Structured 4-week engagements for BFSI, healthtech, and enterprise SaaS teams navigating AI deployment under RBI, DPDP, and HIPAA requirements. Built for teams where sending code or data to frontier AI APIs is not an option.
Every engineering team is being told to adopt AI. Regulated teams face a harder question: how do you use AI when your data cannot leave your infrastructure? Sending source code to Claude, ChatGPT, or Copilot is often blocked by RBI mandates, HIPAA-adjacent client requirements, or internal data governance policies. Meanwhile, competitors in less regulated spaces are shipping 30–50% faster with hosted AI tools.
Most generic AI consultants build strategy decks. Most AI product vendors sell you their platform. Neither maps your specific compliance framework to the architecture decisions you need to make. That gap costs regulated teams 12–18 months of stalled AI adoption while they figure it out internally, or they end up with expensive Big 4 assessments that read like generic AI trend reports.
Every engagement produces a written 30–50 page architecture document plus an executive presentation. Concrete, decision-ready, vendor-agnostic.
Maps your specific regulatory framework (RBI Master Directions, DPDP Act, HIPAA if applicable, sector-specific rules) to concrete architecture constraints.
Evaluates hosted-with-BAA, hosted-private-endpoint, self-hosted VPC, hybrid, and air-gapped approaches — with cost, latency, and compliance tradeoffs for each.
Compares open-source models (Qwen 2.5 Coder, DeepSeek Coder, Llama 3.3, Mistral) and hosted options (AWS Bedrock, Azure OpenAI) against your specific use cases with real benchmarks.
3-year total cost of ownership modeling: hardware, cloud, compliance overhead, managed service, and internal team time.
Phased 6–12 month deployment plan with specific technology choices, vendor recommendations, and risk mitigation for each phase.
Facing RBI's AI governance requirements, DPDP compliance for customer data, or internal audit pressure on AI use.
Handling HIPAA-adjacent data flows, GDPR patient data, or planning international expansion where compliance requirements will tighten.
Whose enterprise contracts require BAA, SOC 2, or specific data residency guarantees that make public AI APIs off-limits.
Delivering to US healthcare, European finance, or Indian BFSI clients whose compliance requirements pass through to your delivery architecture.
Structured interviews with your engineering, security, and compliance leads. Review of current AI use, tools evaluated, and existing compliance framework.
Draft compliance mapping, evaluate 3–5 deployment approaches against your specific constraints, model selection analysis with benchmarks on representative data.
Deliver draft architecture document. Review sessions with your team. Iterate on recommendations based on team feedback and internal constraints.
Final 30–50 page architecture document, executive presentation to your leadership, and 30-day post-delivery availability for follow-up questions.
For custom implementation engagements (deployment, managed service, ongoing operations), pricing is scoped separately after the roadmap is complete.
ideaToMVP is a specialist practice focused on AI compliance and deployment architecture for regulated enterprises. We do 4–6 client engagements per quarter, not 40. That means you get the founder personally on your engagement — not a rotating team of junior consultants attempting to learn your industry on the job.
Current active engagement: delivering an enterprise AI engineering cohort to TCS engineers through LearnQuest, focused on production AI agent development for enterprise contexts. Founding technical background in Python, AWS infrastructure, and ML systems engineering.
How we think about this problem, before you book a call.
The architecture behind running capable models on infrastructure you control — the same layering we evaluate in a compliance roadmap.
Read the article →Hands-on with the agent tooling enterprise teams are assessing — what it does well, and where it needs guardrails.
Read the article →No. The engagement is architecture and compliance analysis, not code review. Where benchmarking on representative data is useful, we work with synthetic or anonymised samples inside your environment — never in ours.
That is usually the right time. Most of the value is in translating an unsettled regulatory position into concrete architecture constraints before you commit to a vendor or a build. If your requirements are genuinely undefined, the Lite tier is designed for exactly that discovery stage.
Those reports tend to stop at governance frameworks and market trends. This engagement ends with a specific deployment architecture, a named model shortlist with benchmarks, 3-year TCO numbers, and a phased implementation plan your engineering team can act on.
The roadmap engagement is advisory and vendor-agnostic by design — that independence is what makes the recommendation trustworthy. If you want us to implement afterwards, that is scoped as a separate engagement with its own pricing.
Most teams take Standard, which covers the full architecture design and vendor comparison in four weeks. Choose Lite if you are still building the internal case, and Deep if you need a working proof-of-concept to convince stakeholders.
Roughly 6–10 hours total across the engagement, concentrated in Week 1 discovery interviews and the Week 3 review sessions. We work asynchronously between those touchpoints.
Not every engagement is a fit. A 30-minute call helps us both understand whether your specific situation — your compliance requirements, timeline, and internal team — matches what this engagement is designed to deliver. No sales pitch, no follow-up spam.
Book a qualification call →