A model that isn't aware of your sources makes assumptions based on sound reasoning. We support our answers by referring to the excerpts we have gathered, checking whether the cited references support the claims, and testing our answers in practice.
RAG Development Company
Why RAG Matters for Your Company Data
Braintix Labs offers RAG development services that use AI to connect to your documents, knowledge bases, and enterprise systems, making sure your answers come from verified sources. We start with a concrete use case, test and run it against your data, and expand its capabilities.
Benefits
Business challenges RAG can solve
Such issues usually arise when a team first tries to apply a broad-based artificial intelligence system to business problems. Each issue has a theoretical resolution, but none disappears completely.
The model cannot decide on a new policy alone so we would get obsolete results from the old index. We sync per event or on a schedule, remove old files from the index, and use people to keep all sources up to date.
Contracts are in Drive, procedures in Notion, client communications in the CRM, so you have to check multiple places to verify a single fact. A common search engine searches all integrated sources and returns an answer with a link to its source.
Valid concerns exist about sending sensitive files to an AI-driven tool. We filter results for each user’s access rights before the content arrives at the model, treat the source content as data (not instructions), and can deploy the system on your infrastructure.
Solution can connect with
Systems your RAG solution can connect to
The success of the RAG system depends entirely on the availability of different data sources. Our system connects with your existing document and data-handling systems, as well as other platforms you send requests through, depending on their API settings.
- Google Drive
- Notion
- Slack
- Telegram
- Pipedrive
- HubSpot
- NetHunt
What we build
Custom RAG development services
Your inquiries, documents, and quality standards differ from those of other businesses, so a ready-made solution may not fit your needs. At the start of developing a custom solution for your case, our custom RAG development services address the most typical user inquiries and develop rules for generating answers according to these inquiries.
For your team
How a RAG system helps your team
The retrieval-augmented generation system, powered by enterprise data, works within the same tools your team already uses. Here's how it works during a typical workday.
About solution
The user will be provided with a result after entering a question in the Slack app or the website application, along with links to the documents from which the information is derived.
Those sources that the user cannot access are filtered out of the results. If no sources are provided, it is stated accordingly.
About solution
New colleagues can ask the system about procedures, software, and deadlines without disturbing their colleagues. Because the answers link to the relevant documents, they can find the required information.
Managers don't have to repeat the same explanations to new employees during induction. New employees' searches also help identify training material that needs further explanation.
About solution
In real-time, the platform aggregates the customer’s interaction history and selects the right response strategy.
The representative decides what information to send, and complex questions are routed to specialists with the right context. The answers stay the same because everyone works from the same material.
About solution
Large documents, including contracts, reports, and meeting minutes, are summarized to highlight key information in each section. Employees can ask follow-up questions about the document's contents without reading it cover to cover.
Employees can view two policies or contracts simultaneously. Summaries help employees decide which documents they need to read in full, but they are not a replacement for doing so.
Tell us where your company knowledge lives
Bring one workflow and a list of the sources behind it. We’ll review them with you and outline a first use case you can test.
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Our RAG Development Process
Each of these stages is followed by the creation of a reviewable product, be it a concrete application case or an assessment review, so that you may proceed depending on the outcomes.
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Project analysis
Our RAG development firm determines the system's users, what questions need to be answered, and how the software fits into those individuals' workflows. The result is a defined usage framework with explicit acceptance guidelines.
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Data audit
Each source is analyzed by our RAG development agency for quality, formatting, ownership, access conditions, and update frequency. You get a report highlighting deficiencies, possible threats, and suggested ways to clean your data.
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Architecture preparation
Using the audit findings, we select a data discovery methodology, a modeling approach, an information integration approach, and development parameters. You will obtain an overview of the architectural design and a preliminary price estimate for the project.
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Development phase
We are developing data import modules, a search pipeline, and the necessary connector components through short-cycle development of RAG services & solutions. You can review working versions using your own content as each component comes online.
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Testing and evaluation
We perform statistical analysis to assess the pertinence of search outcomes, information credibility, links, permissions, responses to lack of results, delay, and cost per search query. The report will be generated in accordance with mutually agreed standards.
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Launch and support
We pilot the product with a select group and, if it performs as expected, roll it out. Our RAG development company has a monitoring system, source-updating policy, contact person, and feedback system.
See it running on your own data first
We start with a short pilot on your real history. If the accuracy does not hold up, you have lost a few weeks — not a year-long platform contract.
Request a Pilot
Best fit
Where RAG solutions deliver business value
A RAG software development solution functions optimally when employees consistently search for information available somewhere in the organization. The most common situations are:
Internal knowledge bases
Now HR policies, sales brochures, and internal procedures are easily searchable and available in simple language. Employees can access what they need, including quotes and source links, instead of flipping through documents or waiting for co-workers to answer their questions.
- Simple language searches for answers from policies and instructions
- Search results filtered according to the employee's job description.
- Content owners of outdated sources notified
Document search and retrieval
All these departments usually need a sentence or a figure that can be found in a very long contract or report. A key benefit of using both semantic and keyword search is that it becomes easier to locate fragments that don't match the search terms.
- Find clauses and terms in extensive contract collections
- OCR for scans of contracts and old files
- Direct links to the appropriate page or section
AI chatbots and virtual assistants
Client assistants can answer questions about products, policies, and procedures using only approved information. If you haven't decided on a platform yet, review how chatbots, AI-powered assistants, and AI-powered agents compare with your needs.
- Responses are restricted to approved and published content
- If sources are not sufficient, get in touch with the person in responsibility
- Development on a website, in a messaging app, or in an application
Data analysis
RAG development systems will provide definitions, commentary, and methodology to help explain the numbers. We will access the most up-to-date information and computations by connecting the agent to your databases or business analytics systems, assuring that the computations are not generated text rather than the result of a verified query.
- Definition of metrics and report information on demand
- Current data obtained from databases or business analysis tools
- A clean distinction made between explanation and calculation
What sets us apart
Why choose Braintix as your RAG development agency
Why choose Braintix as your RAG development agency
RAG development services implementation is done in accordance with the manner in which we do other AI implementations – that is, first implementing one use case with actual data and then implementing it at scale.
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Start with a measurable use case
We select a process to work on, such as managing inquiries related to internal policy, and define our success criteria before the design process. This gives us a minimum viable product of RAG application development we can test and assess for effectiveness.
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Build around your existing systems
Your existing integration software doesn't require you to move your content to another platform. Your audience will get answers on their work premises through chat, CRM, or any other internal platform.
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Keep sensitive data under your control
You can install the system on your private servers, as we have with the AI Knowledge Agent. Access control by roles lets employees access certain information based on the permissions defined for their roles.
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Pilot first, commit later
We begin with a smaller-scale RAG development services pilot study using real data, rather than a full-year contract to use this technology. In the end, if it fails, you will have lost just a few weeks, not an entire year.
AI projects we've delivered
Our Cases
5 → 1tools replaced by one CRM
Custom AI CRM
A CRM built around your business process, with messengers, lead tracking, dashboards and an AI autopilot inside. Add finance, project management, HR or any other module you need, and run your whole business from one place.
1 questioninstead of 5+ services
AI Knowledge Agent
A private AI agent connected to your documents, CRM and finance systems. Employees ask questions and get answers, reports and files, with role-based access.
5+data sources in one view
AI Analytics Dashboard for End-to-End Analytics
A custom analytics dashboard that connects your CRM, finance systems, Google Analytics and ad platforms like Meta and LinkedIn, builds end-to-end funnel analytics and comes with an AI assistant that reads the numbers for you. Running at client companies and in our own team.
RAG development is building an artificial intelligence system that responds to inquiries using information collected from your firm’s internal sources. The name “RAG” stands for “retrieval-augmented generation”: first, the system collects relevant snippets from related documents and tools; then, the language model uses those snippets to produce a response based on that information. The development process includes data preprocessing, document parsing, indexing, search, response generation, interface development, and integration. The result is an assistance tool that answers inquiries using your company's knowledge.
The price of RAG application development services will change based on the quantity and types of source documents, the quality of the documents, integrations, interfaces, and the number of queries that will be performed. Generally, scanned documentation, a large number of users, or high latency tend to increase system cost. For example, a retrieval-augmented generation pilot project involving a single use case, several curated sources, and one interface would cost between $15,000 and $30,000.
Delivery of RAG system development time depends on the amount of material to combine, the quality of the documents involved, and the effort required to secure the required permissions and integrations. A compact pilot project, involving a small amount of material in an organized format, can be developed much faster than a project that includes multiple teams, scanned material, and custom interfaces. For instance, Braintix developed its AI Knowledge Agent project in 2-4 weeks.
Solutions proposed by RAG and fine-tuning address different challenges; therefore, selecting the right alternative would depend on what is anticipated from the model. RAG is most suitable when responses should reflect the business’s current state, change often, or demand references. Tuning can be used to adjust the model’s behavior, such as its tone, response format, or its approach to performing a specific task; however, this process does not allow for updating facts. For updates, the RAG system requires indexing new documents, whereas a tuned model requires retraining.
Which vector data store to pick depends on your data volume, query rate, hosting requirements, and other systems you’ve already rolled out. If your team is already using PostgreSQL, pgvector can be a good starting point. Still, for more complex, use-specific cases, specialized databases like Qdrant, Weaviate, Milvus, and Pinecone may be better. Another factor to consider is the delivery method, as some solutions may be deployed on-premises, while others are cloud-based.
Data security in RAG depends on the architecture chosen, not the implementation method. These include content filtering based on the user’s permissions before the data is fed into the model, data encryption, access control on the document, and limiting access to model data by the model provider. You have full ownership of the documents when you host the architecture on your servers, as Braintix did with their AI agent. Treat input content as data, not prompts, to avoid prompt generation from the documents.
The RAG model can be easily integrated into any application or document, as long as it can be integrated via an API or by exporting data. Answer sources include Google Drive, Notion, email, CRM, finance tools, enterprise databases, and documentation websites. The answer can be delivered to any platform your team uses —be it Slack, Telegram, WhatsApp, or a web application.