Turn your documents into answers your users can check.
Your users need answers from your information, inside the product or workflow they already use. Indext develops retrieval-augmented generation (RAG) systems around that job: preparing sources, finding relevant evidence and connecting an AI assistant to your application. Start with one useful workflow and agreed acceptance criteria.
Discuss your RAG projectFree 20–30 minute scoping call · First stage from $5,000
Content, updates and access rules
Find and rank material for the question
Grounded output with source references
Evaluate quality and the complete workflow
Start with the question your users cannot answer today
A support specialist may need an answer buried in several manuals. A customer may need to search a subscription knowledge base. A product team may need document intelligence inside an existing application. These are different products, even when each uses RAG.
We first define who will ask questions, which sources they may access and what they should receive: an answer with references, a ranked result list or a structured output. If a simpler search interface or an existing platform meets the requirement, that should inform the scope.
A working component, from sources to application
The first stage covers the parts needed to test your actual use case. We agree on the boundaries before implementation; not every project needs every component below.
- Source preparation: sample documents, extraction quality, document structure, metadata and a plan for updates or deletions.
- Retrieval: an approach to indexing, filtering and ranking that can be tested against representative questions.
- Answer generation: instructions for using retrieved evidence, source references and an explicit response when the evidence is insufficient.
- Access controls: requirements for users, teams or customers, with permission checks included in retrieval and testing where the project needs them.
- Product integration: the agreed API or interface, error handling and a clear boundary with your existing application.
- Handover: implementation, configuration and the checks your team needs to review and maintain the agreed component.
Evaluate the difficult questions before expanding the system
A convincing demonstration is not enough to judge a RAG feature. We propose an evaluation set drawn from the questions your users will actually ask, including ambiguous questions, missing information and documents that disagree.
The acceptance plan separates finding the right evidence from producing a supported answer. Depending on the workflow, it can also cover permission boundaries, source freshness, response time and operating cost. You decide which failures are tolerable and which must block release.
RAG does not guarantee that every answer will be correct. Source links, an appropriate refusal to answer and a route to human review should be designed around the consequences of a mistake.
Relevant engineering work
In our ChatGPT-Based Knowledge Assistant project, we combined PDF text cleaning and chunking, vector indexing and a conversational interface. The system retrieves relevant document sections before generating an answer.
For AI Semantic Talent & Company Sourcing, we built data ingestion, embeddings, vector search and LLM-assisted scoring. The FastAPI and PostgreSQL/pgvector backend supports ranked discovery across a multi-million-record dataset. This is related retrieval and search experience; it is not a claim that every RAG project needs the same architecture.
One scoped stage before a larger commitment
Start with a free 20–30 minute call. Bring a few example questions, an overview of the sources and the place where the feature should appear. You do not need a finished technical specification.
Our starting process is a written scope and fixed price for stage one within two business days. First stages start from $5,000. This is a starting price for an agreed stage, not a fixed price for an entire RAG system. Source quality, permissions, integrations and acceptance requirements determine the work.
Stage one might validate retrieval against a sample corpus or deliver a bounded feature inside your product. Its output should make the next decision easier: continue, adjust the approach or stop before committing to a broader build.
For agencies and product teams
If you lead a client project, we can take responsibility for the agreed data or AI component while your team leads the product. We define interfaces, review points and handover together. NDA and no-poach are standard; white-label delivery is available on request.
If the assistant is the beginning of a new product, the first conversation should also cover the user journey and the smallest useful release. We can help scope the broader product, rather than treating retrieval as an isolated feature.
Plan your RAG implementation
If you are comparing approaches or preparing a brief, these guides can help you identify the decisions that affect scope.
Questions before we start
Do we need clean documents before we contact you?
No. A representative sample helps reveal extraction and structure problems early. We can include data preparation in the proposed scope rather than assuming every file is ready to index.
Can the assistant work with different access permissions?
We can scope access requirements for your application. We need to understand where permissions are stored, how they change and which users must be prevented from retrieving particular content.
Do we need to replace our existing application?
Not necessarily. We can define a component that integrates through an API or an existing interface. The integration boundary is part of the first-stage scope.
What happens after the first stage?
We review the agreed outputs and test results with you. Further development is scoped as a separate decision, with its own deliverables and price.
Discuss your RAG project
Start with a free 20–30 minute scoping call. A few example questions and an overview of your sources are enough to begin.
Discuss your RAG project