AI Development Services for LLM Apps, RAG and Workflow Automation
RED SAG is an AI development company in India providing AI development services for businesses that want generative AI to do useful work inside their own systems: search and answer questions across company documents, read invoices and KYC documents, draft routine replies and reports, and automate multi-step back-office tasks. We build the application around the model, which is where most of the effort and most of the value sits.
As a generative AI development company we are model- and vendor-neutral. Depending on your data, budget and privacy needs we work with OpenAI, Anthropic Claude, Google Gemini or open-source models you host yourself, and we design the system so the model can be swapped later without a rebuild. Before anything goes live we agree how quality will be measured, what the AI is not allowed to do, and what each request is likely to cost.
If you mainly need a customer-facing chatbot or WhatsApp bot, see our AI chatbot development page. This page covers the wider work: internal tools, document pipelines, AI agents and AI features added to software you already run.
Who AI Development is for
Teams buried in documents
Finance, operations and compliance teams that key in data from invoices, purchase orders, bank statements, contracts or KYC documents by hand every day.
Companies with knowledge scattered everywhere
Businesses whose policies, SOPs, product manuals and past tickets sit across drives, wikis and email, so staff waste time finding the right answer or ask the same senior person.
Software products adding AI features
SaaS and app owners who want summarisation, smart search, drafting, classification or an in-app assistant added to an existing product without rewriting it.
Operations with repeatable multi-step work
Back offices that follow the same steps across several systems, such as checking an order, updating a record and emailing a customer, and want parts of that handled by an AI agent with human approval.
Features we build
RAG over your company documents
Our RAG development services cover retrieval-augmented generation that searches your PDFs, wikis, spreadsheets and databases, then answers with references to the source passages. Access rules follow your existing permissions, so staff only see answers from documents they are allowed to read.
Document processing and extraction
Pipelines that read invoices, bills, bank statements, ID and KYC documents, forms and contracts, extract the fields you need into structured data, flag low-confidence values for a person to check, and post clean records into your ERP or accounting system.
AI agents and workflow automation
Our AI agent development services build agents that call your APIs to look up data, create drafts, update records or route work, with a defined list of allowed actions, spending and rate limits, and approval steps for anything that changes money, customer data or external communication.
AI features inside existing apps
Summaries, semantic search, auto-tagging, reply drafting, report narration and natural-language filters added to your web or mobile app through a clean backend service, without handing your whole database to a model.
Model choice and hosting options
OpenAI, Anthropic Claude, Google Gemini or open-source models such as Llama, Mistral or Qwen, called through official APIs or cloud platforms like AWS Bedrock, Azure OpenAI and Vertex AI, or self-hosted where data must stay on your own servers.
Evaluation before and after launch
A test set built from your real questions and documents, scored for accuracy, grounding and tone. We re-run it whenever prompts, data or models change, so improvements are measured rather than guessed.
Guardrails and data privacy
Input and output checks, masking of personal data before it leaves your systems where required, prompt-injection defences for documents and web content, and logs of what the AI saw and did. We design with India's DPDP Act, GDPR or other applicable privacy rules in mind.
Cost control and monitoring
Per-request cost estimates before you commit, caching, smaller models for simple steps, and a dashboard showing usage, latency, failures and spend so the monthly model bill does not surprise you.
From first call to launch
- 01
Pick one valuable use case
We look at where time or errors really go, choose a use case with a measurable outcome, and check that the data needed for it exists and can be used.
- 02
Prototype on your own data
A short proof of concept on a sample of your real documents or tickets shows what accuracy is realistic and which model and approach fit best.
- 03
Define evaluation and guardrails
We agree on the test set, the quality bar, what the AI must never do, when a person takes over and the expected running cost.
- 04
Build and integrate
We build the production service, connect it to your systems and users, and add logging, permissions, monitoring and admin controls.
- 05
Launch, measure and improve
We release to a small group first, review results and failures weekly, then widen use once the numbers hold up.
What you receive
- Written use-case brief with success measures and cost estimate
- Proof-of-concept results on a sample of your own data
- Production AI service with API, admin screens and user interface as scoped
- Document ingestion and indexing pipeline with permission handling
- Evaluation test set and a repeatable scoring script
- Guardrails, audit logs and human review workflow
- Usage, cost and quality monitoring dashboard
- Deployment on your cloud account with your own model API keys
- Complete source code, prompts and technical documentation
What AI Development costs
A proof of concept usually takes 3–5 weeks. A production RAG system or document pipeline typically takes 2–4 months, and multi-system agent projects 4–6 months, including evaluation and a staged rollout. Model API or GPU hosting costs are billed separately by the provider.
Proof of concept
₹1.25–3 lakh (≈ $1,800–4,500)
One use case tested on a sample of your data, with an evaluation report, working demo and recommendation on model, approach and running cost.
Production RAG or document pipeline
₹3–7 lakh (≈ $4,500–11,000)
A document Q&A system or extraction pipeline integrated with your systems, with permissions, review screens, monitoring and evaluation.
AI agents across several systems
₹7–18 lakh+ (≈ $11,000–27,000+)
Multi-step agents or AI features across several products or departments, with approval workflows, self-hosted model options and detailed audit logs.
Indicative budgets based on typical projects. Your fixed-price quote depends on scope, integrations and timeline.
Tools and technology we use
- Python
- FastAPI
- Node.js
- Next.js
- OpenAI API
- Anthropic Claude
- Google Gemini
- Llama and Mistral
- PostgreSQL + pgvector
- Qdrant
- LangGraph
- AWS Bedrock
- Docker
Why teams choose us
Software engineers first
Most of an AI project is ordinary software: data pipelines, permissions, integrations and user interfaces. We build those properly, so the model sits inside a system you can maintain.
No lock-in to one AI vendor
We keep model calls behind a thin layer and you hold the API accounts, so you can change provider when prices, quality or policies change.
Honest about what AI can do
If a use case is better solved with rules, a search index or a simple form, we say so. Where AI fits, we show measured accuracy on your data before you scale it.
Fintech-grade care with data
Our fintech and accounting work means we are used to audit trails, role-based access and careful handling of financial and personal data.
Frequently asked questions
How much do AI development services cost?
A proof of concept on your own data typically costs ₹1.25–3 lakh (about $1,800–4,500). A production RAG system or document processing pipeline is usually ₹3–7 lakh, and multi-system AI agent projects start around ₹7 lakh. Model usage or GPU hosting is paid directly to the provider, and we estimate that monthly cost before you commit.
How is this different from your AI chatbot development service?
Our AI chatbot page focuses on customer-facing bots on websites and WhatsApp. This service covers broader generative AI work: internal knowledge search, document extraction, AI agents that act inside your systems, and AI features built into your existing software.
Which AI model do you use?
It depends on the task. We compare OpenAI, Anthropic Claude, Google Gemini and open-source models on your own test set, then weigh accuracy, speed, cost and data rules. Often different steps use different models, such as a small, cheap model for classification and a stronger one for complex answers.
Is our data used to train the AI provider's models?
Business API offerings from the major providers generally state that API data is not used for training by default, but terms differ by provider and plan and can change, so we review the current terms with you. Where data must not leave your environment, we can use cloud-hosted enterprise options in your chosen region or self-hosted open-source models.
How do you stop the AI from making things up?
We ground answers in retrieved source documents, show references, instruct the model to say when it does not know, and test against a set of real questions with known answers. For high-stakes outputs such as payments or customer communication, a person reviews before anything is sent or posted.
Can AI extract data from invoices and KYC documents accurately?
For clear, reasonably standard documents, modern models extract most fields reliably, but accuracy varies with scan quality, layouts and languages. We measure field-level accuracy on your samples, set confidence thresholds, and route uncertain fields to a reviewer instead of posting them blindly.
Can you add AI to our existing software?
Yes. We usually add a separate AI service that your application calls through an API, so your core system changes very little. We work with existing codebases in JavaScript, TypeScript, Python, PHP and other common stacks, after a short code review.
Is there an AI development company near me, or do you work remotely?
Our team is in Tiruppur, Tamil Nadu, and we work remotely. Most clients using our AI development services in India, and all of our overseas clients, never need to meet in person: we run scoping calls, prototype reviews and weekly check-ins over video, and give you a test environment to try the work yourself. If AI development near me matters because you want a face-to-face meeting, clients around Tiruppur can visit by appointment.
How do I choose between the top AI development companies?
Ask each one to test your use case on a sample of your own data before quoting the full build, and to show you how they will measure accuracy, cost and failures. Rankings of top AI development companies say little about your problem. The best AI development company for you is the one that can show measured results on your documents and is honest when a simpler, non-AI approach would work better.
Do you work with companies outside India?
Yes. We work with clients in the USA, UK, Australia and Canada as well as India, with overlapping working hours for calls and reviews. Data residency and privacy requirements such as GDPR or UK GDPR are discussed at the start and reflected in the hosting and model choices.
Related solutions
AI Chatbot and WhatsApp Automation Development
AI chatbots, WhatsApp Business API automation and AI agents for lead capture, support, bookings and order updates.
SaaS Product Development
MVPs and growing SaaS products with multi-tenancy, Stripe or Razorpay subscriptions, admin panels and product analytics.
Fintech Software Development
Payment gateway and UPI integration, lending systems, wallets, eKYC and fintech MVPs built with audit-ready foundations.
We work with clients in India, United States, United Kingdom, Australia, Canada, Europe and New Zealand.
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