10 RAG Development Companies for Financial Services in 2026

Financial institutions have a harder RAG problem than most businesses.

A general enterprise assistant may only need to find an HR policy or product manual. A financial-services RAG system may need to retrieve the correct version of a lending policy, identify clauses across regulatory documents, interpret financial tables, respect customer-level permissions, provide source citations, and escalate an answer rather than guessing when evidence is insufficient.

That combination of accuracy, traceability, security, and governance makes the development partner important.

For U.S. broker-dealers, using generative AI does not replace existing regulatory responsibilities. FINRA has specifically reminded member firms that its rules continue to apply when GenAI is used, including when the technology comes from third-party providers. It highlights governance, model risk, privacy, data integrity, reliability, and accuracy among the issues firms should consider.

The companies below bring different strengths to that challenge, from enterprise banking transformation and regulatory knowledge systems to multimodal financial-document retrieval and focused RAG engineering.

Financial Services RAG Companies at a Glance

1. EPAM

Best for: Large financial institutions with complex regulatory and technology environments

EPAM is a strong option when RAG is one part of a broader financial-services AI transformation rather than a standalone chatbot.

Its financial-services AI architecture work spans retail banking, payments, capital markets, and insurance. Current EPAM roles for its financial-services practice specifically describe building GenAI and agentic systems using RAG, vector stores, and orchestration for use cases including fraud detection, credit risk, and regulatory reporting. They also reference financial-sector requirements including MiFID II, Basel-related regulation, payments standards, data protection, and operational resilience.

That breadth matters for banks whose RAG application must interact with systems far beyond a document repository.

A compliance assistant, for example, could require access to policy libraries, regulatory publications, case-management systems, employee permissions, transaction information, and audit infrastructure.

Why EPAM stands out

EPAM is particularly suitable for:

• enterprise banking knowledge platforms
• regulatory and compliance assistants
• credit-risk workflows
• fraud investigation support
• insurance operations
• agentic AI integrated with RAG
• large cloud and data modernization programs

Its scale may be unnecessary for a small, narrowly scoped proof of concept, but it becomes an advantage where the implementation touches multiple business units and legacy systems.

2. Tredence

Best for: Banks and financial institutions where analytics and data infrastructure are central to the RAG project

Tredence combines banking and financial-services expertise with data engineering, analytics, machine learning, and generative AI.

Its financial-services GenAI offering includes question-answering over investment and financial documents, regulatory-policy interpretation, underwriting copilots, disclosure review, complaint summarization, and other finance-specific applications. Tredence also describes using context-driven RAG, vector databases, and prebuilt connectors as part of its financial-services GenAI architecture.

This makes it particularly interesting for financial institutions where the RAG layer needs to sit on top of a substantial analytics or enterprise-data environment.

Why Tredence stands out

Consider Tredence for:

• regulatory knowledge assistants
• financial-report analysis
• investment-document retrieval
• underwriting copilots
• disclosure-review workflows
• enterprise analytics connected to GenAI

Its data-focused background can be useful when the biggest challenge is not selecting an LLM but organizing the financial information that the model needs to retrieve.

3. SoftServe

Best for: Financial documents containing complex tables, charts, images, and text

Many important financial documents are not clean blocks of prose.

Annual reports contain tables. Loan packages contain forms and scanned documents. Investment reports combine charts and commentary. Insurance files can contain images alongside structured and unstructured information.

SoftServe's multimodal RAG offering is designed to retrieve and reason across text, images, and tables instead of processing each modality independently. The company explicitly positions the technology for financial applications and also maintains a dedicated financial-services GenAI practice.

This approach can be more useful than text-only RAG when critical evidence is buried inside complex document layouts.

Why SoftServe stands out

SoftServe is worth considering for:

• financial statement analysis
• document-heavy banking workflows
• insurance document processing
• multimodal investment research
• regulatory document intelligence
• enterprise financial knowledge systems

The important evaluation question is whether multimodal retrieval actually improves performance on your own documents. Buyers should test tables, charts, footnotes, scanned files, and unusual report layouts rather than relying only on simple PDF demonstrations.

4. Globant

Best for: Banks combining RAG with broader digital and customer-experience modernization

Globant has explicitly explored RAG architecture for banking and financial-services environments.

Its banking guidance describes using RAG to connect LLM applications with proprietary information such as product manuals, FAQs, research reports, customer-service materials, and risk-document repositories. Globant highlights applications across onboarding, customer service, underwriting, transaction processing, and other banking workflows.

The company also emphasizes an important practical point: a RAG architecture is not static. Retrieval quality can deteriorate as documents, data sources, and user behavior change, so production systems require ongoing maintenance.

Why Globant stands out

Globant can be a good candidate for:

• digital banking assistants
• customer-service intelligence
• underwriting workflows
• internal banker copilots
• banking knowledge management
• RAG as part of larger digital transformation

Its broader product and experience-design capabilities may appeal to institutions building customer-facing applications rather than only internal AI tools.

5. Zymr

Best for: Financial institutions that want RAG tightly integrated with governed operational workflows

Zymr's financial AI practice is unusually specific about how retrieval should operate inside regulated environments.

Its finance offering describes hybrid retrieval across structured data, documents, metadata, and knowledge graphs, with reranking and entitlement filtering before information reaches the model. It also emphasizes source traceability, human checkpoints, secure APIs, and controlled agentic workflows.

Those capabilities are particularly relevant in financial services because retrieving the semantically closest passage is not enough. The passage also needs to be authorized, current, and appropriate for the workflow.

Why Zymr stands out

Potential applications include:

• credit underwriting assistants
• policy and product knowledge systems
• financial operations agents
• regulatory reporting support
• customer-service intelligence
• document and transaction knowledge retrieval

Zymr is especially relevant when structured financial systems and unstructured documents need to participate in the same retrieval process.

6. LeewayHertz

Best for: Fintech companies building custom RAG, agentic AI, and workflow automation together

LeewayHertz provides both generative AI development and specialized fintech AI services.

Its financial-services capabilities cover areas such as lending, credit operations, financial analysis, KYC, compliance, regulatory reporting, accounting, treasury, customer service, and exception management. Its broader GenAI practice includes enterprise data integration and retrieval-based architectures alongside custom models and AI agents.

This combination becomes useful when the intended application needs to take action rather than simply answer questions.

For example, a compliance workflow could retrieve relevant requirements, extract information from a case, prepare a structured recommendation, route it to a reviewer, and update an internal system after approval.

Why LeewayHertz stands out

It is a good fit for:

• fintech product development
• KYC and compliance workflows
• lending assistants
• financial research applications
• agentic RAG
• financial operations automation
• AI integrated with existing enterprise software

Organizations considering agentic workflows should pay particular attention to approval boundaries and tool permissions. Giving an AI system the ability to act introduces additional risks beyond ordinary question answering.

7. Addepto

Best for: Financial organizations that need strong data engineering around the RAG system

A retrieval system is only as reliable as the information architecture underneath it.

Addepto combines large-language-model development, RAG, machine learning, analytics, and data engineering. Its finance and insurance practice includes RAG-generated assistance for customer-service workflows, AI-supported regulatory reporting, model tracking, explainability, and traceability.

Its broader LLM offering explicitly includes retrieval-augmented generation and integration with enterprise applications and data systems.

This makes Addepto particularly relevant when an institution first needs to make fragmented enterprise data usable for AI.

Why Addepto stands out

Consider Addepto for:

• fragmented financial data estates
• finance and insurance AI systems
• compliance knowledge assistants
• RAG evaluation
• data preparation for GenAI
• document and analytics integration

This data-first approach is valuable because changing the language model rarely fixes poor metadata, duplicated documents, broken permissions, or conflicting versions of the same policy.

8. Bacancy Technology

Best for: Financial organizations seeking a focused end-to-end RAG development team

Bacancy offers dedicated RAG development covering architecture, retrieval, LLM integration, optimization, deployment, and managed RAG operations.

Its banking use cases include retrieval from regulatory information, internal product documentation, and customer records with source traceability. The company says its RAG work spans banking, finance, insurance, healthcare, and other industries.

That makes it a more specialized alternative to larger consulting organizations for institutions that already understand the use case and primarily need engineering execution.

Why Bacancy stands out

Good potential fits include:

• compliance document retrieval
• banking knowledge assistants
• customer-support copilots
• financial policy search
• audit-support systems
• RAG optimization and maintenance

Buyers should still ask to see evaluation methodology rather than relying on generic accuracy claims. A useful demonstration should show how the system performs on difficult, ambiguous, outdated, and permission-sensitive queries.

9. SoluLab

Best for: Fintech companies that need both the RAG backend and the user-facing application

SoluLab provides dedicated RAG application development alongside broader AI and software engineering.

Its RAG services include enterprise retrieval architecture, integrations, data ingestion, model orchestration, and application development. Its portfolio also includes fintech-related work, making it a potential fit for companies that need to turn retrieval into a complete product rather than an isolated AI service.

This is particularly relevant to startups and mid-market firms that may not already have separate frontend, backend, AI, cloud, and DevOps teams.

Why SoluLab stands out

Potential projects include:

• fintech customer assistants
• financial document Q&A
• internal knowledge platforms
• RAG-enabled SaaS products
• compliance applications
• AI agents connected to enterprise systems

For financial-services projects, the key due-diligence step is verifying that the proposed security, audit, identity, and governance architecture matches your actual regulatory obligations.

10. ScalaCode

Best for: Focused financial RAG systems involving compliance and document intelligence

ScalaCode offers RAG development specifically for financial services and banking.

Its stated financial applications include searching large collections of market information, answering compliance-related queries, generating credit-memo support from filings, and assisting with internal audit workflows. Its architecture references hybrid retrieval, metadata filtering, and source citation.

Those are useful capabilities for a sector where an answer without evidence may have limited practical value.

Why ScalaCode stands out

It may suit organizations looking for:

• compliance-policy assistants
• financial filing analysis
• market-research retrieval
• audit knowledge systems
• credit-document intelligence
• relatively focused RAG engagements

As with any provider making industry-specific claims, request examples that closely match your data volume, permission model, and regulatory environment before making a selection.

What Makes Financial-Services RAG Different?

A proof-of-concept RAG chatbot can often be assembled quickly.

Building one that a bank, investment firm, lender, or insurer can safely rely on is a different problem.

Retrieval must respect entitlements

A model should not be able to retrieve information simply because the user entered a semantically relevant query.

Financial organizations often need access controls based on:

• customer or account
• business unit
• employee role
• geography
• legal entity
• information classification
• product
• transaction
• regulatory jurisdiction

Those permissions should normally be enforced during retrieval, not applied only after sensitive information has already entered the model context.

Source citations matter more than fluent answers

RAG outputs can sound convincing even when retrieval has failed.

For financial workflows, users often need to inspect the policy, filing, research note, transaction record, contract, or regulatory document supporting an answer.

This is especially important for compliance, risk, investment research, underwriting, and audit applications.

Temporal accuracy is critical

Financial information changes continuously.

Consider the difference between retrieving:

• the current lending policy and last year's policy,
• a superseded regulatory procedure and the active one,
• the latest earnings filing and an older quarter,
• a current product rate and an expired rate.

Both documents may be semantically relevant. Only one may be appropriate.

Version control, effective dates, metadata, and source prioritization therefore become part of retrieval quality.

Structured and unstructured data often need to work together

Financial institutions do not operate entirely through PDFs.

Information can live in:

• relational databases
• data warehouses
• transaction platforms
• CRM systems
• market-data feeds
• spreadsheets
• contracts
• reports
• email
• regulatory filings
• internal knowledge portals

A mature architecture may therefore require database querying, APIs, knowledge graphs, semantic retrieval, keyword search, and vector search rather than a single vector database.

An AI answer may require human approval

Not every financial workflow should be automated end to end.

FINRA's 2026 discussion of GenAI specifically highlights issues such as monitoring system access and data handling, tracking AI actions, establishing guardrails, and identifying where human-in-the-loop oversight is appropriate.

For high-impact decisions, the system should often prepare evidence for a qualified person rather than becoming the decision-maker.

Common RAG Use Cases in Financial Services

RAG is most useful when employees repeatedly need information that is spread across large, changing bodies of institutional knowledge.

Compliance and regulatory research

A compliance professional could ask a question in natural language and receive an answer grounded in approved internal policies and regulatory materials, with citations to the underlying documents.

Investment and equity research

Analysts can search earnings reports, transcripts, filings, internal research, and market commentary through a unified interface.

RAG should support the research process rather than present generated investment conclusions as unquestionable facts.

Credit and underwriting support

A system can retrieve lending policies, borrower documents, exception histories, underwriting guidelines, and relevant internal information to help an underwriter prepare a case.

Customer-service copilots

Agents can obtain answers from approved product documentation, fees, procedures, account policies, and customer-specific information while maintaining access controls.

Internal policy search

Employees at large institutions often spend considerable time searching fragmented policy repositories.

A permission-aware RAG assistant can provide a single conversational interface across these systems.

Audit and investigation support

RAG can help teams locate policies, case histories, documents, correspondence, and relevant evidence without manually searching multiple repositories.

How to Evaluate a RAG Development Company for Financial Services

A strong demo should not be enough to win the project.

Ask vendors to prove how the underlying retrieval system behaves.

1. Ask how they measure retrieval quality

The vendor should be able to test questions such as:

• Did the correct document appear?
• Was the correct passage ranked first?
• Were irrelevant passages excluded?
• Did retrieval respect permissions?
• Were citations accurate?
• Did the system use the latest applicable version?

Separate retrieval evaluation from generated-answer evaluation.

Otherwise, it becomes difficult to determine whether a wrong answer came from bad search or bad generation.

2. Test difficult queries, not only easy ones

Your evaluation dataset should include:

• ambiguous questions
• similar policies with different effective dates
• conflicting documents
• information the user cannot access
• questions with no valid answer
• multi-document questions
• numerical tables
• abbreviations and internal terminology

A reliable system should sometimes refuse to answer.

3. Ask how authorization works

Find out whether document permissions are preserved when information is indexed.

If a user loses access to a document in the source platform, determine how quickly the RAG index recognizes the change.

4. Evaluate source freshness

Ask what happens when a regulation, policy, prospectus, product document, or research report changes.

A good architecture should define:

• indexing frequency
• change detection
• document versioning
• deletion handling
• metadata updates
• rollback procedures

5. Review vendor and model dependencies

Understand every third party that may touch your data.

That can include:

• model providers
• vector databases
• cloud providers
• observability platforms
• document-processing services
• embedding providers
• external APIs

The financial institution remains responsible for understanding how the overall system is being used within its regulatory environment.

FINRA explicitly notes that obligations continue to apply whether a member develops its own GenAI system or relies on third-party technology.

6. Require an evaluation and monitoring plan

AI quality can change after launch as the underlying documents, models, embedding systems, user behavior, and retrieval configurations evolve.

Look for continuous testing covering:

• groundedness
• retrieval precision
• citation accuracy
• latency
• failure rates
• security events
• prompt injection
• data leakage
• access-control failures
• model or retrieval regressions

NIST's Generative AI Profile provides a broader voluntary framework for identifying and managing generative-AI risks across the lifecycle and can serve as one useful reference when designing governance and evaluation processes.

Which Company Should You Choose?

The best partner depends more on the intended financial workflow than on company size.

Choose EPAM if you are a large institution dealing with complex banking systems, regulatory architecture, and enterprise-scale transformation.

Consider Tredence if the project is tightly connected to analytics, enterprise data, underwriting, or financial-report intelligence.

SoftServe is especially relevant when tables, charts, scanned records, and other multimodal documents are important.

Globant fits well when banking RAG is part of a broader customer-experience or digital modernization program.

Zymr deserves consideration when entitlement-aware hybrid retrieval and governed financial workflows are central requirements.

LeewayHertz can fit fintech organizations combining RAG with agents and workflow automation.

Addepto is particularly useful where data preparation and enterprise information architecture need substantial work before retrieval can perform reliably.

Bacancy, SoluLab, and ScalaCode provide alternatives for more focused custom development engagements where a large global systems integrator may be unnecessary.

The strongest vendor will not simply ask which model you want to use. It should first understand the workflow, failure risk, data sources, authorization requirements, evidence standards, human-review points, and criteria that will determine whether the system is safe enough to deploy.