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RAG Development and LLM Integration Services
Go beyond basic chatbots. We build intelligent assistants that help users explore complex information and retrieve relevant answers from business data: natural-language questions over your documents and MySQL databases, and language-model features inside the web and mobile software you already run. Our Pune team builds the AI services in Python and FastAPI and connects them to Laravel applications and OpenAI GPT-class models.
What We Build
A conventional chatbot may answer general questions, but business applications often require more. They need to identify the user’s intent, retrieve relevant information from the right data sources and generate responses grounded in that information.
RAG knowledge assistants
Retrieval-augmented generation over your documents, manuals and policies, so answers come from your own content rather than only the model’s general knowledge.
Natural language to SQL
Ask “what was spent on structural work last quarter?” and get an answer from your MySQL data. The system turns the question into a controlled database query and explains the result.
AI assistants for large datasets
Interpret the question, retrieve only the relevant records through controlled queries or search, and generate an answer from those results.
LLM integration into existing software
Add language-model features to your current web or mobile application through a backend service it calls. See Custom Software Development.
Domain-specific knowledge assistants
Assistants that understand the terms and structure of your industry, such as work categories and rates in construction.
Data validation and response controls
Checks on what the assistant can access and on the answers it returns, matched to how critical the information is.
How It Works
The exact architecture depends on your data format, scale, security requirements and use case. This is the pattern most of our RAG and LLM work follows.
How a data-aware assistant works
- User question. A user asks a question in plain language.
- Question and context understanding. The system interprets intent and the relevant context, such as the project or module.
- Relevant data retrieval. Matching records and documents are fetched from the right sources.
- AI answer generation. A language model drafts an answer based on the retrieved information.
- Response to user. The answer is returned in the app, with checks applied where required.
Architecture
A FastAPI service in Python handles the question, the retrieval and the call to an OpenAI GPT-class model through its API. Your data stays where it is, usually a MySQL database behind a Laravel application, and the Laravel front-end or your mobile app shows the answer. Everything runs on your VPS or cloud account behind Cloudflare.
Retrieval or fine-tuning?
Retrieval-augmented generation (RAG) looks up the relevant information at the moment a question is asked and gives it to the model as context. Fine-tuning instead changes the model itself by training it on your examples. For business data that changes every day, retrieval is usually the better fit: answers reflect your latest records, you can see which sources were used, and your data does not become part of a model.
What text-to-SQL means
Text-to-SQL turns a plain-language question into a database query. The system reads the question, works out which tables and fields it needs, builds a query within the limits you allow, runs it against your database and turns the result into a readable answer. Rather than sending an entire database to a language model, it sends only the question and the few rows the answer needs.
How we keep your data safe
Your data stays in your own database. For each request, the system sends the model only the records or passages that request needs, never your whole database, and access follows your user roles. We do not use your data to train AI models, credentials and API keys stay on the server, and we review the data-handling terms of any AI provider we use. AI responses can still be wrong, so we design validation and review steps to match how critical the information is.
How a RAG Project Runs With Us
Six stages, starting small and proving value before a full rollout.
1. Use case and questions
We collect the real questions your team asks and agree what a good answer looks like.
2. Data audit
We review your documents, database structure and access rules to decide between document retrieval, text-to-SQL or both.
3. Proof of concept
A focused version answers a defined set of questions on your real data, so you can judge it before committing to more.
4. Build and integrate
The FastAPI service is built and connected to your Laravel application, mobile app or admin panel.
5. Test against edge cases
We test against realistic questions and edge cases, add validation rules and agree where human review is needed.
6. Monitor and improve
We monitor errors and answer quality after launch and extend the assistant as new questions appear.
What Drives the Cost of RAG and LLM Integration
Timelines and cost depend on scope, the number of integrations, data quality, security requirements and overall complexity.
- Data sources. One clean database is quicker to connect than several systems, spreadsheets and document stores.
- Data quality. Inconsistent or incomplete data has to be cleaned or mapped before answers can be trusted.
- Security and access rules. Role-based access and audit requirements add design and testing work.
- Integration points. Adding answers inside an existing web or mobile app means work on that app as well as the AI service.
- Usage. Model API charges depend on the number of questions and the amount of context each one needs.
A focused proof of concept is usually much quicker than a full production rollout. Tell us about your project and we can give you a realistic estimate.
Keystone Insight AI: Construction Data You Can Ask Questions Of
Construction projects involve detailed quantities, cost information, technical records and extensive project data. Navigating this information manually can be challenging. For Keystone QS, a quantity-surveying firm, we built Keystone Insight AI, a construction cost-estimation assistant that answers natural-language questions about project costs and quantities.
It is built on top of systems we delivered first: the Keystone QS HRMS & ERP and the Keystone QS cost estimation app, which hold the project data the assistant answers from.
Business problem
Construction information can be extensive and structured in ways that are difficult to explore through manual searches alone.
Solution approach
A FastAPI backend and Laravel front-end interpret the question, identify the project context, retrieve the relevant cost and quantity data from MySQL and generate a useful response.
Business value
More intuitive access to complex construction information, less reliance on manual searching and a conversational interface for project data.
Frequently Asked Questions
Have Data Your Team Struggles to Search?
Tell us the questions your team asks every week and where the answers live today. A 30-minute call is enough to say whether RAG, text-to-SQL or a simpler report is the right fit.