Custom AI Application Development Services & Integration
Raghwendra Web Services provides AI application development services that turn large language models and predictive analytics into working software, not proof-of-concept demos. Our 15-person team designs, builds, and integrates custom AI applications into your existing systems, starting with a focused readiness assessment before any code is written.
If your project is a legacy web or mobile build rather than an AI-specific application, our custom software development page covers that work instead — this page is scoped strictly to AI application development, integration, and strategy.
From Concept to Code: Full-Stack AI Software Development
Most "AI-powered" products on the market are wrappers around a single API call — a thin interface bolted onto someone else's model, with no ownership of the underlying logic or data pipeline. That's a reasonable way to test an idea, but it rarely survives contact with real operational requirements: rate limits, data governance rules, and the need to swap models as better ones become available.
We build differently. Every engagement starts with a working MVP scoped to a single, measurable use case — a document-processing workflow, a recommendation engine, a customer support assistant — rather than a sprawling roadmap of every AI feature you might eventually want. The MVP runs on real data from day one, using retrieval-augmented generation and vector search where it improves accuracy, instead of depending purely on a model's built-in knowledge. Once it proves out against your actual data and users, we expand the architecture one validated layer at a time.
This gives your team a working application to react to within weeks rather than a specification document, and it keeps spend tied to a proven result before further investment is agreed.
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Predictive Analytics & Data Modelling
Data Forecasting & APIs
We build models that forecast demand, flag anomalies, or score risk from your historical data, using Python-based frameworks to structure the pipeline from raw data through to a model your team can query directly. The output is typically a dashboard or an API endpoint your existing systems call, not a one-off spreadsheet.
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Generative AI & Large Language Model Integration
LLM Integration & RAG Architecture
We integrate models from OpenAI, Anthropic, and open-source options like Llama 3 into your applications, using LangChain and LlamaIndex to manage context, memory, and retrieval. Rather than fine-tuning a model from scratch — an expensive step that's often unnecessary — we typically ground responses in your own data through retrieval-augmented generation, which keeps outputs accurate and auditable.
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NLP & Intelligent Document Processing
Automated Text Extraction
For document-heavy workflows — contracts, claims, compliance filings — we build pipelines that extract, classify, and summarise text at volume, feeding structured output into your existing case-management or CRM tools rather than leaving it stranded in a separate AI dashboard.
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Computer Vision & Image Recognition
Visual Data Analysis
We also take on computer vision work — image classification, defect detection, and extracting data from scanned images — scoped project by project against your specific accuracy requirements.
Connecting AI Into Your Existing Enterprise Systems
A new AI application only earns its keep if it can read from, and write back to, the systems you already run each day. We build API-based connections between the applications we develop and your existing CRM, ERP, or data warehouse tools, so results appear inside the systems your team already works in, rather than in a separate portal nobody logs into.
Get in touch to walk through the specific systems you'd need this connected to.
Where AI Creates Value Across Your Business
Rather than pitching a single industry vertical, we scope AI applications around the operational problem you're solving — the underlying engineering is similar whether the workflow sits in logistics, professional services, or retail.
Common starting points include automating first-line customer support so your team only handles escalations, building an internal knowledge base that lets staff query company documentation in plain language, and running predictive analysis on operational data to flag issues before they become expensive. Each can be delivered as a contained MVP and expanded once it's proving value.
The Technologies Powering Our AI Applications
Our core stack centres on OpenAI's API and Anthropic's Claude models for generative AI work, with Llama 3 available as an open-source option where data residency or cost rules out a hosted API. We use LangChain and LlamaIndex to manage retrieval, memory, and multi-step reasoning, paired with vector databases such as Pinecone or ChromaDB to ground model outputs in your own data. The application layer itself is built in Python.
Data Privacy and AI Security
RWS is not currently certified to a formal security framework such as SOC 2, and we don't claim one. What we commit to contractually is a signed NDA before any project data changes hands, with access to that data limited to the engineers working on your project throughout the engagement.
Deployment depends on the components involved: calls to hosted models like OpenAI or Anthropic run in that provider's cloud environment, while the retrieval layer — your own documents and data, held in a vector database — can typically be deployed within your own cloud account, keeping your proprietary data under your own access controls.
Our AI Development Process: From Readiness to ROI
Every engagement starts with a discovery phase we scope as an AI readiness assessment: reviewing your existing data, systems, and the specific problem you want solved, then producing a technical roadmap before development begins. From there, we move into data preparation and architecture design, structuring your data for retrieval and defining how the application will connect to your existing systems.
Build work happens in an MVP-first cycle rather than one long build phase — a working version reaches your team early, tested against real inputs rather than sample data. Once it clears QA and deployment, the project isn't treated as finished: ongoing monitoring tracks model performance and flags when outputs drift or a newer model becomes worth switching to, so the application keeps working as intended rather than degrading quietly in the background.
Frequently Asked Questions (FAQs)
Because every enterprise AI application involves a unique data footprint and integration scope, we provide value-based pricing following an initial discovery call rather than a flat rate.
Yes — we build API-based connections between the AI applications we develop and the systems you already run, so results surface inside your existing tools.
Yes. Full IP ownership of the application and any models we build transfers to you on completion — the code, configuration, and trained system are yours, not licensed back to you.
Timelines depend on scope: a focused MVP addressing one use case moves faster than a multi-system enterprise rollout. We scope a realistic timeline during the readiness assessment rather than quoting a generic figure upfront.
We sign an NDA before any project data changes hands, with access limited to the engineers working on your project. RWS doesn't hold a formal certification such as SOC 2 — this is our current, honest position rather than a compliance guarantee.
Custom. We integrate underlying models like OpenAI, Anthropic's Claude, and Llama 3 into an application built around your data and workflow, using LangChain and LlamaIndex for retrieval — not a generic AI wrapper product with your logo on it.
Ready to talk through an AI application development services project for your business?
Get in touch to book an AI readiness assessment and see what a working MVP could look like against your own systems.
