Every contract, policy and report your firm owns, answering questions in plain language
Your company's accumulated knowledge sits in thousands of documents nobody can search properly. We build a private system that reads them all and gives your staff precise answers with the exact source page cited, in seconds. It can run entirely inside your building so confidential material never leaves your control.
Fixed price, typically $8K to $45K · written plan in 3 days · runs on your infrastructure
السعر
$8K to $45K fixed
المدة
6 to 8 weeks
ماذا يحدث بعد ذلك
- 01مكالمة 30 دقيقة مع المؤسس، لا مع فريق مبيعات
- 02خطة مكتوبة وسعر ثابت خلال 3 أيام
- 03عرض عملي أسبوعي على بياناتكم الحقيقية أثناء البناء
- 04التسليم: الكود المصدري والتوثيق وتدريب فريقكم و30 يوماً من الدعم
ما تملكونه
النظام ملك لكم بالكامل. يعمل على بنيتكم التحتية. لا رسوم ترخيص ولا ارتباط بنا.
غير مناسب لـ
- A few dozen documents a shared drive search already handles
- Teams that need answers without a source they can check
- Anyone under ten people
إن لم يكن مناسباً لكم، نقولها في المكالمة.
For your IT team
Production RAG capabilities
- Hybrid search: dense (vector) + sparse (BM25) retrieval
- Per-document citation with source attribution on every response
- Namespace isolation for multi-tenant deployments
- On-premise deployment, keeping all data inside your infrastructure
- Quantized local LLMs (Qwen, Mistral, Llama, 4-bit) for air-gapped environments
- Re-ranking pipeline for precision improvement
- Chunking strategy tuned to your document type (PDF, HTML, code)
- Ingestion pipeline for incremental updates without full re-index
Tested model configurations
Frequently asked questions
What is a RAG system?
Retrieval-Augmented Generation (RAG) is an architecture that retrieves relevant documents from your knowledge base and passes them as context to an LLM before generating a response. This grounds answers in your actual data instead of the model's training weights, and enables citation.
Can this run completely on-premise?
Yes. We deploy quantized open-weight LLMs (Qwen, Mistral, Llama) locally using Ollama or vLLM, combined with a self-hosted Qdrant instance. A 6GB VRAM GPU can run a full production stack at 25–40 tokens/sec.
How do you prevent hallucinations?
Retrieval-grounding, forced citation, constrained generation (the model can only answer from retrieved context), and confidence thresholds that route low-confidence queries to a human fallback.
How long does ingest take for a large document corpus?
Typical ingest rates: ~500 to 2000 pages/minute with parallel chunking and batch embedding. A 10,000-document corpus typically ingests in under 2 hours. Incremental updates (new documents only) are near-real-time.
Built for your industry
Need a private knowledge assistant?
We'll scope the document corpus, recommend the right embedding and retrieval strategy, and deliver a production system with full source code.
Book a call