Custom RAG development
Full build from data ingestion to a grounded, production assistant you fully own.
RAG Development · AI-First · Results-Driven
Retrieval-Augmented Generation,Grounded in Your Data
Short answer: RAG development builds systems that retrieve relevant information from your own documents and feed it to a language model at answer time, so responses are grounded in your data instead of the model's training. It's the standard fix for hallucination and stale knowledge. Quality depends on chunking, embeddings, retrieval accuracy, and evaluation — and data prep usually dominates the timeline. We build RAG pipelines tuned to your content and measured for real accuracy.
How RAG Grounds AI in Your Data
RAG — retrieval-augmented generation — is an architecture that fetches relevant passages from a knowledge base and passes them to a language model as context, so its answer is based on your actual documents rather than whatever it memorized during training. RAG development is the engineering behind that pipeline: ingesting content, embedding it, storing vectors, and retrieving the right chunks at query time. It matters because it lets AI cite current, private, or domain-specific information accurately.
Most RAG quality problems are retrieval problems, not model problems. If the system pulls the wrong passages, even the best LLM answers wrong. That puts the real work in chunking strategy, embedding choice, metadata and filtering, reranking, and honest evaluation against a question set you actually care about. Hybrid search, source citations, and guardrails against answering when nothing relevant is found are what turn a promising prototype into something you can trust in production.
We treat evaluation as part of the build, not an afterthought — measuring retrieval and answer accuracy so improvements are provable rather than felt. Clean ingestion pipelines keep the system honest as your content grows.
What we build
Types of RAG Systems We Build
Internal knowledge assistants
Answer staff questions from wikis, SOPs and internal docs.
Learn more →Customer support copilots
Deflect tickets with grounded answers from your help content.
Learn more →Document search & Q&A
Semantic search across contracts, reports and long manuals.
Learn more →Data-grounded chatbots
Website and in-app chat that answers only from your real content.
Learn more →RAG-powered agents
Retrieval feeding multi-step agents that take action, not just chat.
Learn more →Research & analysis assistants
Summarize and compare across large private document sets.
Learn more →Services
RAG Development Services
Full-cycle RAG development services — from a grounded pilot to a scaled, production knowledge platform.
Knowledge base design & ingestion
Pipelines that clean, chunk and embed your docs and data into a searchable source of truth.
Vector search & retrieval tuning
Embeddings, reranking and retrieval quality tuned for accurate, relevant answers.
Evaluation & guardrails
Answer testing, citations and safeguards against hallucination and data leaks.
Integration & deployment
Connect CRMs, wikis, databases and chat channels, and deploy to your own cloud.
Support, monitoring & scaling
Care plans for reindexing, monitoring and improving answer quality over time.
Technology
Our RAG Stack
The exact toolset depends on your goals — these are the platforms we use most, and we work with whatever your team already relies on.
Orchestration & Frameworks
LLMs & Generation
Embeddings & Reranking
Ingestion & Chunking
Chosen per project — not a fixed menu. Have a preferred tool or platform? We’ll work with it.
Built to last
Secure, Private & Reliable
Your data stays yours — we deploy on your infrastructure or a private cloud, with access controls, encryption and no training on your data by default. Sensitive workflows keep a human in the loop.
Every agent and automation ships with monitoring, guardrails and fallbacks, so it behaves predictably in production and you can trust it with real work.
Who we build for
Industries We Automate
20+ years across sectors — in Houston and internationally.
Transparent pricing
Estimate your project in seconds
Pick what you’re building for an indicative range, then request an exact quote. No email wall.
Simple prices for typical tasks
- RAG pilotfrom $8k
- Production knowledge basefrom $16k
- Enterprise searchfrom $30k
- Care planfrom $1.5k/mo
Proof
700+ projects, 20+ years
See the products and growth work we’ve shipped across industries — and request a case study relevant to yours.
How we work
Fixed scope. Sprints. Working software.
- 01
Scope & fixed estimate
A short discovery call turns your idea into a clear spec and a firm range — free.
- 02
Design & architecture
UX, data model and stack chosen for your scale, not ours.
- 03
Build in sprints
Working software every 1–2 weeks — you see progress, not promises.
- 04
Launch & scale
We ship, measure and keep improving with care plans.
FAQ
RAG & Knowledge Bases questions
How much does RAG development cost?
Most RAG projects land between about $12,000 for a grounded pilot and $60,000+ for a production, multi-source knowledge platform. Cost depends on the number of data sources, document volume, accuracy requirements and integrations — use the estimator above for an indicative range, then book a call for a firm quote.
What is retrieval-augmented generation (RAG)?
RAG is an AI pattern that first retrieves the most relevant passages from your own documents and data, then passes them to a language model so its answer is grounded in your facts rather than its training data. Combining a custom knowledge base and vector search with a model is what makes answers accurate and traceable to a source.
How long does it take to build a RAG system?
A grounded pilot on a defined set of documents typically ships in 4–6 weeks; a fuller, multi-source platform in 2–4 months. We build in 1–2 week sprints so you can test answer quality on your own content early and often.
How is RAG different from fine-tuning or a generic chatbot?
A generic chatbot answers from public training data and can guess. Fine-tuning bakes patterns into a model but not your live facts. RAG retrieves your current documents at question time, so answers reflect your latest content, cite their source, and update the moment you update a document — no retraining required.
Does RAG reduce AI hallucinations?
Yes. By grounding every answer in retrieved passages and returning citations, RAG keeps the model close to your source material and makes answers easy to verify. We add retrieval tuning, evaluation and guardrails so the assistant says it does not know instead of inventing an answer when the knowledge base has no match.
Where does my data live, and is it secure?
We build cloud-native, scalable RAG systems on AWS, Azure or Google Cloud with encrypted storage, role-based access and options to keep data in your own environment. Security is designed in — access controls, isolation and privacy defaults — so your documents power the assistant without being exposed.
Which models and vector databases do you use?
We work with modern LLMs from OpenAI and Anthropic as well as open-source models, paired with vector search such as pgvector, Pinecone, Weaviate or Elasticsearch — chosen for your accuracy, cost and data-residency needs, not lock-in.
Do you work with businesses outside Houston?
Yes. We are based in Houston, TX and have delivered 700+ projects internationally over 20+ years, working with local and remote teams alike.
Do you offer RAG development in Houston?
Yes. We are based at 9800 Richmond Ave in Houston (Westchase / Energy Corridor) and build RAG systems and knowledge bases for Houston businesses locally, as well as clients nationwide and worldwide.
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