools means more teams are shipping code faster, which means more deployment risk and more production incidents. The need for DevOps assistance is growing proportionally with AI-assisted development adoption.

Revenue potential: $300–$3,000 per month per customer. Complexity is higher than other opportunities on this list, which means fewer competitors can execute well.

Ranking All Opportunities

Opportunities We Would Avoid

Being clear about what not to build is as important as identifying what to build.

Generic AI wrappers are not businesses. Adding a chat interface to an existing model without vertical specificity, proprietary data, or workflow integration creates a product that a well-funded competitor can replicate as a feature update. The pace of model improvement in 2026 makes this position increasingly precarious.

Commodity chatbots for generic customer support face intense price pressure from platform players Intercom, Zendesk, Salesforce who are adding AI capabilities faster than standalone startups can differentiate. Without a specific vertical or a genuinely novel capability, this is a difficult market to enter.

Clone products built without genuine differentiation are a losing strategy. Building a cheaper version of Notion, Asana, or HubSpot means competing against products with years of development, established networks, enterprise security certifications, and deep integrations into customer workflows. The incumbent's switching cost advantage is significant.

Feature businesses are products that solve a real problem but one too small or too easily replicated to support a company. A single-feature tool that addresses one micro-workflow gets acquired by the platform that owns the surrounding workflow or copied as a minor update. Building a feature in search of a product is building a startup in search of an acqui-hire.

If We Had Only 90 Days

With 90 days, we would focus on the three opportunities where time-to-first-revenue is shortest and the problem is most clearly defined.

First Choice: AI Proposal and Estimation Platform for Software Agencies

Why: the customer is reachable through LinkedIn and agency communities, the problem is felt every day, and the value is immediately quantifiable. A founder with a software development background has natural credibility with this customer segment.

MVP scope: proposal generation from a deal brief, effort estimation by feature, SOW template library for common project types, and integration with one CRM. Nothing else. Eighty percent of the value is in the proposal generation and estimation accuracy.

Estimated timeline: six to eight weeks to MVP. Two weeks of customer interviews and workflow mapping, four to six weeks of development using AI-assisted tools.

Go-to-market: outbound to agency principals and business development leads on LinkedIn. Offer a free pilot to the first ten agencies in exchange for structured feedback. Publish case studies showing time saved per proposal. Target agency communities and forums where principals discuss operations.

Second Choice: AI Customer Success Platform for Early-Stage SaaS

Why: every B2B SaaS company with 50 or more customers needs this and nothing practical exists for under $1,000 per month. The customer is easy to identify Series A SaaS companies with a customer success function and no enterprise tool budget.

MVP scope: usage signal ingestion via API, account health scoring based on configurable signals, at-risk alert notifications to Slack, and a simple dashboard showing health trends by account. No playbook automation in version one that comes after validating the core health scoring value.

Estimated timeline: eight to ten weeks to MVP. Integration work with Stripe and common product analytics tools is the primary technical complexity.

Go-to-market: target customer success communities, Slack groups for SaaS operators, and LinkedIn outreach to Customer Success Managers and VP of Customer Success at Series A companies. Content marketing about churn reduction has strong organic search demand.

Third Choice: Internal Knowledge Intelligence Platform

Why: the problem is universal, the technology has matured to where a small team can build something genuinely useful, and per-user pricing creates revenue that grows automatically with the customer's headcount.

MVP scope: connections to Notion and Google Drive, a query interface that answers questions with source citations, and a Slack integration for in-workflow access. Three integrations, one query interface, source attribution. Everything else is v2.

Estimated timeline: six to eight weeks to MVP. The core retrieval-augmented generation architecture is achievable quickly with existing infrastructure; the challenge is answer accuracy and citation quality.

Go-to-market: product-led growth with a free tier for teams under ten users. Conversion to paid at team growth. Content marketing about knowledge management and employee productivity drives organic acquisition. Word of mouth within companies where the product is used daily is a strong secondary channel.

If We Had $50,000 to Invest

With $50,000 and a clear mandate to reach first revenue, we would focus entirely on the compliance automation platform.

Team structure: one technical co-founder with full-stack and cloud infrastructure experience, one founder with B2B sales experience and ideally some compliance domain knowledge. No other hires until first revenue. External contractors for specialized compliance expertise as needed.

Development roadmap: Weeks 1–2 are validation ten customer interviews with startups that have gone through or are preparing for SOC 2. Weeks 3–14 are MVP development evidence collection for the five most common SOC 2 controls, a policy management module, and an audit progress dashboard. Weeks 15–18 are pilot customer onboarding with three to five companies going through a SOC 2 audit.

Customer acquisition plan: direct outreach to founders and engineering leads at Series A SaaS companies who are being asked by their first enterprise customer to complete a SOC 2. This is a moment of acute pain with a specific timeline. Partnerships with auditors and compliance consultants who work with this segment provide a warm referral channel.

Budget allocation: $20,000 for development infrastructure and tooling over six months, $15,000 for founder salaries and operations, $10,000 for customer acquisition in the first three months, $5,000 for contingency. Revenue from pilot customers even at a discount begins replacing the development budget by month four.

Lessons Founders Should Learn

Validate the problem before building the product. Ten customer interviews take two weeks and cost almost nothing. They will tell you more about what to build than six months of product work based on assumptions.

Ask customers about the problem, not the solution. The question is not whether they would use your product. The question is whether the problem costs them enough that they would pay to solve it. People overstate their interest in solutions and understate the friction of changing their behavior.

Design the MVP around the single most valuable feature. Every feature you add to the MVP is a feature you are building without customer validation. Build the one thing that delivers 80% of the value, get customers using it, and add the next thing based on what they actually need.

Price based on value delivered, not on what feels reasonable. Founders consistently underprice because they anchor to consumer software prices. A product that saves a team five hours per week at a $60-per-hour fully loaded cost is worth $1,200 per month. Pricing at $99 per month is not humility it is leaving revenue on the table and signaling that the product might not be serious.

Distribution is as important as the product. A product without a path to customers is a project. Most technical founders underinvest in distribution because it is less interesting than building. The reality is that a mediocre product with great distribution beats a great product with no distribution almost every time.

What Nurture Technologies Would Recommend

When founders ask us which idea to pursue, our first response is always the same: talk to ten potential customers before you do anything else.

Validation before architecture. Before thinking about technology choices, before designing the database schema, before writing a line of code confirm that the problem is real, painful, and that the people who have it will pay to solve it. Skip this step and you risk building the right product for a problem nobody has.

Architecture designed for the problem. Once the problem is validated, the architecture should serve the product requirements not the founder's technology preferences. The best architecture for an integration monitoring tool is not the same as the best architecture for a compliance automation platform. Choose the approach that delivers the required reliability and performance with the smallest team in the available time.

MVP planning focused on the single most valuable outcome. Define the one thing the product must do well for a customer to pay for it. Everything else is a roadmap item. Build the one thing, get customers paying for it, and build the next thing based on what they actually need next.

Scalability built in from the beginning, not retrofitted later. The most expensive engineering work is rebuilding a system that was never designed to scale. The architecture decisions made in the first eight weeks determine the cost of growth for the next three years. Make them deliberately.

Conclusion

The question of what SaaS would we build today has a consistent underlying answer: we would build something that solves an expensive problem that businesses deal with repeatedly.

Technology changes. The specific capabilities of AI models, the cost of cloud infrastructure, and the tools available for building software all evolve. Customer pain remains. The businesses that need to manage compliance, track technical debt, close deals faster, and retain customers exist regardless of which model is powering their AI tools.

The best SaaS businesses are not built around technology. They are built around expensive, recurring problems that businesses will pay to solve for as long as those problems exist. Find those problems first. The technology is the easy part.

Thinking about building a SaaS product? Nurture Technologies helps founders validate ideas, define MVPs, build scalable SaaS platforms, integrate AI capabilities, and launch production-ready products faster.