AI Solutions
Use AI where it creates operational value: internal copilots, document workflows, knowledge search, content support, data extraction, and safe model integrations.
Typical deliverables
- AI use-case assessment and risk review
- Prototype or production AI workflow
- Prompt, retrieval, and integration design
- Security, privacy, and fallback considerations
- Deployment and handover documentation
Business outcomes
- Reduced manual effort
- Clearer AI feasibility before overspending
- Safer model integration in real business workflows
What it costs
Indicative ranges for the Indian market, not a quote. They exist so you can sanity-check a budget before writing a brief — scoping produces the real number, and it may land outside these bands.
Proof of concept
₹3,00,000 – ₹8,00,000
Typically 3–6 weeks
One workflow, prototyped against your own data, enough to decide whether to build, adjust or stop.
Production AI workflow
₹10,00,000 – ₹30,00,000
Typically 8–16 weeks
Integrated into the systems the team already uses, with retrieval, guardrails, fallbacks and monitoring.
Budget roughly 15–20% of the build cost per year for maintenance — dependency and OS updates, security patches, and small improvements.
Where the budget goes
Every engagement runs through the same five phases. Discovery is the smallest line and the one that most reliably pays for itself — industry data puts it at 5–10% of budget while cutting development overspend by 40–60%.
Clarify the business goal, users, constraints and risks, then agree what the first release must contain. The cheapest phase to get right and the most expensive to skip.
Interface design, data model, integration map and the technical decisions that are hard to reverse later.
Engineering in focused cycles with working previews, so scope and direction stay visible instead of arriving as a surprise at the end.
Functional testing, performance, accessibility and security checks. Runs alongside the build rather than only at the end.
Deployment, documentation, store submission where relevant, and the handover that lets the product be operated and improved after release.
Common questions
- How do I know if AI is the right solution for my business problem?
- Start from the workflow, not the model. AI fits best where the work is high-volume, language- or document-heavy, and currently done by hand — review queues, classification, extraction, knowledge search, drafting. If the task needs exact, auditable answers every time, conventional software is usually the better tool. QuantamQ maps the use case before building, and will say when AI is the wrong answer.
- What does an AI proof of concept actually include?
- A working prototype of the real workflow rather than a demo: the prompt or retrieval design, the integration path into existing systems, a view of accuracy on your own data, and a note on the failure modes and fallbacks. The goal is a decision — build, adjust, or stop — before committing to a full implementation.
- Is our business data used to train AI models?
- That depends on the provider and configuration chosen, and it is a decision made deliberately at design time. Model integrations are set up with data handling, retention and privacy considerations documented as part of the build, alongside security and fallback behaviour.
- Can AI be added to software we already run?
- Yes — most AI work is integration rather than replacement. Common patterns are an assistant inside an existing product, automated document or data extraction feeding current systems, and knowledge search across tools a team already uses.
- How much does an AI project cost in India?
- As a benchmark, a proof of concept typically runs ₹3,00,000–₹8,00,000 over 3–6 weeks, and a production AI workflow ₹10,00,000–₹30,00,000 over 8–16 weeks. The variables that move it most are how many systems it has to integrate with, how clean the underlying data is, and how much accuracy the workflow needs before it can be trusted. Scoping produces the real figure.