Questions, answered honestly

Our clients span early-stage startups building their first product, D2C brands needing a scalable e-commerce platform, and mid-size enterprises upgrading legacy ERP or internal tools. We've worked across healthcare, logistics, retail, fintech, and edtech. What ties them together: they need a technical partner who understands both the business problem and the engineering solution.

Yes, about 20% of our clients are based in the US, UK, UAE, and Southeast Asia. We use asynchronous communication tools and overlap our working hours with client time zones. Our standard contract, payment terms, and NDAs are written to work internationally. Payments are accepted via wire transfer and major international methods.

Absolutely. Staff augmentation is a common engagement model — companies plug in 1–4 of our engineers, designers, or AI specialists into their existing team. Our people communicate directly with your PM or tech lead, follow your tooling and processes, and attend standups like any other team member. Minimum engagement is 3 months.

No. Most clients come with a problem and a rough idea — not a 40-page spec. Our discovery phase exists precisely to turn fuzzy ideas into clear product scopes. Bring us the problem. We'll help you figure out the right solution, the right scope, and the right sequence to build it.

A lean, well-scoped MVP typically falls between ₹4–8 lakhs. Products with custom AI capabilities, complex backend architectures, multi-platform delivery (iOS + Android + web), and third-party integrations are scoped individually and can range from ₹12–40+ lakhs. We don't give ballpark numbers without understanding the scope — contact us for a proper estimate.

Both. Fixed-price works well for clearly scoped projects where requirements are stable. Time-and-materials is better for evolving products where scope grows with learnings. We'll recommend the right model during scoping and explain the trade-offs for your specific situation. Retainer engagements for ongoing work are also available.

MVP builds: 8–14 weeks. Full-featured products: 4–6 months. Enterprise systems with complex integrations and compliance requirements: 6–12 months. These estimates assume timely feedback and clear requirements. We share a milestone plan at kickoff so you can always track progress. Sprints are two weeks long with a demo at the end of each.

For fixed-price projects: 40% at contract signing, 30% at mid-project milestone, and 30% at final delivery. For T&M and retainer engagements: monthly invoicing with net-15 payment terms. We accept NEFT/RTGS, UPI, and international wire transfers. All invoices include GST as applicable.

Every project gets a dedicated Project Manager who is your single point of contact. We use Linear for task tracking (clients get read access), Slack or WhatsApp for daily communication, and Loom for async updates. Fortnightly sprint demos are mandatory — you see working software every two weeks, never just status slides.

We expect requirements to evolve — it's normal. We manage changes through a structured change request process. Minor adjustments (under ~4 hours of effort) are absorbed in the sprint. Significant scope changes are documented, estimated, and approved before work begins. Nothing is added without your explicit sign-off and a clear cost/timeline impact.

Yes. Final deliverables include: full source code in a private GitHub repository transferred to your account, technical architecture documentation, API documentation (Postman collections or OpenAPI spec), deployment runbooks, and a handoff walkthrough call. We document as we build, not as a last-minute scramble.

Our primary stack: React Native and Flutter for mobile; React.js and Next.js for web; Node.js, Python, and Laravel for backend; PostgreSQL, MongoDB, Redis for data; AWS and GCP for cloud infrastructure. For AI: LangChain, OpenAI, Hugging Face, and custom fine-tuning pipelines. We're not dogmatic — we pick the right tool for the problem, not the trendiest one.

Usually yes. We've integrated with legacy Java Spring backends, .NET APIs, SAP systems, and custom ERP databases. Before any engagement on an existing codebase, we do a paid technical audit (typically ₹30,000–50,000) to assess quality, identify risks, and give you an honest picture of what you're working with. This protects both of us from scope surprises.

Security is built in, not bolted on. Standard practices: encrypted data at rest and in transit, JWT-based auth, environment-separated secrets, OWASP Top 10 awareness in code review, and VPC-isolated infrastructure on cloud projects. We sign NDAs before discovery calls and maintain strict data segregation across client projects. For regulated industries (healthcare, fintech), we follow applicable compliance standards.

Every project includes a 30-day post-launch warranty: bugs discovered in this window are fixed free of charge. After that, we offer three retainer tiers: a Maintenance plan (critical fixes + hosting monitoring), a Growth plan (monthly feature velocity + support), and a Dedicated Team plan (full ongoing development capacity). Most clients move to one of these at launch.

You do. Full IP transfer — source code, design files, documentation, and all related assets — is included in every project contract. Upon final payment, everything is yours with no strings attached. We don't retain licenses, backdoors, or ongoing claims. You're free to take the product to any other team without restriction.

We take this seriously. First: raise it immediately — the fortnightly demo exists so there are no surprises at delivery. If a deliverable doesn't meet the agreed specification, we revise it at no cost. If there's a genuine dispute, our contracts include a structured escalation and mediation process. Our 94% client retention rate reflects that issues get resolved fairly.

Practical AI that solves real business problems: document processing and extraction, conversational AI for customer support, recommendation engines, demand forecasting, multilingual voice bots, RAG-based knowledge bases, and AI-assisted workflow automation. We don't build research demos — everything goes to production with proper monitoring, fallback handling, and human oversight where needed.

Both, depending on the use case. For most applications, a well-engineered RAG system on top of GPT-4 or Claude outperforms a custom-trained model at a fraction of the cost and time. We build custom fine-tuned models when: data privacy prevents sending data to third-party APIs, domain specificity is very high, or latency/cost at scale makes proprietary APIs prohibitive.

Data never leaves your infrastructure without your explicit consent. For sensitive industries (healthcare, legal, finance), we default to self-hosted open-source models (LLaMA, Mistral, etc.) so no data touches external APIs. For projects using third-party APIs, we implement data masking, anonymisation, and prompt injection protections. We help you build an AI governance policy as part of delivery.