As an advisor to Vectiro, I meet Sudhakar, CEO of Vectiro regularly, and every time I am amazed with the progress the team has made on the product. I was curious to understand how a team of four founders built an enterprise-grade data protection and disaster recovery platform in about 12 months. New workloads, new capabilities, new features, all delivered by the same four people. Sudhakar revealed the fifth teammate: Anthropic Claude Code.
What follows is Vectiro’s journey in Sudhakar’s own words: how the team worked with Claude across architecture, coding, debugging, and what other small teams can learn from it.
At a glance
Vectiro, a four-person startup in Pune, India, built an enterprise-grade data protection and disaster recovery platform in roughly 12 months with Claude as a daily engineering partner. The platform now covers cloud virtual machines and five database engines, with a storage layer that includes versioning, deduplication, compression and encryption, and a control plane for discovery and job management.
| Company | Vectiro Pvt Limited — cloud data protection anddisaster recovery |
| Team | 4 co-founders, all hands-on; no dedicated QA,docs, design or marketing staff |
| Built | Storage plane, control plane, backup/recovery forVMs and five database engines, and lake-readyqueryable copies of backed-up databases foranalytics and AI |
| Time to platform | ~12 months, versus an estimated 24–36 months for a team this size without AI assistance |
| Engineering effort avoided | Equivalent of 6–8 additional engineers over the build period |
| Where Claude helped | Architecture, coding, debugging, proof-of-concepts, competitive research, sales andmarketing material |
The challenge
Enterprise backup is a category where incumbents field engineering teams of hundreds. Vectiro’s founders had spent years building such products at established companies and knew the scope: a storage layer that must be durable and space-efficient, a control plane that must be reliable, and a separate integration for every workload a customer runs.
With four people, the team could not afford specialists. Each founder had to cover architecture, implementation, testing, documentation and, increasingly, customer-facing work at the same time. Every week spent on a wrong design, a dead-end proof-of-concept, or a slow debugging session was a week the product did not have.
The team also had a strict constraint: customer data must never leave the customer’s own cloud boundary. That ruled out many shortcuts and meant much of the platform had to be built rather than bought.
How Claude fit into the team
Claude became the fifth member of the team, working across the whole lifecycle rather than in one corner of it.
Architecture and design reviews. Before committing to a major component, the founders worked through the design with Claude: comparing candidate technologies, weighing trade-offs, and freezing decisions in writing. Several core design choices were settled in single sessions that would otherwise have taken days of reading and prototyping.
Proof-of-concepts before production. For each new workload, the team first rehearsed the approach end to end on a local lab machine, with Claude generating the scaffolding, scripts and test data. Ideas that did not hold up were discarded cheaply, before any cloud spend, and the ones that did carried straight into the product.
Pair programming with Claude Code. Using Claude Code on the codebase directly, founders built and verified whole subsystems in extended sessions: scaffolding services, writing integration test suites, and iterating until the suites were green. Claude worked in the team’s chosen languages and followed the codebase’s existing conventions.
Debugging the hard problems. Distributed storage and change-capture systems fail in subtle ways. Claude helped diagnose issues ranging from protocol mismatches and configuration defaults that silently truncated traffic to edge cases in transaction ordering, often cutting investigations from days to hours.
Everything that is not code. The same four people also had to sell. Claude helped produce the customer-facing sales deck, a grant application, competitive analyses, the company website, and the scripted product demo video with its voiceover.
Beyond backup: the data lake and AI angle
Vectiro’s backups are not just a safety copy; they are a queryable, analytics-ready dataset. Every backed-up database can be exported on demand into an open lakehouse format and queried directly from Vectiro storage, with zero load on the production system. A customer can ask “what did this table look like at 2 pm on Tuesday”, compare two points in time, or feed a clean historical copy into a pipeline without touching production.
This turns data protection into a data platform. The same copies support ransomware detection (spotting anomalous changes between snapshots), instant audits and legal discovery, and, increasingly, AI: model training, retrieval and agent workloads need governed, point-in-time, production-safe data, which is exactly what a backup already is. Because the copies never leave the customer’s own cloud boundary, AI teams get the data without the compliance argument.
Claude was central to getting here with four people. The lakehouse export path started as a walking skeleton built in a single extended pair-programming session: capture changes from a source database, apply them to an open table format, and query the result. Claude helped the team evaluate and choose between open-source capture and catalog tools, resolve the incompatibilities between them, and settle the design of the storage layer so that the same object store serves backup, restore and analytics. Building a lakehouse pipeline is normally a data-engineering specialism of its own; here it was done by the same founders, in weeks, alongside the core platform.
Results
Four people shipped a platform that would normally take a team two to three times the size two to three times as long. The bracketed figures below are the team’s own estimates and should be confirmed before publication.
| Measure | Without Claude(estimate) | With Claude | Saving |
| Time to a complete platform | 24–36 months | ~12 months | 50–65% |
| Engineers needed forthe same scope | 10–12 | 4 | 6 –8 hires avoided |
| Typical new workload integration | 8–12 weeks | 3–4 weeks | ~65% |
| Typical hard debugging investigation | 2–3 days | 2–4 hours | ~85% |
| Proof-of-concept to go/no-go decision | 1–2 weeks | 1–2 days | ~80% |
| Sales, grant and marketing material | Contractor or agency | In-house, days | 6 x 20 x 2 = lakhs avoided |
Beyond the numbers, three things changed how the company operates:
• Decisions got cheaper. Because a proof-of-concept costs a day rather than a fortnight, the team tries more approaches and commits to fewer wrong ones.
• Quality did not trade off against speed. Subsystems shipped with integration test suites and conformance runs against real client tooling from day one, because the tests were cheap to write.
• Founders stayed founders. Engineering time did not have to be pulled away from customers; the same people ran discovery, built the demo, and closed the design at the same time.
What made it work
The gains came from how the team used Claude, not from handing it the keyboard.
1. Design first, in writing. Major decisions were argued out with Claude and frozen as a numbered list before any code was written. The written record kept four people aligned without meetings.
2. Rehearse locally, then go to the cloud. Every workload was proven on a lab machine before touching cloud resources, which kept infrastructure spend low during development.
3. Small steps, verified by a human. For hands-on work, the team preferred short, inline changes they could run and report back on, rather than large, generated bundles. Each step was checked before the next.
4. Tests as the contract. Claude was asked for the integration suite alongside the feature, and the suite, not the explanation, decided whether a piece was done.
5. Founders own the pointed changes. In the most sensitive parts of the platform, Claude proposed and the founders applied, keeping ownership and understanding with the people who would support it in production.
Quote from the Vectiro team:
“It felt like to have a fifth engineer who never got tired of reading logs, and what they would tell another small team.”
What’s next
Vectiro is adding its next database workload by the end of October 2026 and expanding into new recovery scenarios its customers are asking for, including point-in-time protection for object storage and tiered file data. The team expects to keep shipping at the same pace with the same four people.
Vectiro builds cloud data protection and disaster recovery for businesses that need ransomware recovery, compliance and fast, granular restores without moving their data outside their own cloud. Learn more at www.vectiro.com
Sudhakar Paulzagade, CEO, Vectiro can be reached at sudhakar.paulzagade@vectiro.com
Vectiro’s story shows what becomes possible when a small, experienced team learns to work with AI deliberately rather than casually. At GSTAMP, we are proud to be an Anthropic Claude partner and collaborate with Vectiro on training and best practices for building with Claude Code.