RAG Development Services: Chat With Your Enterprise Data
Retrieval-augmented generation (RAG) lets a language model answer from your own documents and databases instead of its training data, and cite the source it used. SumatoSoft builds RAG systems that connect ERP, CRM, SQL, PDFs and intranets, enforce role-based access at retrieval, and are tested with RAGAS before launch.
What is RAG development and when do you need it?
RAG development builds a system that finds the right passage in your documents and databases and gives it to a language model, so answers come with a source. Your company already has the information it needs. The risk comes from fragmented sources, inconsistent versions, and delayed access to critical knowledge.
- Engineers search across legacy systems.
- Legal teams review entire contracts to locate a single clause.
- Compliance specialists manually verify policies before audits.
- Support escalates tickets because knowledge is scattered.
RAG removes search friction and shortens decision cycles. Instead of digging through files, your teams receive precise, citation-backed answers inside a secure environment.
End-to-end RAG development services
We engineer production-ready RAG systems – securely, predictably, and with measurable ROI.
Discovery & feasibility
We assess your data quality, infrastructure, security posture, and projected usage.
If RAG does not create value, we communicate that upfront.
Outcome: Architecture direction + ROI and token cost forecast.
Architecture & secure design
We design RAG systems with:
- Secure ETL and data ingestion pipelines.
- Private vector databases.
- Hybrid search and re-ranking.
- RBAC at the retrieval level.
- PII masking layers.
- VPC-isolated LLM endpoints.
Your data is vectorized privately and never trains public models.
Outcome: Secure, governed RAG blueprint.
Development & integration
We build the full stack – including retrieval architecture, data pipelines, and orchestration.
Our engineers build the data pipelines for RAG that connect your legacy ERP, CRM, SQL databases, PDFs and intranets to one clean retrieval architecture.
Multi-modal support handles tables, charts, and scanned documents.
Continuous sync keeps knowledge up to date.
Outcome: Working RAG system integrated with real data.
Evaluation & production deployment
Before launch, we test and score:
- Faithfulness – no invented answers.
- Context precision – correct document retrieval.
- Prompt-injection resilience.
- Token burn projections.
We deploy inside AWS, Azure, or private infrastructure with monitoring and cost controls.
Outcome: Production-ready RAG system with governance built in.
Why do RAG systems give wrong answers?
RAG answers go wrong when data is messy and fragmented, retrieval is imprecise or the knowledge base is out of date. Here is what typically goes wrong, and how we address it.
Messy, fragmented data environments
Knowledge rarely lives in clean text files. It is scattered across scanned PDFs, complex financial tables, legacy SQL databases, SharePoint folders, ERP exports, slide decks, and archived reports. It is the most common gap. In SumatoSoft’s 2026 AI readiness research, 58% of the 72 executives surveyed named data quality and consistency as their biggest readiness gap.
When documents are not properly parsed and structured before vectorization, retrieval quality declines and answer reliability suffers.
How we solve it
We treat data readiness as a foundational engineering phase. Before deployment, we assess, structure, and prepare knowledge so the retrieval layer operates on governed, high-quality inputs instead of raw, inconsistent documents.
Retrieval precision determines answer accuracy
Large language models generate responses strictly from retrieved context.
If the wrong paragraph is retrieved or partially relevant content is supplied, the model attempts to complete the answer. Hallucinations emerge in otherwise functioning systems.
How we solve it
We design retrieval as a controlled accuracy system with measurable evaluation criteria. Responses are constrained to verified context, and reliability is validated through structured testing before production rollout.
If the answer does not exist in your documents, the system is programmed to say so rather than generate assumptions.
Data exposure risks
Information has strict access boundaries. Many RAG implementations enforce permissions at the UI level while leaving the retrieval layer unrestricted. This creates internal data exposure risk.
How we solve it
Security enforcement begins at the infrastructure level. Access control, isolation, and governance are embedded into the retrieval architecture so the system only accesses data aligned with authenticated permissions. Proprietary data remains protected within controlled environments.
Knowledge changes daily
Contracts are revised. Policies are updated. CRM records expand. A static vector database becomes outdated quickly, reducing answer reliability over time.
How we solve it
We engineer RAG as a living system. Continuous synchronization mechanisms ensure knowledge remains aligned with the current state of business data. Your RAG system always reflects the latest version of organizational truth.
Token costs scale with usage
RAG systems operate on token-based infrastructure. At a small scale, costs appear negligible. At high usage volumes, uncontrolled retrieval depth and prompt size inflate monthly cloud bills.
How we solve it
Cost governance is incorporated into architecture planning from the outset. Usage forecasting, prompt discipline, and infrastructure optimization are evaluated before scaling decisions are made, ensuring predictable total cost of ownership.
Legacy systems are hard to connect
Knowledge often lives inside 10-15-year-old ERP systems, fragmented databases, and custom web platforms. Many AI vendors build impressive demos and encounter difficulties extracting structured data from legacy environments. Integration with legacy systems was the second most common gap in the same research, named by 24% of respondents.
Without integration, RAG becomes a disconnected tool.
How we solve it
We approach RAG as an integration initiative. Data sources are systematically connected to retrieval infrastructure through engineered extraction and normalization workflows that align legacy systems with modern AI environments.
Accuracy & hallucination control framework
RAG systems fail when the model guesses. As part of our Agentic Development Lifecycle (ADLC), we add accuracy controls that can be measured and enforced, so the model answers from verified context instead.
Deterministic grounding
The model answers only from the passages it retrieved. Every answer must cite its source: a link or page number in the internal document the model used. If your documents don’t hold the answer, the system says so instead of guessing.
Hybrid retrieval & re-ranking
Hybrid retrieval combines several search methods to find the right paragraphs, and re-ranking puts the most relevant ones first. Basic RAG retrieves approximate context. Advanced RAG retrieves precise context.
We implement hybrid search architectures combining:
- Semantic search.
- Keyword search.
- Metadata filtering.
- Re-ranking algorithms.
This ensures the model receives the most relevant paragraphs before generating an answer. The LLM operates on complete and properly ranked context.
Algorithmic evaluation – RAGAS & faithfulness scoring
We score accuracy on test sets before launch instead of spot-checking answers by hand. We use automated evaluation frameworks such as RAGAS to score:
- Faithfulness – Â Did the model strictly use the retrieved context?
- Context precision – Did the retrieval layer supply the correct documents?
- Answer relevancy – Does the response resolve the user’s query?
Red-teaming & prompt injection defense
We attack the system on purpose before launch to find its weak spots. Before rollout, we simulate:
- Prompt injection attempts.
- Data extraction manipulation.
- Role escalation attempts.
- Guardrail bypass scenarios.
How much would your RAG system cost?
Tell us which data sources the system should search and who will use it. We’ll reply within 1 business day with a cost range for a 4-week proof of concept and the production build.
Secure & governed RAG architecture
We build RAG as governed infrastructure: your data stays in your perimeter, access rules hold at retrieval, and every answer can be traced.
Dual-engine integration capability
We connect the AI to the systems your knowledge already lives in, old ones included. Because SumatoSoft engineers both traditional software and AI systems, we build the full bridge:
- Secure extraction from legacy ERP and SQL systems.
- Structured ingestion services.
- Normalized knowledge layers.
- Integration into modern vector infrastructure.
Isolated, production-grade deployment
Your data stays inside your own cloud. We deploy RAG systems through:
- VPC-isolated infrastructure.
- Private LLM endpoints.
- Open-source model hosting in secure environments.
- Encrypted data channels.
At query time, only the relevant context goes to the model. The model processes it inside a controlled endpoint, does not retain it and never uses it for public training.
Governance embedded in the retrieval layer
The AI retrieves only what the signed-in user is allowed to see. Permissions are enforced in the retrieval logic, before the model generates anything, which gives you:
- User-aligned document access.
- Role-aware knowledge boundaries.
- Infrastructure-level enforcement.
- Audit-ready traceability.
Built for compliance-grade environments
We build systems your compliance team can approve. Every deployment covers:
- Isolation boundaries.
- Encryption standards.
- Audit logging.
- Usage monitoring.
- Controlled retention policies.
SumatoSoft holds ISO/IEC 27001:2022 and ISO 9001:2015 certifications.
PII protection & data safeguarding
Personal and regulated data can be masked before it is indexed. Retrieval then stays within defined compliance boundaries, and the data keeps its integrity and traceability.
Living knowledge infrastructure
When your documents change, the system updates with them. We sync RAG systems continuously with your policies, contracts and operational records, so nobody has to rebuild the index by hand.
Forecasting your AI ROI – no surprise cloud bills
A RAG system that answers correctly and consumes unlimited tokens creates financial risk. We engineer cost predictability from day one.
What we model before you scale
- Expected monthly token consumption.
- Infrastructure and vector database load.
- Scaling scenarios based on user growth.
- Cost comparison vs. current manual workflows.
You receive a projected operating cost range before full deployment begins.
How we reduce token waste
- Context compression and smart chunking.
- Hybrid retrieval to minimize prompt size.
- Re-ranking to prevent over-fetching.
- Model-size optimization per use case.
Well-architected RAG systems operate inside defined economic boundaries.
What you get
- Estimated monthly AI operating cost.
- Scaling cost forecast.
- ROI breakeven projection.
- Clear total cost of ownership (TCO) model.
AI built with financial predictability and operational control.
RAG vs. Fine-tuning – strategic decision matrix
Choose RAG for enterprise knowledge systems; fine-tune only when you need deep control over model behavior or domain reasoning. For most knowledge systems, RAG delivers faster ROI, stronger governance and lower operational risk. When fine-tuning is the right call, our LLM fine-tuning work covers it.
| Decision factor | Retrieval-augmented generation (RAG) | Fine-tuning |
|---|---|---|
Primary purpose |
Real-time knowledge retrieval from enterprise documents |
Behavioral customization of the model |
Upfront cost |
Low to moderate (embedding + architecture setup) |
High (GPU training, dataset preparation, experimentation) |
Ongoing cost |
Predictable inference costs |
Additional retraining costs as knowledge evolves |
Knowledge updates |
Instant – add or modify a document, re-vectorize automatically |
Requires a retraining cycle to update knowledge |
Source citations |
Yes – returns page numbers, document links, traceable context |
No – knowledge is embedded in model weights |
Explainability |
High – retrieved paragraphs are visible and auditable |
Low – difficult to trace the reasoning origin |
Security control |
Data stays in a private vector DB inside VPC |
Training pipelines require large-scale data exposure |
Compliance readiness |
Strong – RBAC and document-level permissions |
Complex – retrained models are harder to audit |
Operational flexibility |
Adjust retrieval logic, chunking, and re-ranking without retraining |
Behavior changes require a new fine-tuning cycle |
Scalability |
Scales with document-based and query volume |
Scales with compute and training resources |
Best for |
Internal knowledge assistants, compliance search, support copilots |
Domain-specific reasoning, style control, proprietary AI products |
Risk profile |
Lower operational and governance risk |
Higher cost and governance complexity |
Frequently asked questions
What does RAG stand for in AI?
RAG stands for retrieval-augmented generation: a language model answers from your own documents and databases and cites the source it used. Before it answers, the system retrieves the relevant passages from your knowledge base and passes them to the model, so the answer doesn’t depend on what the model learned in training.
Which vector database should we use for RAG?
It depends on where your data already lives, how much of it there is and where it must be hosted. pgvector suits teams already on PostgreSQL that want vectors next to their relational data. Pinecone is a fully managed cloud service. Milvus and Weaviate are open-source and can run inside your own cloud. We recommend one during discovery, based on data volume, query load, hosting rules and cost.
What is RAG as a service?
RAG as a service means a provider builds and runs the retrieval layer for you: the data pipelines, the vector database, access rules, accuracy testing and monitoring. Your teams use the system, while the provider keeps its knowledge in sync and its costs under control. SumatoSoft deploys it inside your own cloud, on AWS, Azure or private infrastructure, so your data stays in your perimeter.
Can RAG understand complex tables and charts in our PDFs?
Yes. We use OCR and document parsing models such as Unstructured.io. They keep tables and images intact when the data is vectorized, instead of flattening them into raw text.
Will the AI provide a source for its answers?
Yes. We engineer RAG prompts with deterministic grounding, forcing the LLM to output a direct link or page number to the exact internal document used to generate the answer.
 Start your AI journey today
RAG answers go wrong when data is messy and fragmented, retrieval is imprecise or the knowledge base is out of date. Here is what typically goes wrong, and how we address it.
Enterprise GenAI tech stack
For a worked example of these tools in a multi-step retrieval system, read our agentic RAG implementation guide.
Foundation models and access path
- Azure OpenAI
- Amazon Bedrock
- Anthropic Claude
- Meta Llama
- and more
Orchestration and agent frameworks
- LangChain
- AutoGen
- LlamaIndex
- CrewAI
- and more
Vector databases and retrieval
- Pinecone
- Weaviate
- pgvector
- Qdrant
- and more
Evaluation, guardrails, and observability
- Response evaluation frameworks
- Logging and traceability layers
- Access control and policy enforcement
- Usage monitoring and budget tracking
Awards & Recognitions
Our recent AI works
We brought in SumatoSoft to help us reduce unexpected turbine failures, and the result met our expectations. From the early stages, their team demonstrated a strong understanding of our business domain and the constraints of working with existing SCADA systems. They took a proactive approach, carefully analyzing our requirements and proposing a solution that enhanced our infrastructure without disrupting it. Communication was clear and structured, and the project was managed professionally, with all milestones delivered on time.
They are very sharp and have a high-quality team. I expect quality from people, and they have the kind of team I can work with. They were upfront about everything that needed to be done.
I appreciated that the cost of the project turned out to be smaller than what we expected because they made some very good suggestions. They are very pleasant to work with.
SumatoSoft is the firm to work with if you want to keep up to high standards. The professional workflows they stick to result in exceptional quality.
Important, they help you think with the business logic of your application and they don’t blindly follow what you are saying. Which is super important. Overall, great skills, good communication, and happy with the results so far.
SumatoSoft succeeded in building a more manageable solution that is much easier to maintain. Their project management reflects the excellence of a larger company. Their team delivers high-quality results, attentive service, and reasonable pricing.
Thanks to SumatoSoft’s can-do attitude, amazing work ethic, and willingness to tackle clients’ problems as their own, they’ve become an integral part of our team. We’ve been truly impressed with their professionalism and performance and continue to work with the team on developing new applications.
We are completely satisfied with the results of our cooperation and will be happy to recommend SumatoSoft as a reliable and competent partner for development of web-based solutions
The system has produced a significant competitive advantage in the industry thanks to SumatoSoft’s well-thought opinions. They shouldered the burden of constantly updating a project management tool with a high level of detail and were committed to producing the best possible solution.
Prove the value of your data in 4 weeks
Avoid committing to a full rollout before seeing measurable results.
A RAG pilot runs as our 4-week AI proof of concept:Â a fixed-scope, fixed-price sandbox, that validates technical feasibility, quantifies ROI and forecasts token costs before you scale to production.
We securely analyze a defined slice of your data (e.g., 500 HR documents, 1,000 support tickets, or one CRM dataset). We evaluate data quality, structure, access controls, and compliance constraints.
You receive:
- RAG feasibility confirmation.
- Data ingestion strategy.
- Security & deployment model recommendation (Amazon Bedrock, Azure OpenAI, or private open-source).
We design and deploy a production-grade RAG sandbox inside a VPC-isolated environment.
This includes:
- Secure vector database setup.
- Hybrid search (semantic + keyword).
- Role-based access controls (RBAC).
- PII masking pipeline (if required).
- Deterministic grounding prompts.
Your data remains fully private. Nothing trains public models.
We measure system performance using defined evaluation criteria. Using automated evaluation frameworks (such as RAGAS), we score:
- Faithfulness – the model strictly uses retrieved documents.
- Context precision – the retrieval layer supplies the correct data.
- Answer accuracy – the output matches ground truth.
We also perform prompt-injection red-teaming to stress test security guardrails.
Before scaling, we simulate real-world usage at your expected query volume. You receive:
- Estimated monthly AI operating cost.
- Scaling cost forecast.
- ROI breakeven projection.
- Total cost of ownership (TCO) model.
- A go/no-go readout for your executives.
Let’s start
If you have any questions, email us info@sumatosoft.com
