Your data stays yours.
Design ingestion, access boundaries, and retrieval around your requirements. Keep sensitive information in an environment you control.
Your intelligence.
Your infrastructure.
Your AI.
AI that belongs inside your business. I help teams build and fine-tune models on their own data, deploy in their own environment, and take control of the whole system.
BLUEPRINT / 001● SOVEREIGN BY DESIGN
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Your training dataCompany-owned datasets · domain expertise
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Your model weightsCustom fine-tuning · evaluated on your tasks
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Your environmentPrivate cloud · on-premises · your controls
BOUNDARY: YOUR ORGANIZATION↳ BUILT TO HAND OVER
An AI system you can understand, operate, and keep.
01 / DATA UNDER YOUR CONTROL02 / MODELS ADAPTED TO YOUR DOMAIN03 / INFRASTRUCTURE YOU OPERATE
Flagship offering / Sovereign AI
Own the system.
Keep the know-how.
Your competitive knowledge should become a capability your team can keep building on. Ownership starts with architecture and ends with a usable handover.
Models that know your domain.
Prepare datasets, adapt suitable open-weight models, and evaluate them against real tasks. Use fine-tuning where it helps and retrieval where knowledge needs to stay current.
A complete handover.
Plan for source code, model artifacts, evaluation suites, deployment configuration, and documentation, so your team can operate and evolve the system.
Ownership and deliverables are defined per engagement. Base models and third-party software retain their original licenses; model selection must fit your intended use.
Other services
AI engineering services.
Alongside Sovereign AI, I take on focused projects across these five independent service areas.
Custom models & machine learning
Dataset preparation, model fine-tuning, computer vision, and NLP. From classical ML to transformer-based systems, selected around the problem.
OCR & document intelligence
Turn difficult documents into usable information. Handle layout, tables, visual questions, and entities with evaluation at each stage.
RAG, memory & ingestion
Connect your organization's knowledge to its AI systems. Build ingestion pipelines, retrieval, and memory for relevant, grounded context.
Multi-agent systems & chatbots
Build purpose-specific assistants and coordinated agents with tool access, shared context, state management, and human handoffs.
Evaluation, optimization & deployment
Measure quality on your tasks, investigate failure modes, optimize inference, and deploy with observability. Make performance and operating costs visible.
Explore the economics
Make the numbers work.
Three ways to assess an AI investment. Start with your own measurements or load an illustrative example.
Methodology & sources
Own AI: upfront = data + engineering + evaluation + training compute. Training compute = GPUs × hourly GPU rate × hours per run × runs. Monthly hosting = replicas × billed hours × hourly replica rate, plus storage, operations, and retraining. Alternative cost = fixed monthly cost + successful tasks × per-task price. Compare the same quality, workload, and service requirements.
Automation: adopted tasks = volume × eligible share × adoption. Successful attempts retain review and rework time. Failed attempts retain original handling time plus exception overhead. Unadopted tasks retain original handling time. Time savings: volume × adoption × (minutes before − minutes after) ÷ 60.
Gross capacity value = released hours × loaded hourly cost. Positive capacity value becomes estimated cash savings only at the spending-realization percentage you enter. Added labour is counted in full. ROI = (benefits over period − upfront − recurring costs over period) ÷ (upfront + recurring costs over period). Simple payback = upfront ÷ positive monthly net benefit, assuming steady cash flows. No positive net benefit means no payback.
Compare conservative and optimistic assumptions by changing completion rates, review time, compute costs, and utilization. These are planning estimates, not quotes or forecasts.
Sources: AWS: generative AI costs · NVIDIA: inference benchmarking · Microsoft: measuring business value.
Private AI & governance
Control your AI.
Design for your obligations.
Private deployment gives you choices over data access, location, and retention. Compliance also depends on your use case, operating practices, contracts, and applicable law.
Engagements can scope access controls, encryption, data minimization, retention and deletion, audit logging, human oversight, and evaluation evidence alongside your legal and security teams.
Privacy & personal data
EU GDPR and UK GDPR: assess lawful processing, individual rights, data flows, and AI-specific privacy risks. California CCPA/CPRA: consider applicable consumer privacy rights. India’s DPDP Act and Rules: map applicable duties and commencement dates to the deployment.
Healthcare & payments
HIPAA: for covered US healthcare activities, scope safeguards for protected health information, risk analysis, and business associate agreements where required. PCI DSS: assess payment-card data scope and required security controls. Neither applies automatically to every AI system.
AI-specific governance
EU AI Act: determine the system’s risk classification, your provider or deployer role, and applicable obligations. ISO/IEC 42001 provides an AI management-system standard; the NIST AI RMF is voluntary risk-management guidance.
Security assurance
ISO/IEC 27001 addresses information-security management. SOC 2 is an independent examination of relevant organizational controls, not a product certification. Engineering documentation and operational evidence can support your assurance work.
Selected frameworks, not an exhaustive global list. Sector and regional rules may add requirements. These are scoping considerations, not claims that I or this website hold certifications or that a deployment is automatically compliant. Legal applicability and formal assurance require the appropriate specialists. Sources reviewed October 31, 2026.
BEHIND THE WORKBENCH / VK
vk.
AI ENGINEERING × CURIOSITYHi, I'm Vaibhav.
I like connecting the model
to the real-world problem.
I've built multi-agent customer-support systems that route requests, execute tools, share context, and hand conversations over to people. My work includes session and long-term memory, RAG and Graph-RAG retrieval, and self-hosted language models connected to business databases.
I've also developed document-understanding pipelines, fine-tuned ML models and language/vision models for domain-specific tasks, and built evaluation pipelines for retrieval quality, response accuracy, latency, and agent failures. Across these systems, I've worked on inference optimization and deployments on AWS and GCP using Docker and Kubernetes.
I studied Computer Science at LNMIIT, where I received the Chairman's Gold Medal. Beyond engineering, my college years included student leadership, astronomy, and social service.
This is the kind of work I want to bring to your team: thoughtful system design, hands-on implementation, and knowledge that stays with you.
Working together
A clear path to ownership.
Identify manual workflows.
Identify repetitive manual work where automation can increase productivity and keep costs down. Establish time, cost, and quality baselines, then prioritize the opportunities with measurable value.
Test the approach.
Build a focused prototype and evaluate it on representative tasks.
Make it operational.
Integrate, optimize, and deploy into the agreed environment.
Make it yours.
Deliver the agreed artifacts, documentation, and operational walkthrough.
Your next capability starts with a conversation
Let's build AI you can own.
Tell me what your team does, where the friction is,
and what needs to stay under your control.