We build retrieval-augmented generation systems that ground AI responses in your actual documents. Not hallucinated facts—verified information with citations. From knowledge bases to customer support, we deliver RAG that works in production.
Oleg Kalyta
Founder & AI Lead
Free Discovery
Document analysis, architecture recommendationRAG Prototype
Working system with your documentsProduction Ready
Full solution deployed
Your team went above and beyond and built an interesting project in very short time.
These are the problems that bring companies to us. Sound familiar?
LLM responses are unreliable or hallucinate facts
RAG grounds every answer in retrieved documents. Citations let users verify. Confidence thresholds prevent overconfident wrong answers.
Knowledge is trapped in documents nobody can search effectively
RAG makes your documents searchable with natural language. Semantic search finds relevant content even when keywords don't match.
Keeping AI current requires expensive retraining
RAG retrieves from your live knowledge base. Update a document and the system knows immediately. No retraining required.
Generic chatbots don't know your product or policies
RAG connects LLMs to your actual documentation. Every answer specific to your business, not generic web knowledge.
Support costs are scaling faster than revenue
RAG-powered support agents handle routine queries autonomously. Our benchmark: $0.02 per conversation, 70% ticket automation.
Compliance requires traceability for AI-generated content
Every RAG response cites source documents. Full audit trails. Regulators can verify. No black box answers.
From architecture through deployment and optimization. We handle the full RAG development lifecycle.
Most RAG projects fail before they start. Wrong chunking strategy. Embedding model that doesn't fit your content type. Vector database chosen for the wrong reasons. We spend the first week understanding your documents, your queries, and your accuracy requirements. Then we design an architecture that actually works. Not a generic template—a system built for your specific knowledge base.
End-to-end RAG systems that go beyond basic document Q&A. We build applications where retrieval is just the beginning—summarization, comparison, extraction, report generation. The interface your users interact with. The APIs your systems call. The monitoring that catches problems before users notice. Production-grade, not proof-of-concept-grade.
The retrieval pipeline makes or breaks RAG quality. Chunking strategies that preserve context. Embedding models that understand your domain vocabulary. Reranking that surfaces the right documents. Hybrid search when semantic alone isn't enough. We tune each component until retrieval precision hits your accuracy targets. Most projects need 85%+. Some need 95%+. We get there.
Corporate knowledge trapped in SharePoint, Confluence, Google Drive, and dozens of other places. We build RAG systems that unify it. Employees ask questions in natural language and get answers with citations—from the actual source documents, not hallucinated facts. Role-based access ensures people only see what they're authorized to see. The enterprise search you always wanted.
Chatbots that don't make things up. Every answer grounded in your documents. Every response includes sources. Conversation history that maintains context across turns. Graceful handling when the knowledge base doesn't have the answer. We integrate with Slack, Teams, your website, your mobile app—wherever your users are. Not a generic chatbot with your logo. A knowledge assistant that knows your business.
RAG that reasons before it retrieves. Multi-step queries decomposed into sub-questions. Agents that decide which knowledge bases to search. Systems that synthesize information across sources, compare documents, and build structured outputs. This is where RAG meets AI agents—autonomous reasoning backed by your actual data. Still emerging. Complex to get right. Worth it when you need it.
Real projects, measurable results. RAG systems in production serving actual users.
Most clients start unsure whether they need RAG, fine-tuning, or something else entirely. That's what the discovery phase is for.
Different problems require different RAG architectures. Here are the systems we develop.
The most common RAG use case. Upload documents, ask questions, get answers. But production-grade document Q&A is harder than it looks. Legal contracts with nested clauses. Technical manuals with tables and diagrams. Research papers with citations. We handle document complexity that breaks generic RAG solutions. Output includes source citations, confidence indicators, and graceful fallbacks when information isn't found.
See example:RaisalComplex document processing at scale→Support agents that actually know your product. Not because they memorized FAQs—because they retrieve from your entire knowledge base: product docs, past tickets, internal wikis, release notes. Customers get accurate answers in seconds. Support tickets that used to take hours get resolved in minutes. Our AI support agent runs at $0.02 per conversation. That's the benchmark we hit.
See example:AI Support Agent$0.02/conversation, 70% automation→Employees spend 20% of their time searching for information. Usually they give up and ask a colleague. Or worse—they guess. Internal knowledge assistants give instant access to company knowledge: policies, procedures, technical specs, project history. New employees get up to speed faster. Experts stop answering the same questions. Institutional knowledge stops walking out the door when people leave.
See example:Healthcare PlatformKnowledge retrieval for field workers→Analysts drowning in documents. Market research reports, competitor filings, industry publications, internal memos. RAG systems that let them ask questions across the entire corpus. Compare positions across documents. Identify contradictions. Generate summaries with citations. We've built these for legal due diligence, competitive intelligence, and academic research. Turns weeks into hours.
See example:BeautyAdvisorAI-powered product research→Regulatory requirements scattered across hundreds of documents. Policy updates that employees miss. Compliance questions that require legal review. RAG systems that know your regulatory landscape. Employees ask 'Can we do X?' and get answers citing specific policy sections. Auditors ask questions and get documented evidence. Keeps you compliant without slowing you down.
See example:Healthcare PlatformHIPAA-compliant document access→Text isn't everything. Technical diagrams that explain how systems work. Product images that show variations. PDFs with embedded charts. Video transcripts with timestamps. Multi-modal RAG retrieves across content types. Ask a question about a schematic and get the relevant diagram. Search for a product feature and see the actual interface. Still maturing. We know what works and what's still experimental.
See example:EvLuvMulti-source data integration→What most impressed me about ProductCrafters was their dedication to my project and understanding of our goals. They were very honest and transparent throughout the entire process.
They were flexible, and it was easy to work with them on a day-to-day basis. Their brilliant ideas were critical to the project success.
Out of over 40 applicants, we selected ProductCrafters based on their experience, technical expertise, and cost estimate. The team showed deep technical expertise, a strong work ethic, and honesty.
The team has honest billing practices and creates incredible value for the cost. Working with ProductCrafters has saved us hundreds of thousands of dollars compared to domestic firms.
The quality of their code makes them a valuable partner. They thought holistically about solutions and brought up all-encompassing ideas.
Their insightful advice has maximized the application's performance. We're actually learning things from ProductCrafters that we can adapt and use in other applications.
Customer stories
We work with leading vector databases, embedding models, and frameworks. The right tools for your requirements.
Pinecone
Weaviate
Chroma
PostgreSQL + pgvector
OpenAI
Claude
Gemini
Hugging Face
LangChain
LangGraph
Python
FastAPI
AWS
GCP
Docker
KubernetesWe dig into your documents before proposing solutions. What formats? How structured? What makes a good answer in your context? We test sample queries against your knowledge base. Identify where off-the-shelf solutions will fail. Define accuracy targets and success criteria. You get a clear roadmap with realistic estimates—before spending development budget.
Building the retrieval foundation. Chunking strategies tailored to your document types. Embedding model selection (or fine-tuning) for your domain. Vector database configuration. Hybrid search if semantic alone isn't sufficient. We iterate until retrieval precision hits your targets. This stage determines whether the system will work in production.
Connecting retrieval to generation. Prompt engineering that produces accurate, well-formatted responses. Citation handling so users can verify answers. Conversation history for multi-turn interactions. The interface your users will actually use—whether that's a chatbot, API, or integrated feature. Edge case handling and graceful fallbacks.
Connecting to your systems. Authentication. Data source integrations. Role-based access if needed. Then rigorous testing: adversarial queries, edge cases, load testing, failure scenarios. We break things in staging so users don't break them in production. Security review. Performance optimization. Final sign-off on accuracy targets.
Production launch with monitoring in place. We track retrieval accuracy, response quality, latency, and cost per query. Early production data reveals optimization opportunities—caching strategies, query routing, chunk size adjustments. The first month of real usage teaches more than months of testing. We stay engaged to optimize based on real patterns.
RAG applications vary dramatically by industry. Domain knowledge matters as much as technical skill.
Clinical knowledge retrieval for physicians. Patient education systems that cite medical sources. Drug information databases with interaction checking. Research literature assistants for life sciences teams. Healthcare RAG requires HIPAA compliance, medical terminology understanding, and extreme caution about the consequences of wrong answers. We've built AI systems that process thousands of patient interactions daily—with appropriate guardrails and human oversight. The stakes are too high for generic solutions.
See example:Healify$2M raised, 100K+ health queries→Regulatory document analysis. Compliance knowledge retrieval. Investment research across thousands of filings. Customer service automation for banking products. Financial RAG needs audit trails, explainability for regulators, and careful handling of advice boundaries. We build systems where every answer traces back to source documents. Compliance teams can review. Auditors can verify. No black boxes allowed.
See example:Raisal$2.5T commercial mortgage marketplace→Contract analysis at scale. Legal research across case law and statutes. Due diligence automation. Policy compliance checking. Legal RAG demands precision—missing a clause or misinterpreting precedent has consequences. We fine-tune retrieval for legal language, implement citation verification, and build confidence thresholds that prevent overconfident wrong answers. Lawyers review the edge cases. The system handles the volume.
Technical documentation search that actually works. Developer support chatbots. Internal knowledge bases for engineering teams. Customer support automation for complex products. Tech companies often have the sophistication to implement RAG but lack the specialized ML engineering bandwidth. We bridge that gap—turning scattered documentation into searchable, conversational knowledge. Our AI support agent reduced response times from 4 hours to under 1 minute.
See example:AI Support Agent4h→1min response, $0.02/conversation→Technical manuals for equipment maintenance. Safety procedure retrieval. Quality control documentation. Supply chain knowledge bases. Manufacturing RAG handles specialized terminology, equipment specifications, and safety-critical information. Field technicians get answers without calling headquarters. Quality teams access the right specs instantly. We build for environments where downtime costs money and safety isn't optional.
See example:EvLuv65K+ charging stations managed→Product information retrieval across thousands of SKUs. Customer service automation that knows your catalog. Comparison tools that help customers decide. Internal knowledge bases for retail staff. E-commerce RAG handles constantly changing inventory, seasonal variations, and customer questions that span products. Accurate product information increases conversion. Automated support scales with traffic spikes.
See example:BeautyAdvisorAI-powered product recommendations→Honest pricing based on real projects. No competitor shows RAG development costs. We do.
$15,000 – $25,000
4-6 weeks
Proof of concept with your actual data. Single document source. Basic retrieval pipeline. Working interface to test with real queries. Validates the approach before larger investment.
Best for: Validating RAG viability before full investment
$35,000 – $75,000
8-12 weeks
Complete RAG application ready for real users. Multiple data sources. Optimized retrieval with reranking. Full application development with your required integrations.
Best for: Production deployment for internal or customer-facing use
$75,000 – $150,000+
3-6 months
Enterprise-scale RAG with multiple use cases, role-based access, advanced security, and organizational integrations. For companies deploying RAG across departments.
Best for: Enterprise-wide deployment with security requirements
Clean markdown is cheap to process. Scanned PDFs with tables, images, and complex formatting require specialized extraction. Legal contracts with nested clauses need careful chunking. The messier your documents, the more work required.
80% accuracy is achievable with standard approaches. 95%+ accuracy requires advanced retrieval, reranking, fine-tuned embeddings, and extensive evaluation. Healthcare and legal applications with no tolerance for errors cost more than internal tools where occasional misses are acceptable.
Standalone chatbot is simpler than RAG integrated with your CRM, ticketing system, and authentication provider. Each integration adds development time. Legacy systems without modern APIs add more.
Systems designed for 100 queries per day are simpler than those built for 100,000. High-volume systems need caching, load balancing, and infrastructure that adds to initial cost but reduces per-query cost over time.
Standard cloud deployment is straightforward. On-premise deployment, data residency requirements, or compliance frameworks (HIPAA, SOC 2) add complexity and cost. Worth it when required—but verify you actually need it.
Start with a free discovery week. We'll analyze your documents, test retrieval feasibility, and provide realistic estimates—before you commit to anything.
RAG fundamentally changes what's possible with AI and your documents. Here's why it matters.
The fundamental problem with LLMs: they make things up. RAG changes that. Every answer retrieved from your actual documents. Every claim backed by a source citation. Users can verify. Auditors can trace. When the system doesn't know, it says so instead of inventing an answer. This is what makes RAG enterprise-ready.
Fine-tuned models freeze knowledge at training time. New product launches, policy updates, recent research—none of it exists in the model. RAG stays current because it retrieves from your live knowledge base. Update a document and the system knows immediately. No retraining. No waiting. No $50,000 fine-tuning bill every quarter.
RAG demos are easy. Production RAG is hard. Edge cases that break chunking. Queries that require information from multiple documents. Users who phrase questions in unexpected ways. We've shipped RAG systems that handle real traffic, real complexity, real user expectations. Our AI support agent processes thousands of queries daily. We know what breaks and how to fix it.
RAG can get expensive fast. Vector database costs. Embedding API calls. LLM tokens for generation. We architect for efficiency from day one. Intelligent caching. Tiered retrieval that tries cheap options first. Query routing that uses smaller models when they're sufficient. Our benchmark: $0.02 per conversation. That's not an accident—that's deliberate architecture.
RAG development requires a specific combination of skills. Here's what sets us apart.
We've built RAG systems that serve real users at scale. Our AI support agent handles thousands of queries daily at $0.02 per conversation. Healify raised $2M with RAG-powered health knowledge retrieval. The gap between a working demo and a reliable production system is vast. We've crossed it repeatedly.
RAG systems need more than good retrieval. They need APIs, frontends, authentication, monitoring, and integration with existing systems. We handle the entire stack—from vector database configuration to the chat interface users interact with. One team. One point of accountability.
Sometimes RAG isn't the answer. Sometimes your documents aren't ready. Sometimes the accuracy requirements are unrealistic for the budget. We'll tell you. Our job is solving your problem, not selling you a RAG project. If a simpler approach works better, we'll recommend it.
No competitor on the first page of Google shows RAG development pricing. We do. RAG implementation starts at $25,000. Production systems run $35,000-$75,000. Enterprise deployments can exceed $100,000. You know what you're getting into before we start.
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Meet your team
Slack channel, assigned developer, daily standups. First code committed to your GitHub.Working prototype delivered
Technical spike or prototype complete. Architecture + budget roadmap for the full build.You keep everything. Zero cost. Zero commitment.
Oleg Kalyta
Founder & AI LeadRetrieval-Augmented Generation (RAG) development is the process of building AI systems that combine large language models with external knowledge retrieval. Unlike traditional LLMs that generate responses solely from training data, RAG systems retrieve relevant information from your documents, databases, or knowledge bases before generating answers. This grounds AI responses in verified facts, provides source citations, and keeps knowledge current without expensive model retraining. RAG development involves designing retrieval pipelines, optimizing vector databases, configuring embedding models, and building applications that deliver accurate, source-backed answers to users.
Building the infrastructure that converts documents into searchable embeddings, stores them in vector databases, and retrieves relevant content for each query.
Connecting RAG systems to your actual data sources: documents, wikis, databases, and APIs. Keeping content synchronized as information changes.
Configuring LLMs to generate accurate responses from retrieved context, including citation handling, confidence scoring, and hallucination prevention.
Taking RAG from prototype to production: monitoring, scaling, cost optimization, and maintaining accuracy as usage grows.
Building for a large organization? Enterprise RAG has additional requirements.
Enterprise data can't just flow to external APIs. On-premise deployment, private cloud setups, data encryption, audit logging. HIPAA, SOC 2, GDPR compliance when required. Your legal team signs off before we deploy.
Different users should see different information. RAG systems that respect your permission model. Executives see strategic documents. Engineers see technical specs. Nobody sees what they shouldn't.
RAG that scales across the organization. Shared infrastructure with department-specific knowledge bases. Central management with distributed ownership. Consistent experience, customized content.
RAG connected to your SSO, integrated with SharePoint and Confluence, feeding answers into Slack and Teams. Fits into existing workflows instead of creating new silos.
Understand AI development costs from $5K prototypes to $180K enterprise systems. Based on real project data.
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