May 6, 2026
Updated May 11, 2026
About Author
Hardik Sojitra
Product
Hardik is part of the growth team at Rocket.new, where he spends most of his time figuring out why people stay or leave. Curious by default, active blood donor, and a big cricket fan.
You already know what you're trying to figure out. Type it. Rocket handles everything after that.
Solve on Rocket.new transforms build vs buy decisions into structured, evidence-backed reports using live data from 150+ sources. Parallel AI agents analyze market, pricing, competition, and feasibility to deliver clear recommendations. Teams align faster and move from research to execution without losing context.
A Business Question Worth Getting Right
Build custom: Full control over outcomes, but slower and resource-intensive.
Buy off-the-shelf: Speed and convenience, but potentially less flexibility.
Both options have trade-offs, yet all too often, the underlying research is thin.
Building custom AI solutions offers complete control and the potential for competitive differentiation, but it requires significant upfront investment and advanced data capabilities.
According to a 2026 Product School report, global AI spending exceeds $200 billion annually, but much of it doesn’t drive meaningful impact. The issue isn’t the budget; it’s the quality of the research before committing funds.
This is the gap Solve on Rocket.new aims to close.
Most product failures stem from misdirection rather than execution.
Research from Standish Group, cited by Today in 2025, shows ~64% of delivered software features are barely used.
Teams often build for a request, not a validated need, and errors compound:
Wrong build: Years of maintenance overhead.
Wrong buy: Matching a misfit tool and wasting engineering resources on workarounds.
"The priciest mistake is committing to any path before thorough research."
Before committing to any path, organizations can acquire generative AI solutions through three main strategies: buying an off-the-shelf model, boosting an existing model with proprietary data, or building a custom solution from scratch.
Purchasing an off-the-shelf generative AI model allows organizations to adopt technology quickly without investing in model development. However, this speed comes at a cost, limited differentiation from competitors and growing dependence on vendors for updates, pricing, and reliability.
Boosting generative AI solutions involves enhancing a vendor's model with proprietary data, which can improve accuracy but requires strong data governance and may increase operational costs. While this middle-ground approach can yield better results than a pure off-the-shelf buy, it demands strong data governance practices and often drives up operational costs over time.
Building a custom generative AI solution from scratch offers complete control over outcomes and the strongest potential for competitive differentiation. The trade-off is significant; this path requires substantial investment, advanced internal capabilities, and long-term resource commitment.
Choosing the wrong acquisition strategy doesn't just waste budget; it compounds over time through maintenance overhead, vendor lock-in, or costly rebuilds. This is exactly the kind of high-stakes, multi-variable decision that Solve on Rocket.new is designed to support, bringing structured, evidence-backed, live-data research to a question that too many teams answer with gut instinct alone.
Both groups care about build-vs-buy, but rarely share the same data or perspective.
Error risk and technical debt
Maintenance/customization
Feasibility and scalability
Code quality
Development overhead
Vendor lock-in
Time to market
Competitive positioning
Pricing/vendor risk
Alignment to business goals
Customer/market data
| What Engineering Weighs | What Strategy Weighs |
|---|---|
| Error risk and technical debt | Time to market |
| Maintenance and customization costs | Competitive positioning |
| Technical feasibility and scalability | Pricing and vendor risk |
| Code quality and long-term build path | Business goals alignment |
| Development overhead | Customer and market data |
Solve generates a single report that addresses both engineering and strategy priorities from the same live data, which makes sense, given that both teams are ultimately solving the same business problem."
Traditionally, research means:
Gathering data from tabs
Summarizing into slide decks
Debating sources
Handoffs multiply confusion
Accuracy degrades with every step
Vibe solutioning reverses this:
Rigorous thinking before building shapes what gets built.
Most AI tools return a summary, Solve returns a structured, actionable plan readable and actionable by all.
Alon Goren, CEO of AnswerRocket, notes:
“The traditional build vs. buy equation for enterprise software just got rewritten. Now you buy evergreen pieces—authentication, database, infrastructure, and build where you’re unique.” - LinkedIn
Solve delivers this clarity at scale, grounded in real context and live market data.
So let's find out how does Solve on Rocket.new generates a build-vs-buy analysis that both engineering leaders and strategy teams can trust?
When you ask a business question like “Should we build a custom analytics layer or buy a reporting tool?”
Solve does not just respond with a summary or one AI output. Instead, the platform uses parallel AI agents to pull real-time data from over 150 sources, including competitor websites and job listings.
A structured report (8–12 sections) such as:
Executive summary
Market sizing
Competitive teardowns
Pricing comparisons
Build vs. Buy risk matrix
Clear, weighted recommendations
Each finding is signal-tagged so teams know how much weight to give it.
Most AI tools answer once and move on. Solve splits your question into parallel research streams, each handled by a dedicated agent, showing:
What sources were researched
The evidence found
Confidence in each finding
Final results are merged:
One signed-off, shareable, structured document
Much higher quality than single-pass AI
Context loss is common:
Research in one tool
Strategy written elsewhere
Engineering starts with handoffs
Research results are saved as a living data layer to prevent the loss of strategic intent during the transition from planning to development.
By keeping research and the build together in Rocket.new (via shared context), nothing resets between phases. No re-explaining. Code generation becomes faster and more accurate.
Most build-vs-buy reports rely on static research. By the time a decision is made, the market may have changed. Solve always pulls live data, so each deliverable includes:
Latest competitor pricing & product positioning
Recent moves/updates by competitors
Gaps in the market and customer demand
How others solved the same build-vs-buy challenge. This live intelligence fundamentally raises the quality of decision-making.
Research shows 60–70% of “vibe coding” users aren’t sure what to build at the start.
Solve closes this gap for:
Startup founders: Competitive teardown and market analysis before building
Enterprise teams: Pre-sprint stakeholder alignment with shared, actionable research
Engineering leaders: Analysis covering both feasibility and fit
Strategy teams: Full build-vs-buy assessments carried straight into the build, no rework or tool switching
👉Check out this LinkedIn post by Visal Virani
Every use case yields a structured, shareable report, present, PDF, or hand-off in Rocket.
Standalone AI like ChatGPT or Perplexity = general summaries, not decisions or actions.
Teams still have to:
Interpret research
Connect it to decisions
Carry it through to building and delivery
Rocket.new is a vibe solutioning platform with three integrated products in one place:
Solve – Generates build-vs-buy analysis for any business question; provides structured, multi-source, recommendation-rich reports in hours.
Build – Turns research into ready code (Next.js for web, Flutter for mobile, with real design systems, theming, and navigation). Uses the same research context to cut waste and speed up builds.
Intelligence – Rocket.new's platform integrates ongoing market monitoring to keep build-vs-buy decisions valid over time.
“The AI tool market made a fundamental mistake; it built tools. Tools require you to be the system. Rocket is the system.”
Perplexity finds info.
ChatGPT responds to queries.
Rocket produces decisions & builds from them.
The core problem is not cost, it’s research.
Teams often have fragmented data, misaligned stakeholders, or lose context between research and build.
Solve does parallel, structured research, produces signal-scored, evidence-backed findings, and carries that shared context directly into the build process, so research turns straight into execution.
For companies where this decision means the difference between shipping value or building waste, that’s exactly what Rocket delivers.
Make smarter build vs buy decisions with data-backed clarity, try Solve on Rocket.new today.
Table of contents
What type of business question does Solve handle best?
Does Solve use live data or just AI training data?
Can engineering and strategy teams work from the same Solve report?
How is Solve different from ChatGPT or Perplexity?