RAG application development for document search and grounded AI answers. Build ingestion, retrieval, citations and access controls around your knowledge.
Useful knowledge is scattered across documents, and a general AI model cannot reliably answer questions about your private material. Uploading everything without a retrieval strategy creates noise.
Help people find the right passage and understand where an answer came from, with document permissions and a clear response when the evidence is missing.
Assess file formats, document quality, chunking and update frequency. Scope OCR, image-heavy PDFs and connectors separately when required.
Combine search and model responses with source references. Evaluate whether the retrieved passages actually support the answer.
Apply agreed document access rules before retrieval. Plan deletion, refresh and logging policies around your information governance requirements.
Businesses with maintained knowledge bases, SaaS teams adding document search, and internal teams working with policies, manuals or research.
We agree acceptance criteria before kickoff. Changes to scope are reviewed before additional work begins.
Service-specific scope
Choose a useful starting point. We’ll confirm fit, dependencies and the final quote before you commit.
One-time project
Best for Test one document collection
One-time project
Best for Maintain an internal knowledge tool
Custom engagement
Best for Multiple repositories or sensitive knowledge
All prices are starting estimates in USD, subject to an agreed scope. Hosting, AI/API usage, paid licenses, advertising and applicable taxes are additional. Timelines start with agreed requirements, access and content. Support hours, payment terms and acceptance criteria are confirmed in your proposal.
Compare ongoing support and managed plans →Explore the work
Browse relevant work from our public portfolio. Project examples illustrate the work; they do not imply guaranteed results for your project.
An AI-powered WordPress database explorer.
Portfolio / 02An AI content platform with a user workspace and generation tools.
From first conversation to ongoing support
We agree the stages your project needs, with review points and acceptance criteria before moving forward.
Define the outcome, scope, dependencies and budget.
Map the experience, data and technical approach.
Deliver agreed work in reviewable increments.
Configure access, environments and recovery needs.
Check critical flows against acceptance criteria.
Release with a handover and rollback plan.
Prioritize the next improvements using evidence.
Maintain and extend within an agreed support scope.
No. Grounding and citations help, but retrieval and generation still need evaluation. We include no-answer behavior and test unsupported or misleading questions.
The pilot starts with an agreed set of text-based documents. Scanned files, complex tables, authenticated connectors and large archives require a separate ingestion estimate.
RAG is the retrieval and grounding layer. It can power a chatbot, search interface or API. Choose the chatbot service when the primary requirement is a support conversation.
Plan the next step
Make your next move a clear one
Share your goal, current platform, budget range and target date. We’ll help turn the problem into a scoped next step.
Ramlit Limited is a global software & IT solutions company delivering secure, scalable, and smart digital experiences. From cloud infrastructure to AI automation — we help businesses grow, innovate, and thrive.