More grounded answers
The model responds from retrieved source material instead of relying only on general training knowledge. Citations let users inspect the evidence and decide whether the answer is sufficient.
Retrieval-Augmented Generation
RAG Development Services for Trusted AI Answers
Torch Solutions builds retrieval-augmented generation systems that connect language models to approved business knowledge with permissions, citations, evaluation, and production-grade search infrastructure.
What Is This Service?
Ground language models in the information your business trusts
Retrieval-augmented generation, or RAG, combines search with a large language model. When a user asks a question, the system retrieves relevant passages from approved sources and gives that context to the model before it responds. This helps the application answer with current, domain-specific information without retraining a model whenever documents change.
A reliable RAG system is not simply a vector database attached to a prompt. Document parsing, chunking, metadata, permissions, hybrid retrieval, ranking, context assembly, citations, query rewriting, evaluation, and feedback all influence whether users receive useful answers. Poor retrieval cannot be repaired by a more fluent model.
Torch Solutions builds RAG applications for enterprise knowledge, healthcare records, policies, product documentation, support content, reports, and document-heavy SaaS workflows. We connect the retrieval layer to secure web, mobile, API, and cloud systems so it becomes a maintained product capability.
We also design the operational workflow behind the knowledge base. Source owners need a clear way to understand what is indexed, when it was updated, which documents failed, and how deletion or permission changes propagate. Product teams need visibility into unanswered questions and weak citations. By treating ingestion, retrieval, generation, and feedback as separate observable stages, the team can diagnose quality problems without guessing whether the model, the search layer, or the underlying content caused the failure. That separation also makes improvements easier to prioritize and verify.
Business Benefits
Business value designed into the system
Current knowledge without retraining
Documents can be added, updated, removed, and re-indexed through a controlled pipeline. The knowledge base evolves without the cost and delay of model fine-tuning for every factual change.
Permission-aware knowledge access
Retrieval can apply tenant, role, team, patient, project, or document-level filters before context reaches the model. Users receive answers only from sources they are allowed to access.
Faster research and support
Employees and customers can ask natural-language questions across large collections, receive concise answers, and follow links back to the relevant material.
Measurable retrieval quality
Search relevance, answer faithfulness, citation accuracy, coverage, latency, and user feedback can be evaluated separately. Teams know whether failures begin in retrieval or generation.
Our Development Process
From use case to monitored production software
Knowledge and access discovery
We identify source systems, document formats, update frequency, permissions, user questions, and quality expectations. This establishes the ingestion and security design before a vector store is selected.
Ingestion and document preparation
Pipelines extract text and structure from documents, normalize metadata, remove noise, detect changes, and preserve source references. Chunking strategies are tested against the way users ask questions.
Retrieval architecture
We compare semantic, keyword, hybrid, metadata-filtered, and reranked search using Pinecone, Weaviate, ChromaDB, PostgreSQL pgvector, or another suitable platform. The choice reflects scale, permissions, operations, and cost.
Answer and citation design
Prompts instruct the model to use retrieved evidence, acknowledge missing support, return structured fields when needed, and cite exact sources. Interfaces make citations and document context easy to inspect.
Evaluation and adversarial testing
We test known-answer questions, ambiguous queries, conflicting sources, missing content, permission boundaries, and prompt injection. Retrieval and answer measures reveal where targeted improvements are needed.
Deployment and knowledge operations
Docker, FastAPI or Django, queues, PostgreSQL, Redis, and cloud monitoring support ingestion and query traffic. Operational tools expose indexing status, source failures, usage, feedback, and cost.
Technologies We Use
A production stack selected for your requirements
RAG architecture is selected around document volume, change frequency, metadata, permissions, latency, hosting, and relevance requirements. We can combine vector, keyword, and relational search rather than forcing every knowledge problem into one index.
- OpenAI
- Anthropic
- LangChain
- LlamaIndex
- Pinecone
- Weaviate
- ChromaDB
- PostgreSQL
- pgvector
- Python
- FastAPI
- Django
- Redis
- Docker
- AWS
- Azure
Industries We Serve
Applied to workflows where context matters
Healthcare
Permission-aware retrieval can help clinicians navigate selected patient records, medical reports, approved references, and operational policy while preserving source visibility.
Enterprise knowledge
Employees can search policies, procedures, product information, reports, and internal documentation through one grounded question-answering experience.
SaaS customer support
RAG can power tenant-aware help, product guidance, support drafting, and agent assistance using current documentation and account-safe context.
Construction and projects
Teams can find information across project documents, field notes, reports, specifications, and asset records without manually opening every file.
Regulated and professional services
Cited retrieval helps experts research large controlled collections while maintaining traceability and human responsibility for the final decision.
Why Torch Solutions
AI engineering grounded in product and operations
Retrieval quality comes first
We test whether the right evidence is found before tuning generation. This prevents teams from repeatedly changing prompts when the underlying search is the real problem.
Security follows source permissions
Access filtering is designed into ingestion and retrieval, not added after launch. Tenant and role boundaries remain part of every query path.
Complete ingestion engineering
We handle parsing, metadata, updates, deletion, re-indexing, failure recovery, and monitoring. Knowledge remains operationally maintainable as source systems change.
Product-ready delivery
RAG is integrated into usable web, mobile, SaaS, healthcare, or enterprise experiences with APIs, citations, feedback, analytics, and cloud operations.
Related Case Studies
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Combine this capability with the application, cloud, data, integration, and product engineering required to operate it reliably.
Frequently Asked Questions
Questions about rag development
What types of data can a RAG system use?
Common sources include PDFs, office documents, web pages, databases, support content, reports, transcripts, policies, and application records. Parsing and permissions vary by source and must be designed explicitly.
Does RAG eliminate hallucinations?
No. RAG improves grounding, but the model can still misread or overstate evidence. Strong retrieval, citations, answer constraints, evaluation, and human review remain important for sensitive use cases.
Which vector database should we use?
The choice depends on scale, filtering, hosting, operations, latency, and cost. Pinecone, Weaviate, ChromaDB, or PostgreSQL pgvector can each be appropriate; we evaluate requirements before choosing.
Can RAG enforce user permissions?
Yes. Metadata and access filters can restrict retrieval by tenant, role, project, patient, team, or document. The system should verify permissions before any source text is passed to the model.
How do you measure RAG quality?
We measure retrieval relevance and coverage, answer faithfulness, citation correctness, completeness, latency, and user feedback. A question set with expected sources provides a repeatable baseline.
How are updated documents handled?
The ingestion pipeline detects additions, changes, and deletion, then updates affected chunks and metadata. Monitoring exposes failures and stale sources so the index remains trustworthy.
Can RAG run in our cloud environment?
Yes. We can deploy application, indexing, storage, and retrieval components on AWS, Azure, or Google Cloud and select managed or self-hosted services according to security and operational needs.
Need to assess a specific AI use case? Contact Torch Solutions.
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