RAG knowledge assistants
Answers for staff or customers from your policies, manuals, tickets and wikis, with a citation for every answer.
RAG knowledge assistants and AI agents
Generative AI development
Generative AI development services for apps that work on your data
We build RAG assistants, LLM-powered apps and AI copilots that answer from your documents, databases and tools, cite their sources and know when to hand over to a person. Then we run them, measure them and keep them accurate.
Engineering team in Jaipur, India. Building for teams in the US, UK, Europe, UAE, Australia and India.
Generative AI applications we build
Every build starts from a business task with a measurable outcome, not from a model.
LLM apps and AI copilots
Generative features inside your own product or internal tools: drafting, summarising, search and natural-language queries over your data.
AI features inside your productDocument AI
Read invoices, contracts, forms and statements, extract the fields and push them into your systems.
Document AI and data extractionCustom GPTs for business
Private assistants for sales, HR, finance or legal teams, limited to the data and tools each team is allowed to use.
Voice and WhatsApp AI
The same models behind phone agents and WhatsApp assistants that talk to your customers.
Fine-tuning and model evaluation
When retrieval alone is not enough: fine-tuned open-weight models, structured outputs and side-by-side tests of models on your own questions.
Where generative AI pays off first
Customer support
Most tickets repeat questions your documentation already answers.
- Suggests replies to agents, grounded in your help centre and past tickets
- Answers customers directly on chat, email or WhatsApp, and hands over with a summary
- Tags, routes and summarises tickets
Sales
Reps spend hours on research and first drafts.
- Drafts proposals and RFP answers from approved content
- Summarises calls and updates the CRM
- Qualifies inbound leads before a rep calls
Finance and operations
Data arrives in PDFs and emails.
- Extracts invoice, purchase order and contract data
- Answers "what did we agree with this supplier?" from the contract archive
- Writes first-draft management reports from your numbers
HR
The same policy questions arrive every week.
- A policy assistant that answers from your handbook, by country
- Screens applications and schedules interviews
- Drafts job descriptions and offer letters for review
Product and engineering
Teams want AI features without building an AI team.
- Natural-language search inside your SaaS product
- In-app copilots that draft, summarise or explain
- Internal assistants over code, runbooks and incident history
We run generative AI in our own products first
Before we build for you, we build for ourselves. These products use the same retrieval, extraction and agent patterns we deliver to clients.
Own product · AI agents
Prilient AI Agents
Six AI agents (Receptionist, Customer Support, WhatsApp Lead Generation, Appointment Setter, Debt Collection and HR Interview Screening) that understand open questions, follow business rules and hand conversations to each other.
See Prilient AI Agents
Client project · Document AI
DataSwitch
AI document extraction that turns invoices, forms and statements into structured data, built for a client.
DataSwitch document AI
Own product · AI business software
VyapaarSense
AI business management software for SMBs, with document AI that reads supplier bills into the books, bank reconciliation and cash-flow forecasts.
See VyapaarSenseFrom idea to a generative AI app your team trusts
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Discovery sprint
We score your use cases on value, data readiness and risk, pick one, and agree how success will be measured.
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Data and evaluation set
We connect the sources, clean and chunk the content, and write a test set of real questions with approved answers.
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Prototype
A working version on a sample of your data, tested against the evaluation set with two or three candidate models.
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Pilot with guardrails
Real users, permission-aware retrieval, citations, PII masking, fallback to a person and full logging.
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Production and LLMOps
Monitoring for accuracy, cost and speed, regular re-evaluation, and model upgrades without rewrites.
RAG development services: how a grounded assistant works
Retrieval-augmented generation keeps answers tied to your own, current data. This is the architecture we start from and adapt to each project.
Diagram of a retrieval-augmented generation pipeline from company data sources through vector search and a language model to a chat app, with an evaluation loop
- Your sources documents, wikis, tickets, databases, CRM and ERP
- Ingestion parsing, OCR, cleaning, chunking, and access rights copied from the source
- Embeddings and vector search a vector database alongside keyword search
- Retriever and re-ranker finds the passages this user is allowed to see
- LLM with guardrails prompt, citations, PII masking, refusal rules, structured output
- Your app web chat, Slack or Teams, WhatsApp, voice or an API inside your product
Evaluation and monitoring
loops back to steps 2–5 accuracy tests, feedback, cost and latency dashboards
Model-agnostic by design
We pick the model per task on your evaluation set, and build so you can switch later.
Frameworks
- LangChain
- LlamaIndex
- Our own orchestration in Python and Node.js
Application layer
- Python (FastAPI)
- Node.js (NestJS)
- Laravel
- React
- React Native
Built for data you cannot afford to leak
- Your data is not used to train public models: we use business API terms that exclude it, or host open-weight models in your cloud
- Retrieval respects the permissions your users already have
- Personal data can be masked before it reaches the model
- Every prompt, answer and source is logged for audit
- Prompt-injection and jailbreak tests before launch
- Set up for the rules that apply to you, such as GDPR, UK GDPR and India's DPDP Act
Start small, then scale what works
Discovery sprint
Use-case scoring, data review, architecture and a fixed quote for the build.
AI strategy and roadmapPrototype or pilot
A working application on your data, tested with real users before you commit to production.
Production build and run
We deliver, host or hand over, and keep improving accuracy every month.
Dedicated generative AI engineers
Add our engineers to your team, working in your tools and stand-ups.
Dedicated generative AI engineersWe share a working day with the UK, Europe and the UAE.
Questions buyers ask about generative AI development
What do your generative AI development services include?
We scope the use case, prepare your data, build the application and run it after launch. Typical builds are RAG knowledge assistants, AI copilots inside your product, document extraction, content and report generation, and AI agents that take actions in your systems. Each build ships with an evaluation set, guardrails, monitoring and a handover to your team.
What is RAG, and do we need it or fine-tuning?
RAG (retrieval-augmented generation) finds the relevant passages in your own documents and data and gives them to the model with each question, so answers are current and can cite their source. Most business assistants need RAG first. Fine-tuning helps when you need a fixed tone, format or specialist vocabulary, and we often combine the two.
Which model should we use: GPT, Claude, Gemini or an open-source LLM?
It depends on accuracy, cost per request, speed, language support and where your data must stay. We test two or three candidate models on your own evaluation set before choosing, and we build so you can switch models later without rewriting the application.
How do you stop the AI from making things up?
We ground answers in your approved sources, show citations, and tell the model to say when it does not know. Before launch we measure answers against an evaluation set of real questions, and after launch we review low-confidence answers and user feedback so accuracy keeps improving.
Is our data safe, and will it be used to train public models?
We use model providers' business APIs, whose terms exclude your data from training, or we host open-weight models in your own cloud. Access follows your existing document permissions, personal data can be masked before it reaches the model, and every request is logged. We set each project up for the rules that apply to you, such as GDPR, UK GDPR or India's DPDP Act.
How much does a generative AI project cost?
Cost depends on the number of data sources, integrations, users and the accuracy bar. We start with a fixed-price discovery sprint, then quote a fixed price for a pilot and a monthly fee for running and improving the system.
How long does it take to build a generative AI application?
A working prototype on a sample of your data usually comes first, then a pilot with real users, then production. Most of the time goes into data preparation, integrations and testing, not the model itself.
Can you work with our in-house team and time zone?
Yes. Our engineering team is in Jaipur, India, and works with clients worldwide. We share a working day with the UK, Europe and the UAE. We can deliver the whole build or add generative AI engineers to your team.
Have a generative AI idea? Let's test it on your data.
Bring one use case. In a 30-minute call we will tell you whether it needs RAG, fine-tuning or neither, and what a first version would take.