Enterprise Knowledge Search
Connect LLMs to internal docs, wikis, and policies. Teams ask questions in natural language and get accurate answers with source citations — no more digging through SharePoint or Confluence.
RAG Development Services
Your LLMs are only as good as the data they access. We build retrieval-augmented generation systems that connect AI to your enterprise knowledge — reducing hallucinations by 70-90% and delivering answers your teams can actually trust, with source citations.
Let's connect to help you scale fast.
70-90%
Hallucination Reduction (Databricks/Anthropic)
67%
Of GenAI in Production Uses RAG (McKinsey)
$5K
Starting POC for Enterprise RAG
4.9
Clutch Rating (34 Reviews)
RAG Solutions We Build
Six retrieval-augmented generation systems purpose-built for enterprise knowledge workflows — from document search to autonomous reasoning.
Customer Support RAG
AI support that answers from your actual documentation — not generic training data. One client achieved 94% accuracy on 50,000+ policy documents, reducing ticket resolution time by 41%.
Legal & Contract Analysis
Extract clauses, compare terms, and flag risks across thousands of documents in seconds. RAG-powered contract intelligence that turns legal review from weeks to hours.
Medical & Clinical RAG
HIPAA-compliant retrieval from medical records, clinical guidelines, and drug databases. Physicians get evidence-based answers at the point of care with full citation trails.
Financial Document Intelligence
Parse earnings reports, regulatory filings, and market research with compliance-ready retrieval. Financial analysts get synthesized insights instead of reading hundreds of pages.
Agentic RAG
RAG systems that don't just retrieve — they reason, decide, and act. Combine retrieval with autonomous agent capabilities for multi-step research, analysis, and decision support workflows.
67% of enterprises using GenAI in production already rely on RAG.
It's the most reliable way to ground LLM answers in your own knowledge — and the cheapest path from prototype to production-ready accuracy.
Why Most RAG Systems Fail in Production
Gartner reports 56% of enterprises cite hallucination as the #1 barrier to AI deployment. We've built RAG systems processing 300+ queries daily across 50,000+ documents with 94% accuracy by getting these engineering decisions right.
56%
Cite hallucination as #1 AI blocker (Gartner)
40–60%
Of RAG pilots fail to reach production
94%
Production accuracy on our deployments
300+
Queries / day across 50K+ documents
Chunking Strategy
Wrong chunking = wrong answers. Semantic, hierarchical, and hybrid approaches are needed for different content types.
Embedding Quality
Choosing between OpenAI, Cohere, and domain-specific models matters more than most realize.
Retrieval Precision
Vector search alone isn't enough. Hybrid search (semantic + keyword + metadata filtering) is required for production accuracy.
Context Window Management
Fitting the right information in limited context windows without losing critical details.
Evaluation & Monitoring
You can't improve what you can't measure. Automated quality tracking in production is non-negotiable.
Don't become the 40-60% that fails.
Start with a $5K Proof-of-Concept on your own data — clear accuracy targets, production-grade architecture, no lock-in.
Industry-Specific RAG Use Cases
RAG systems built with deep domain knowledge - not generic AI applied to your documents.
Supplier Contract Analysis
Extract terms, SLAs, pricing, and penalty clauses from supplier contracts. Compare across vendors in seconds.
Shipment Documentation
Retrieve shipping regulations, customs requirements, and BOL details across multi-carrier shipments instantly.
Customs Regulation Lookup
Instant answers on customs tariffs, import/export regulations, and country-specific documentation requirements.
SOP Knowledge Base
Warehouse and operations teams query standard operating procedures in natural language. Faster onboarding, fewer errors.
Our RAG Development Process
- 1
Discovery & Data Audit
Assess your documents, data quality, and use case requirements. Identify the right data sources and define accuracy targets. 1 week.
- 2
Architecture Design
Choose chunking strategy, embedding model, vector database, and retrieval approach. Design for your specific data types and query patterns. 1 week.
- 3
RAG Pipeline Build
Implement the full ingestion, indexing, retrieval, and generation pipeline. Hybrid search, metadata filtering, and source citation included. 2-4 weeks.
- 4
Evaluation & Tuning
Measure accuracy, latency, and relevance using Ragas and custom benchmarks. Optimize retrieval precision and answer quality. 1-2 weeks.
- 5
Security & Compliance
RBAC, encryption at rest and in transit, audit trails. HIPAA and SOC 2 compliance as needed. On-premise deployment available. 1 week.
- 6
Deploy & Monitor
Launch with production monitoring, drift detection, and quality alerts. Continuous accuracy tracking and automated evaluation in production. Ongoing.
Ready to connect your LLM to your enterprise data?
Get a tailored RAG architecture recommendation in 48 hours.
Technology Stack
- LLMs
GPT-4o
Claude
Gemini
Cohere
Mistral
Llama
- Vector Databases
Pinecone- Weaviate
Qdrant
Chroma
pgvector
Milvus
- Embeddings
OpenAI Ada- Cohere Embed
Sentence Transformers- domain fine-tuned
- Orchestration
LangChain- LlamaIndex
Haystack- custom pipelines
- Infrastructure
AWS
GCP
Azure
Docker
Kubernetes
- Monitoring & Evaluation
LangSmith- Ragas
- custom dashboards
- drift detection
- Security & Compliance
- RBAC
- tenant isolation
- audit logs
HIPAA
SOC 2- on-premise options
RAG Development Cost
RAG POC
$5K – $10K
- Single data source
- Basic retrieval pipeline
- Accuracy benchmarks
- Feasibility report
Time: 1 – 2 weeks
Start $5K POCProduction RAG
$10K – $25K
- Full RAG pipeline
- Hybrid search (semantic + keyword)
- Production monitoring
- Source citations included
Time: 3 – 6 weeks
Get ProposalEnterprise RAG
$25K – $45K
- Multi-source retrieval
- RBAC & compliance
- Advanced evaluation
- HIPAA / SOC 2 ready
Time: 6 – 12 weeks
Get ProposalAgentic RAG
$45K – $75K
- RAG + autonomous agents
- Multi-step reasoning
- Decision & action workflows
- Enterprise-grade monitoring
Time: 8 – 16 weeks
Contact UsWhat Affects Cost
- Number and type of data sources (PDFs, wikis, databases, APIs)
- Volume of documents to index and re-index frequency
- Required accuracy threshold and evaluation rigor
- Compliance footprint (HIPAA, SOC 2, on-premise)
- Custom integrations with CRMs, ERPs, EHRs
- Whether reasoning / agentic capabilities are required
How We Keep Cost Predictable
- POC validates accuracy on YOUR data before any production spend
- Reusable chunking, hybrid search and evaluation patterns from 100+ projects
- Pre-integrated security primitives (RBAC, audit, encryption)
- Production monitoring built in from day one — no separate engagement
- Fixed-scope proposals — no open-ended T&M billing
Why Companies Choose Softermii for RAG
| Criterion | Softermii | Big Consultancies | DIY / In-House |
|---|---|---|---|
| POC Timeline | 1–2 weeks | 4–8 weeks | 2–6 months |
| Hallucination Rate | <10% (hybrid search + evaluation) | 15–25% | 20–40% |
| Production Monitoring | Built in from day 1 | Extra engagement | Usually missing |
| Source Citations | Always included | Sometimes | Rarely |
| Compliance (HIPAA, SOC 2) | Built in | Extra cost | Self-managed |
| Code & IP Ownership | 100% yours | Often licensed | Yours, but lacks discipline |
RAG isn't about connecting an LLM to a database. It's a precision engineering challenge — the chunking strategy, embedding selection, and retrieval pipeline determine whether your system gives trustworthy answers or confident hallucinations. The difference between a demo that impresses and a system that works in production is 80% engineering discipline and 20% AI.
CSO & Co-Founder, Softermii
Andrii Horiachko
Testimonials
Softermii has a hard commitment towards the project delivery on time without any delay.
We ended up by having a very attractive product that can compete with any other virtual platform.
Walid Farghal, Director General, Event10x
Excellent programming skills and timely delivery.
They were able to take our poorly documented description and deliver a world-class app.
Folabi Ogunkoya, Founder, Cococure
It's great to know that all of the development is backed up by careful planning.
The app has so far garnered a lot of attention from potential investors. Softermii has very structured project management and utilizes the Atlassian Suite; their team is organized, serious, and professional.
Eriz Zarate, CTO, SoundIt
The team is really flexible with picking up urgent bugs.
I found that is a really good working relationship in that sense that the prices are very reasonable and they are accessible even over the weekend.
Duncan Mitchell, Managing Director, Co-Founder, TempTribe, London
Softermii delivered a technically sophisticated app.
It integrates multi-party video conferences with social media dynamics. These guys proven to be a professional, reliable, and effective partner.
David Levine, Founder, Scoby Social
I would highly recommend Softermii for any programming needs.
I am consistently impressed by the quality of the work and team effort brought forth by everyone that we've worked with.
Ashley Lewis, VP of Product, Dollar Shave Club
They were really on top of everything.
They know how important my timelines were and they made sure that they’re dead to them and got everything done quickly.
Reece Samani, CEO & Founder, Locum App, London
They delivered amazing results and worked through holidays to make sure I could deliver on the project deadline.
The results were consistently top quality and the devs are friendly and responsive.
Shervin Delband, Director of US Operations, ITRex Group
Frequently Asked Questions
What is RAG and why does it matter?
RAG (Retrieval-Augmented Generation) connects LLMs to your actual data, so they answer based on facts rather than training data. Instead of an LLM guessing or hallucinating, it retrieves relevant documents from your knowledge base and generates answers grounded in that information. This reduces hallucinations by 70-90% and provides source citations so users can verify every answer.
How much does RAG development cost?
From $5K for a proof of concept to $75K for enterprise agentic RAG. A production single-source system typically costs $15K–$25K. Enterprise multi-source systems with compliance requirements run $30K–$45K. We recommend starting with a $5K–$10K POC to validate accuracy on your actual data before committing to a full production build.
How long does RAG implementation take?
1–2 weeks for a POC with a single data source. 3–6 weeks for a production system with hybrid search and monitoring. 6–12 weeks for enterprise multi-source systems with RBAC, compliance, and advanced evaluation. 8–16 weeks for agentic RAG with autonomous reasoning capabilities.
What data sources can RAG connect to?
Virtually any data source — PDFs, Word documents, wikis, databases, APIs, Confluence, SharePoint, Google Drive, Slack archives, email, Notion, and more. We handle both structured data (databases, spreadsheets) and unstructured data (documents, images with OCR, audio transcripts). The key is choosing the right chunking and indexing strategy for each data type.
How do you handle data security?
Encryption at rest and in transit, role-based access control (RBAC), comprehensive audit logs, and optional on-premise deployment where your data never leaves your infrastructure. We build HIPAA-compliant and SOC 2-compliant systems. Vector databases are configured with tenant isolation, and we implement document-level access controls so users only retrieve what they're authorized to see.
RAG vs. fine-tuning — which should we choose?
RAG is the right choice when your data changes frequently, you need source citations, or you want answers grounded in specific documents. Fine-tuning is better for teaching an LLM domain-specific language, tone, or behavior patterns. Most enterprises need RAG first — it's faster to implement, easier to update, and provides verifiable answers. We add fine-tuning when needed for specialized vocabulary or output formatting.
How do you measure RAG accuracy?
We use automated evaluation frameworks including Ragas and custom benchmarks tailored to your use case. Key metrics include faithfulness (does the answer match the source?), relevance (did we retrieve the right documents?), answer correctness, and latency. In production, we run continuous evaluation with quality alerts so accuracy is monitored 24/7, not just at launch.
Do we own the code?
Yes. 100% code and IP ownership. You own the entire RAG pipeline — ingestion, indexing, retrieval, generation, and monitoring code. You can deploy on your own infrastructure, modify as needed, and maintain full control of your data and systems. No vendor lock-in, no licensing fees on code we build for you.
Ready to Build RAG That Actually Works in Production?
Tell us about your data and the questions you need answered. We'll assess feasibility, recommend an architecture, and provide a fixed-scope proposal within 5 business days. Or start with a $5K POC and validate accuracy on your own documents before committing.
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