Honest answers to the questions that come up most often about working with S.T.R.In.G. If yours isn't here, just ask us.
We're a technical studio that builds AI, automation, data infrastructure and software products for companies across multiple industries. We work across five practices — AI & ML, Workflow Automation, Data & Analytics, Web/App/Product Engineering and Operations Research & Design.
We take on client projects (fixed-scope or retainer), and we also own and license a portfolio of products (Digital Muneem, Learning Engine, Remit, Forecast Kit, Stock Sense and Automated Document Reader).
We're 28 FTE across two offices (Delhi and London), plus a vetted associate network of domain experts we draw on for specialised needs. We deliberately stay small enough that senior people do the work — not just scope it and hand it to juniors.
We have active production systems across healthcare, logistics & supply chain, financial services, education, retail, manufacturing and marketing. See our industries page for use cases specific to each vertical.
Every engagement starts with a 30-minute discovery call — no preparation needed. We'll ask about the problem, your data situation, your team and what success looks like. If there's a fit, we'll propose a paid scoping engagement (1–2 weeks) that produces a concrete technical brief, architecture proposal and fixed-price quote. See our process page for more detail.
Both. Some of our most interesting work has been with early-stage companies building their first AI or data layer. The main requirement is that you have a real problem worth solving — we don't take on speculative AI experiments with no path to production value. The minimum viable engagement is roughly £15,000 / ₹16L.
Weekly sprint cadence. Working software every Friday. A shared Slack channel for async updates, a 30-minute weekly review call, and a Notion board that shows exactly where every task stands. We don't do big reveals at the end of long projects. You see real progress every week.
Yes. All code and IP produced for your engagement is yours from the moment it's created. We don't retain rights to client-specific work. The only exception is if we incorporate one of our proprietary product libraries (e.g. Forecast Kit), in which case you get a licence rather than ownership of that specific component — everything built on top is fully yours.
Yes, always. We sign NDAs before any substantive discussion about your business. Our standard NDA is mutual. If you have a preferred form, we'll review it — most are fine as-is.
We offer three models: Fixed-scope projects (defined deliverables and price, starting from £15,000 / ₹16L); Monthly retainers (dedicated team capacity, starting from £8,000 / ₹9L/month); and Product licensing for our owned products. See our process page for full detail on each model.
No — the discovery call is free, but the scoping engagement is paid (typically £2,000–4,000 / ₹2L–4L depending on complexity). This ensures both sides are serious about the engagement and that we produce a genuinely useful technical document, not just a sales artefact. If you proceed to a full project, the scoping cost is credited against the project fee.
Occasionally, for early-stage companies where there's a strong strategic fit and the team and market are compelling. This is evaluated case by case — it's not our default model. If you're interested, mention it on the discovery call and we'll have a frank conversation about whether it makes sense.
Yes. We work across AWS, GCP and Azure — and on-premise when client security requirements demand it (we've deployed on client data centre infrastructure for regulated-industry clients). We'll recommend the right cloud provider and deployment model for your situation, not the one we happen to have a partnership with.
Yes. We've built systems under HIPAA, FCA, GDPR, FINTRAC and India's DPDP requirements. Security and compliance are scoped as first-class requirements, not afterthoughts. See our Trust Centre for our own security posture and data handling practices.
Both, depending on the use case. GPT-4o, Claude and Gemini for tasks where frontier capability matters. Llama 3, Mistral and other open-weight models for deployments where data privacy, cost or on-premise requirements make commercial APIs a bad fit. We recommend based on your requirements, not a preferred vendor.