RAG Development Companies: How to Choose a Partner for Your AI Knowledge System

RAG development companies to consider for an AI knowledge system include Lexis Solutions, Scalefocus, and Accedia. Each publishes retrieval-augmented generation capabilities. The right partner depends on your information sources, access rules, integration needs, and how you will measure useful answers.

An internal knowledge assistant needs to find the right evidence before it can answer well. It also needs to distinguish current information from obsolete versions, respect permissions, and recognise when the available sources cannot answer a question.

This guide introduces four companies with Bulgarian engineering operations and explains how to compare their proposed approach to your knowledge system.

Published by Lexis Solutions, which appears first in this shortlist. Company information and technical sources were reviewed on 11 October 2026. Listing order is an editorial choice, not an independently measured ranking. Suggested project fit reflects our interpretation of the linked service descriptions.

What does a RAG development company do?

RAG stands for retrieval-augmented generation. A RAG application retrieves relevant information from selected sources and supplies it to a language model as context for generating an answer. Microsoft's RAG overview describes this pattern and its role in connecting generative AI to business content.

For a business knowledge system, the delivery work can include:

• Connecting approved document repositories, knowledge bases, or other sources.
• Extracting content and preserving useful structure and metadata.
• Building search and ranking so relevant evidence reaches the model.
• Applying access rules to retrieved information.
• Producing answers with traceable supporting sources.
• Evaluating performance and maintaining the system as information changes.

Common use cases include support knowledge assistants, internal policy search, product documentation, and onboarding guidance. Start with a defined audience and a set of questions people regularly need answered.

Four RAG development companies to consider

We selected companies that explicitly describe RAG or retrieval-augmented generation in their own service materials. The list combines AI application development with broader software, data, and enterprise engineering capabilities. It is a starting shortlist rather than an exhaustive directory.

1. Lexis Solutions

Consider for: custom AI knowledge systems that need data integration and a usable product interface.

Lexis Solutions is based in Sofia, Bulgaria. Our AI development services explicitly include RAG knowledge systems, semantic search, AI-powered internal tools, and intelligent data pipelines. We also build the application interfaces and agentic workflows around those capabilities.

That combination is relevant when the knowledge assistant needs to become part of an existing product or an internal process. The scope includes how information reaches the system and how employees use and check its answers.

What to ask us: assess your source documents and representative questions, propose an initial retrieval approach, and define the evidence needed to approve a release. Discuss permissions, source updates, evaluation, and handoff alongside the interface.

2. Scalefocus

Consider for: enterprise knowledge systems where governance and deployment requirements shape the architecture.

Scalefocus documents delivery operations in Bulgaria. Its AI agent offering describes retrieval-augmented generation over proprietary data, source attribution, and visibility into agent operations. Deployment can use existing infrastructure or its AION platform.

What to ask the team: demonstrate how sources, permissions, and operational records are managed. Clarify which capabilities use your existing infrastructure and which introduce platform dependencies or additional operating costs.

3. Accedia

Consider for: RAG applications that also need data engineering, cloud integration, and AI operations.

Accedia describes itself as an AI and custom software company headquartered in Sofia. Its AI services explicitly include custom RAG systems, generative AI, validation, production deployment, and ongoing AI operations.

What to ask the team: show how retrieval and answer quality are evaluated separately, identify the specialists assigned to your project, and explain who maintains the data pipeline and monitors changes after launch.

How to choose a RAG development partner

Give every shortlisted company the same sample documents, representative questions, and access requirements. The discussion should establish how the proposed system will work on your information, including cases where it should ask for clarification or decline to answer.

1. Check how the team prepares your source material

Source preparation affects what the system can retrieve. Ask how the team handles scanned pages, tables, headings, duplicate files, and documents with conflicting versions.

When documents are split into smaller passages, important context can be lost. A passage mentioning a deadline may be misleading if the section specifying the relevant product or country is separated from it. Anthropic's contextual retrieval article discusses this problem and ways of preserving context around retrieved passages.

Ask the team to show the extracted content from a difficult document. Verify that its structure, identifiers, and source location are usable before evaluating the generated answer.

2. Ask why the proposed retrieval strategy fits your questions

Semantic search helps find passages with related meaning. Keyword search can help with exact names, codes, and identifiers. Hybrid search combines these approaches, while reranking reassesses the relevance of initial results.

Microsoft's RAG information-retrieval guide explains retrieval choices and how to evaluate search results. A partner should test those choices against your questions rather than assume one strategy will fit every source.

For example, an employee asking about “replacement parts for pump model P-204” needs the correct model's documentation. Similar descriptions of another pump may be irrelevant. Ask to inspect the retrieved passages and their ranking, as well as the final answer.

3. Make permissions part of the retrieval design

Specify which users may access each source and how those permissions reach the knowledge system. Access controls need to apply before restricted content is supplied to the model.

Microsoft's Azure AI Search security guidance describes capturing permission metadata during indexing and enforcing document-level access at query time. Other architectures need an equivalent, tested approach appropriate to their stack.

Have the partner demonstrate the same question under two user roles. A user without permission should not receive the restricted document, its contents in an answer, or a cached response generated for someone with broader access.

Also test permission changes. Ask what happens when an employee changes departments or a document's access is revoked.

4. Evaluate retrieval, answers, and citations separately

A system can find the right passage and still summarise it incorrectly. It can also produce a plausible answer using the wrong document. Evaluation needs to distinguish these failures.

Microsoft's RAG evaluation guidance separates retrieval assessment from answer groundedness, relevance, and completeness. Apply those distinctions to your use case:

• Retrieval: did the system find the evidence needed for the question?
• Groundedness: are the answer's claims supported by that evidence?
• Relevance: does the answer address the user's request?
• Completeness: does it include the necessary information and qualifications?

Check citations directly. A working link is useful only if the cited passage supports the associated claim. Ask the partner to retain source identifiers and locations so reviewers can verify that relationship.

Agree on representative test questions before tuning begins. Include cases with no answer in the approved sources, ambiguous wording, outdated versions, and restricted information. Keep separate cases for final assessment, and use human review to check automated scoring.

5. Plan source updates, removals, and conflicting information

Name who owns each source and define how quickly updates should become available. Ask how the system identifies superseded documents and how it handles disagreements between sources.

A useful acceptance exercise is to change a source document and repeat the affected questions. Then withdraw a document and check the index, retrieved passages, answers, citations, and caches that might still expose its content. The system should follow your agreed update and removal policy.

For conflicts, the application may need to favour a designated source, show the disagreement, or escalate to an owner. Decide that behaviour with the business team rather than leaving the choice implicit.

6. Review security, operations, and ownership

Retrieved content can contain instructions intended to influence the model. OWASP's prompt injection guidance explains why RAG does not fully mitigate this risk and recommends controls such as separating untrusted content, restricting privileges, and adversarial testing.

For a knowledge assistant, define how content is handled and which tools it can use. If later versions add actions, assess their permissions and approval requirements as a separate scope.

The delivery proposal should also identify hosting, monitoring, support, and change responsibilities. Agree which assets your team receives: source code, ingestion configuration, evaluation datasets, prompts, infrastructure configuration, and operating documentation.

Ask how those assets can be used if you change model providers, search platforms, or delivery partners. Understand what rebuilding an index or changing an embedding model would involve.

What should a RAG proof of concept demonstrate?

A useful proof of concept tests assumptions that affect the production decision. Choose a bounded question set and a representative mix of permitted documents, including difficult examples.

Consider a hypothetical support knowledge system containing product manuals, approved troubleshooting notes, and current service policies. A buyer could request these demonstrations:

1. An exact-reference question: retrieve the correct manual for a specific product code.
2. A differently worded question: find relevant guidance when the employee uses everyday language.
3. A multi-source question: combine relevant facts while preserving their individual citations.
4. A missing-answer question: explain that the approved sources are insufficient and offer an appropriate next step.
5. A restricted-access question: enforce different permissions for different users.
6. An update test: reflect an amended policy and stop relying on the withdrawn version within the agreed interval.

This is an illustrative evaluation plan, not a claimed customer project. Define expected evidence and outcomes with the business owner. Record unresolved gaps and the work needed for production.

What affects the cost of RAG development?

Document volume is one input. Complexity also comes from parsing difficult files, connecting repositories, preserving permissions, supporting multiple languages, testing answer quality, and keeping sources current.

Ask suppliers to separate the build into discovery, ingestion, retrieval, application integration, evaluation, and deployment. Identify exclusions such as source cleanup or obtaining access to systems.

For ongoing operation, budget for search infrastructure, model usage, source processing, monitoring, and support. Compare latency and cost at an acceptable quality level on the same workload. A lower cost per answer is less useful if employees need substantial time to verify or correct it.

A proposal should explain what changes the estimate and who pays for work after handoff. Avoid comparing headline fees for systems with different source, permission, and maintenance requirements.

Questions to include in your supplier brief

Send shortlisted companies a brief that answers:

• Who will use the knowledge system, and which decisions will it support?
• Which sources are authoritative, and who maintains them?
• What document formats, languages, and source systems are involved?
• Which information is restricted, and how are users identified?
• What questions should the first release answer, clarify, or escalate?
• What counts as a correct answer and a valid supporting citation?
• How quickly must updates and removals take effect?
• Where will users access the application, and what support is required?

Ask each supplier to respond with an architecture explanation, a scoped validation plan, acceptance criteria, delivery responsibilities, and an operating-cost estimate. This gives you a comparable basis for selecting a partner.

Choose a partner around the evidence your users need

At Lexis Solutions, we build RAG knowledge systems and the data pipelines, interfaces, and workflows around them. Bring us a representative source set, the questions your team needs answered, and your access requirements.

We can assess the integration work, define an initial scope, and agree on how to evaluate retrieval, answers, and source maintenance. Explore our AI development services to discuss your knowledge system.