Question 1

What is EdubildAI and what does the company do?

Accepted Answer

EdubildAI (also known as Edubild) is an enterprise Generative AI development company headquartered in Jaipur, Rajasthan, India. We design, build, and deploy production-grade AI systems for government departments, public sector undertakings (PSUs), and large enterprises. Our core services include Retrieval-Augmented Generation (RAG) systems, AI agents, LLM integration, model fine-tuning, OCR and intelligent document processing, ERP development, Frappe/ERPNext development, and custom software engineering.

Question 2

Where is EdubildAI based?

Accepted Answer

EduBild Technologies is headquartered at Bhamashah Techno Hub, Sansthan Path, Jhalana Gram, Malviya Nagar, Jaipur, Rajasthan 302017, India. Bhamashah Techno Hub is India's largest startup incubation centre, supported by the Government of Rajasthan.

Question 3

Is EdubildAI certified?

Accepted Answer

Yes. EdubildAI holds two major certifications. ISO 9001:2015 Quality Management System Certification confirms that our delivery processes, documentation, and client communication meet internationally recognised quality standards — audited annually by Bureau Veritas. CMMi Level 3 Capability Maturity Model Integration confirms that our software engineering processes are defined, documented, standardised, and proactively improved across all projects. CMMi Level 3 places EdubildAI among the top tier of Indian technology companies and is a significant differentiator in government procurement evaluations.

Question 4

What types of clients does EdubildAI work with?

Accepted Answer

EdubildAI primarily serves central and state government departments, Public Sector Undertakings (PSUs), and large Indian enterprises. We have particular depth in regulated and high-stakes environments — including government document processing, legal tech, healthcare informatics, and financial compliance — where accuracy, auditability, and data sensitivity are non-negotiable. We also work with mid-size enterprises and NGOs undergoing digital transformation.

Question 5

What industries does EdubildAI serve?

Accepted Answer

EdubildAI serves clients across government and public administration, legal services, healthcare and hospitals, financial services (banking, insurance, fintech), manufacturing, education and universities, e-commerce and retail, and NGOs. Our strongest verticals are government, legal, and manufacturing, where we have delivered multiple large-scale production systems.

Question 6

What is a RAG system and why do enterprises need one?

Accepted Answer

RAG (Retrieval-Augmented Generation) is an AI architecture that connects a Large Language Model to your organisation's private knowledge — stored in PDFs, databases, SharePoint, ERPs, or any document store. When a user asks a question, the system retrieves the most relevant documents, passes them to the LLM, and generates an accurate, cited answer grounded in your actual data. Enterprises need RAG because general-purpose LLMs are trained on public internet data and have no access to your internal knowledge. RAG systems eliminate hallucination (the AI making up answers) and give employees natural-language access to verified, authoritative organisational knowledge.

Question 7

What is the difference between LLM integration and LLM fine-tuning?

Accepted Answer

LLM integration connects an existing general-purpose model (like GPT-4o or Claude) to your application via API. The model's knowledge and behaviour remain unchanged; you design the prompts and context to guide its responses. This is typically faster, cheaper, and sufficient for most enterprise use cases. LLM fine-tuning trains the base model further on your specific domain data — adjusting the model's underlying weights. Fine-tuning is beneficial when you need the model to develop deep domain expertise (e.g., understanding Indian legal terminology or medical coding conventions) or to consistently follow a specific output format. EdubildAI helps you determine which approach is right for your use case.

Question 8

What types of AI agents does EdubildAI build?

Accepted Answer

EdubildAI builds autonomous AI agents capable of multi-step reasoning and tool use within enterprise workflows. These include: customer support agents that handle queries and escalate to humans with context; document processing agents that read, classify, and route documents through approval workflows; data analysis agents that query databases, generate reports, and email results; procurement agents that search supplier catalogues, compare prices, and draft purchase orders; and custom multi-agent systems where multiple specialised agents collaborate on complex tasks. We use frameworks such as LangGraph, AutoGen, CrewAI, and custom agent runtimes depending on the use case.

Question 9

Can EdubildAI handle multilingual requirements, particularly Hindi and other Indian languages?

Accepted Answer

Yes. Many of our government and enterprise clients serve users across India who prefer to communicate in Hindi or other regional languages. EdubildAI has delivered multilingual RAG systems (Hindi and English), fine-tuned LLMs on Hindi medical and legal Q&A datasets, and built multilingual OCR pipelines for Devanagari and other Indian scripts. We have deep experience navigating the challenges of Indian language NLP — including transliteration, code-switching, and the limited availability of high-quality training data for regional languages.

Question 10

What OCR and document processing capabilities does EdubildAI offer?

Accepted Answer

EdubildAI builds end-to-end intelligent document processing pipelines that extract, validate, and route structured information from unstructured documents at scale. We handle scanned government forms, handwritten applications, invoices, contracts, medical prescriptions, identity documents (Aadhaar, PAN, passports), and technical drawings. Our pipelines include pre-processing (deskewing, denoising), layout analysis (tables, signatures, stamps), text recognition (Tesseract, PaddleOCR, AWS Textract, Google Document AI), post-processing (NER, validation, cross-referencing), and integration with downstream ERP or workflow systems. We achieve >97% extraction accuracy on most document types.

Question 11

Which Large Language Models does EdubildAI work with?

Accepted Answer

EdubildAI is model-agnostic and works with the full spectrum of leading LLMs: OpenAI (GPT-4o, GPT-4 Turbo, GPT-3.5 Turbo), Anthropic (Claude 3.5 Sonnet, Claude 3 Opus, Claude Haiku), Google (Gemini 1.5 Pro, Gemini 1.5 Flash), Meta (Llama 3.1 70B/8B, Llama 3), Mistral AI (Mistral Large, Mixtral 8x7B), Cohere (Command R+), and open-source models (Qwen, Phi-3, Gemma, Falcon). We select the optimal model for each use case based on performance requirements, cost constraints, latency targets, and data sovereignty needs.

Question 12

Does EdubildAI support on-premises deployment for data-sensitive clients?

Accepted Answer

Yes. For government agencies, financial regulators, healthcare organisations, and any client with strict data sovereignty requirements, EdubildAI can deploy all AI systems entirely within client-controlled on-premises or private cloud infrastructure. No data ever leaves the client's network. We use open-source LLMs (Llama, Mistral, Qwen, etc.) deployed via vLLM or TGI inference servers on the client's GPU hardware, paired with on-premises vector databases and document stores. This approach provides full data control without sacrificing AI capability.

Question 13

What is your technology stack for AI development?

Accepted Answer

Our core AI stack: Python (FastAPI, Django), LangChain, LangGraph, LlamaIndex, Hugging Face Transformers, PEFT/TRL for fine-tuning. Vector databases: Pinecone, Weaviate, Qdrant, Chroma, pgvector, FAISS, Elasticsearch. Cloud platforms: AWS (Bedrock, SageMaker), Google Cloud (Vertex AI), Azure (OpenAI Service). For ERP: Frappe Framework, ERPNext. Frontend: Next.js, React, TypeScript, Tailwind CSS. Infrastructure: Docker, Kubernetes, GitHub Actions.

Question 14

What is the Build-Operate-Transfer (BOT) model?

Accepted Answer

The Build-Operate-Transfer (BOT) model is a structured engagement where EdubildAI takes full responsibility for building and operating an AI platform on a client's behalf, then transfers complete ownership to the client's internal team at the end. A typical BOT spans 12–24 months in three phases: Phase 1 (Build) — platform design and initial use cases deployed; Phase 2 (Operate) — EdubildAI runs the system, trains the client's team, and continues development; Phase 3 (Transfer) — complete knowledge transfer, documentation handover, and transition to client-operated with EdubildAI in a support role. BOT is ideal for organisations that want to adopt AI without building an internal AI team upfront.

Question 15

What is a typical project timeline?

Accepted Answer

Timelines depend on scope and complexity. A RAG proof-of-concept can be delivered in 4–6 weeks. A production-grade RAG system with enterprise integrations typically takes 8–16 weeks. A full LLM fine-tuning engagement including dataset preparation, training, and deployment takes 8–12 weeks. Intelligent document processing pipelines range from 6–12 weeks. Full ERP implementations run 12–24 weeks. BOT engagements are structured over 12–24 months. We always begin with a Discovery phase (1–2 weeks) to define scope, architecture, and timeline before committing to a delivery plan.

Question 16

Does EdubildAI offer post-deployment support and managed services?

Accepted Answer

Yes. EdubildAI offers retainer-based managed services covering: system monitoring and alerting, AI model performance tracking (hallucination rate, latency, accuracy), periodic model retraining or fine-tuning updates as data evolves, infrastructure management and scaling, security patching, and helpdesk support for end users. We can also provide on-call support for critical production systems. Managed services are typically scoped as monthly retainers after a project delivery.

Question 17

How does EdubildAI approach AI safety, accuracy, and hallucination prevention?

Accepted Answer

AI safety and output accuracy are built into every system EdubildAI delivers. For RAG systems, we enforce source citation — every answer is grounded in retrieved documents, and citations are displayed to the user. We implement output validation layers that check responses against factual constraints, confidence scoring that flags uncertain responses for human review, and human-in-the-loop queues for regulated decisions. For agentic systems, all tool use is logged and auditable, and sensitive actions (e.g., sending emails, triggering payments) require human confirmation. We evaluate all production systems using established benchmarks (RAGAS for RAG, custom LLM-as-judge pipelines) and provide ongoing monitoring dashboards.

Question 18

How can I get started with EdubildAI?

Accepted Answer

The best way to start is to contact us via our website contact form or email info@edubild.com. We typically begin with a no-obligation Discovery call (30–60 minutes) to understand your requirements and assess AI readiness. Following that, we provide a tailored proposal covering recommended approach, architecture outline, timeline, and commercial terms. For complex or large-scale engagements, we may propose a paid Discovery Sprint (1–2 weeks) to develop a detailed technical specification and proof-of-concept before full project commitment.

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