RAG
Development Services
Build RAG applications that connect language models with your business data and deliver accurate, grounded answers.
Our RAG development services cover data ingestion, document processing, vector search, hybrid retrieval, reranking, enterprise integrations, evaluation and production deployment.
Proven AI Delivery
Trusted by Fortune Global 500 leaders, governments & top universities across 30+ countries
RAG Development
Services
Build Retrieval Augmented Generation systems around your business data, workflows and existing applications.
Build
Custom RAG Development
Build custom RAG solutions that retrieve relevant information from documents, databases and enterprise systems before generating a response.
Applications
RAG Application Development
Develop knowledge assistants, enterprise search systems, document intelligence applications and business specific question answering platforms.
Integration
Enterprise RAG Integration
Connect RAG applications with CRM, ERP, knowledge platforms, databases, APIs and existing enterprise applications.
Interfaces
RAG API Development
Expose retrieval and generation capabilities through APIs for existing applications, platforms and workflows.
Tuning
RAG Optimization
Improve chunking, embeddings, retrieval, ranking, context construction, citations, response quality and latency in existing RAG systems.
Testing
RAG Evaluation
Measure whether the system retrieves the right evidence and whether generated responses remain grounded in that evidence.
For broader language model engineering, explore our LLM Development Services.
RAG Solutions
We Build
Production RAG applications shaped around how your teams actually search, read and act on business information.
Employees
Knowledge Assistants
Give employees natural language access to policies, manuals, procedures, product documentation and internal knowledge.
Discovery
Enterprise Search
Search business information using natural language while combining semantic retrieval with exact keyword matching.
Documents
Document Intelligence
Search, compare, summarize and extract information from contracts, reports, manuals and large document collections.
Service
Support Copilots
Help support teams retrieve product information, procedures and previous resolutions while handling customer requests.
Analysis
Research Assistants
Search approved sources, compare documents and summarize findings with references to supporting information.
Departments
Knowledge Portals
Create searchable internal knowledge environments while maintaining user permissions and source access controls.
What RAG
Can Improve
Retrieval changes what the model has in front of it at the moment it answers, which is where most of the quality in a production assistant comes from.
Speed
Faster Knowledge Access
Help users find relevant information across documents and business systems without manually searching multiple repositories.
Accuracy
Grounded Responses
Give language models relevant business information before they generate an answer.
Verification
Source Traceability
Connect responses with supporting documents or source pages when users need to verify information.
Freshness
Current Knowledge
Update the retrieval layer as business information changes without retraining the language model each time.
Connect
Business Data
Build RAG systems around the information your organization already uses.
Files
Documents
PDFs, Word files, spreadsheets, reports, policies and manuals.
Structured
Databases
Relational databases, NoSQL systems and structured business information.
Platforms
Enterprise Platforms
CRM, ERP, support systems and operational applications.
Knowledge
Knowledge Sources
Wikis, documentation portals and internal knowledge repositories.
Services
APIs
Internal services and approved third party data sources.
Sources
Web Content
Selected public or private web content where appropriate.
For broader artificial intelligence capabilities, explore our AI Development Services.
RAG
Architecture
A reliable RAG system needs more than a language model and vector database.
Stage 01
Data Ingestion
Collect information from approved business sources and keep the knowledge layer synchronized.
Stage 02
Document Processing
Clean, structure and divide content into useful retrieval units while preserving metadata and source information.
Stage 03
Embeddings
Convert content into representations that support semantic retrieval.
Stage 04
Vector Search
Retrieve information based on meaning and context.
Stage 05
Hybrid Search
Combine semantic retrieval, keyword search and metadata filters when exact terms also matter.
Stage 06
Reranking
Prioritize the strongest retrieved evidence before passing context to the language model.
Stage 07
Context Building
Provide the model with the information required to answer without adding unnecessary content.
Stage 08
Grounded Responses
Generate responses using retrieved evidence and include supporting source references where required.
Learn more in our Retrieval Augmented Generation guide.
Vector and
Hybrid Search
Retrieval quality directly affects the information available to the language model. We design search around the type of information your RAG application needs to find.
Meaning
Semantic Search
Find relevant information even when the user's wording differs from the source content.
Exact Match
Keyword Search
Retrieve exact product names, technical terms, codes, regulations and identifiers.
Scoping
Metadata Filtering
Filter information by department, date, document type, customer, location, project or other business attributes.
Combined
Hybrid Retrieval
Combine semantic and keyword search when neither method provides enough precision on its own.
Explore our guide to Vector Databases for AI.
Evaluate
RAG Quality
A fluent answer does not automatically mean a RAG system works correctly. We evaluate retrieval and generation separately.
Retrieval
Retrieval Quality
Check whether the system finds the information required to answer the question.
Grounding
Response Grounding
Measure whether generated claims are supported by retrieved information.
Citations
Citation Accuracy
Verify whether citations support the statements connected to them.
Usefulness
Answer Quality
Evaluate relevance, completeness and usefulness.
Restraint
No Answer Handling
Test whether the system avoids unsupported responses when reliable information is unavailable.
Regression
Regression Testing
Compare performance after changes to models, embeddings, prompts, retrieval settings or knowledge sources.
Explore our RAG Evaluation Framework for a deeper approach to retrieval and response evaluation.
Secure
RAG Systems
Enterprise RAG applications need to protect the information behind every response.
Permissions
Role Based Access
Restrict information according to users, groups and application roles.
Governance
Source Permissions
Apply source access rules before retrieved content reaches the language model.
Separation
Data Isolation
Keep customer, department, project or business information separated where required.
Traceability
Audit Logging
Record relevant application, retrieval and administrative activity.
Hosting
Private Deployment
Deploy RAG applications within suitable cloud, private or hybrid environments based on business requirements.
RAG Solutions
by Industry
The retrieval layer changes shape with the regulations, document types and access rules of each sector.
Regulated
Healthcare
Search approved policies, operational information, research and documentation with controlled information access.
Controlled
Financial Services
Connect teams with policies, procedures, product information and controlled internal knowledge.
Documents
Legal
Search contracts, policies, precedents and large document collections with supporting source references.
Operations
Logistics
Retrieve procedures, shipment documentation, operating manuals and internal knowledge through natural language search.
Technical
Manufacturing
Make technical documentation, maintenance information and standard operating procedures easier to retrieve.
Cross functional
Enterprise Operations
Connect employees with approved knowledge across departments, applications and business systems.
RAG Technology
Stack
We work with established technologies across the retrieval and application stack.
Commercial and open models selected per workload
Retrieval orchestration built on proven frameworks
Embedding storage and similarity retrieval
Keyword, filtered and hybrid retrieval
Application, service and integration layers
Cloud, container and orchestration platforms
Retrieval Systems,
Shipped to Production.
Five SDLC Corp products and engagements where retrieval decides the answer. Scroll to deal each card onto the table.
in review
Website Grounded RAG Assistant
A retrieval assistant answering only from approved easepetvet.com content. OpenAI embeddings, PostgreSQL with pgvector and semantic retrieval ground every response in source material, with the index refreshed as the site changes.
- Site-grounded answers
- pgvector retrieval
- 100% emails verified
- 10–12 week build
Website-grounded RAG assistant using approved Ease Pet Vet content
trust pipeline
Governed Retrieval for Regulated Environments
Convera runs governed RAG over your own document set inside a six-stage trust pipeline, then checks citations before the answer leaves the boundary. Deployable on premise, in sovereign cloud or fully air gapped, with open weight models running offline.
- Sovereign knowledge base
- Citation check stage
- 50+ languages
- On premise, sovereign cloud, air gapped
Convera returns cited answers from governed enterprise knowledge
resolved autonomously
RAG Over Internal Documentation
A generative AI assistant integrated with internal documentation, CRM and the ticketing system. GPT-4o with Retrieval Augmented Generation resolved 68% of employee queries autonomously and reduced support costs by 50%.
- GPT-4o
- Pinecone
- LangChain
- Guardrails.ai
- −50% support cost
Enterprise knowledge assistant connected with internal documentation and support systems
tickets automated
Retrieval Across ServiceNow, Slack and the Knowledge Base
A GPT-4o enterprise assistant built with LangChain and connected to ServiceNow, Slack and the internal knowledge base through RAG architecture, handling IT queries, HR policy questions and system access requests.
- 45 min → 4 sec response
- GPT-4o
- LangChain
- ServiceNow
- Slack
only
Retrieval Inside a Live Phone Call
A voice agent that understands callers in natural speech, classifies each call by intent and answers from the client's approved knowledge base. Anything it should not answer is routed on with the transcript and summary attached.
- Approved documents only
- Intent classification
- Transcript + summary on transfer
- Keeps your existing number
Real Stories.
Real Impact.
Founders, CEOs, and operating leaders share what it's like to build with SDLC Corp.
Eric Leist
CEO, Edgerton Strategies
Doug Schmidt
CEO, Roofaid USA
Reyzal Razmi
All Star Influencers
They approached our Salesforce discovery with real technical depth, uncovered structural gaps others missed, and delivered a solution that worked exactly as promised.
SDLC CORP built a mobile application that met our strategic requirements with strong technical execution. The solution performs reliably and has become an important operational asset.
They saw inefficiencies in our Salesforce workflow and redesigned our entire quote-to-cash system. We now operate faster, cleaner, and with better accuracy.
From planning to post-launch, SDLC Corp guided us every step of the way. Their support makes them more than a vendor. They're a trusted partner.
The SDLC Corp team scaled our platform with impressive technical expertise, ensuring it's secure, robust, and ready for future growth.
The SDLC Corp team scaled our platform with impressive technical expertise, ensuring it's secure, robust, and ready for future growth.
From planning to post-launch, SDLC Corp guided us every step of the way. Their support makes them more than a vendor. They're a trusted partner.
They saw inefficiencies in our Salesforce workflow and redesigned our entire quote-to-cash system. We now operate faster, cleaner, and with better accuracy.
SDLC CORP built a mobile application that met our strategic requirements with strong technical execution. The solution performs reliably and has become an important operational asset.
They approached our Salesforce discovery with real technical depth, uncovered structural gaps others missed, and delivered a solution that worked exactly as promised.
10+ Years of
Experience.
Ten chapters from inside SDLC Corp, engineered by 1,200+ engineers across six continents.
Our RAG
Process
Five stages from use case definition to a measured RAG application running in production.
Discover
Define users, business questions, information sources, integrations and expected outcomes.
Design
Create the ingestion, retrieval, generation, security and application architecture.
Build
Develop data pipelines, retrieval services, application interfaces and system integrations.
Evaluate
Test retrieval quality, grounding, citations, latency and representative business scenarios.
Deploy
Launch the RAG application in the required environment and monitor production performance.
Improve
Existing RAG
Already have a RAG system that does not perform as expected? We evaluate the complete retrieval pipeline to identify where quality is being lost.
Where retrieval quality is usually lost
We identify the underlying problem before replacing components.
Why Choose
SDLC Corp
One engineering partner for the full RAG lifecycle, from the retrieval layer through production operation.
End to End
Complete RAG Engineering
Build the full system from information ingestion and retrieval through generation, integration and deployment.
Systems
Enterprise Integration
Connect RAG applications with existing databases, APIs, business applications and enterprise platforms.
Retrieval First
Retrieval First Approach
Focus on the quality of information reaching the model instead of relying only on prompt changes.
Model Choice
Flexible Model Support
Use suitable commercial or open language models based on quality, privacy, latency and infrastructure requirements.
Measured
Built In Evaluation
Measure retrieval, grounding and response quality throughout development.
RAG
Resources
Deeper reading on the retrieval layer, the storage layer and how to tell whether a RAG system is actually working.
ExplainerRetrieval Augmented Generation
Learn how retrieval connects language models with external knowledge and enterprise information.
Read the RAG Guide
ArchitectureVector Databases for AI
Understand how embeddings and vector databases support semantic search and enterprise retrieval.
Read the Vector Database Guide
FrameworkRAG Evaluation Framework
Learn how to measure retrieval quality, response grounding, citation accuracy and human evaluation.
Explore the RAG Evaluation Framework
GuideHow to Evaluate RAG Systems
Explore practical methods for testing retrieval quality and generated responses.
Read the RAG Evaluation GuideBuild Your
RAG Solution
Connect your documents, enterprise systems and business knowledge with a RAG application built for real users and production workflows.
From enterprise search and knowledge assistants to document intelligence and custom retrieval systems, our RAG development team can take your project from architecture through deployment.
Contact Us
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Let's Talk About Your Project
- Free Consultation
- 24/7 Experts Support
- On-Time Delivery
- sales@sdlccorp.com
- +1(510-630-6507)