Evolve Edge is a RAG development company. We build custom retrieval augmented generation solutions that connect your private business data to large language models, so your AI gives accurate, source-backed answers and runs securely at enterprise scale.
50+
RAG systems shipped
4.1×
Average recall gain
68%
Average cost reduction
3–8 wk
Typical timeline
Most AI projects stall at the demo. We engineer retrieval systems as production infrastructure, with the architecture, security, and monitoring an enterprise team needs to trust them.
Hybrid retrieval, re-ranking, and caching built for steady enterprise workloads.
Pipelines built around your own documents, workflows, and business processes.
Secure deployments with monitoring and failover, tuned for real team traffic.
Demos hide the hard parts. These four gaps are where retrieval projects lose accuracy, trust, and budget once real users and real documents arrive.
Unstructured PDFs, scanned tables, and duplicate files create inaccurate retrieval and poor answers.
Basic vector search misses exact terms and returns similar text that answers another question.
Without a golden test set and quality metrics, teams cannot tell whether changes help or hurt.
Prototype pipelines break under real document volumes, strict access rules, and latency targets.
Our custom retrieval augmented generation solutions are built in three layers. Each layer is tuned on your own documents and benchmarked against your own questions.
Turns raw company content into clean, searchable knowledge.
Finds the right context for every question, every time.
Produces accurate answers that stay inside approved sources.
Every request follows the same governed path, from your source data to a verified answer. Each stage is measured, so weak links are found before users notice them.
Files, wikis, tickets, databases
Parse, clean, chunk, and tag
Convert text into vectors
Store and filter by metadata
Hybrid search, then re-rank
Generate from retrieved context
Cited, checked, and guarded
Connect internal files, databases, and SaaS tools securely, with no model retraining required.
Each answer is built from the passages that best match the question and the person asking.
Citations point to the exact document and page behind every statement the model generates.
Access filtering, encryption, and audit logs keep sensitive knowledge under verifiable control.
From a first knowledge assistant to an organization-wide platform, we cover the full build. Choose one service or combine several under a single senior team.
Build AI applications powered by your proprietary knowledge, from first prototype to production release.
Deploy secure AI knowledge systems across departments, with access control and audit trails included.
Integrate retrieval systems into your existing workflows, tools, and data platforms without disruption.
Create intelligent internal search experiences that answer employee questions using cited company sources.
Transform complex contracts, reports, and records into searchable AI knowledge your whole team can query.
Improve retrieval quality and response accuracy using benchmarks, re-ranking, and automated evaluation tests.
The pattern is the same everywhere: a large body of trusted knowledge and people who need answers from it quickly. These are the deployments we see most often.
AI-powered access to policies, wikis, and records, with every answer traced to its source.
Faster, consistent customer replies drawn only from approved help articles and product documentation.
Search, compare, and summarize large sets of contracts and case files with clause-level citations.
Secure access to complex clinical and operational knowledge, built with HIPAA-aligned controls.
Help sales, engineering, and operations teams work faster with assistants grounded in company data.
Tell us where your knowledge lives and we will scope the right retrieval approach for you.
Talk to an engineerWe choose components by benchmarking them on your data, not by habit. These are the categories we work in most often across production RAG builds.
Five stages take you from raw documents to a monitored production system. Most custom RAG projects reach launch in three to eight weeks.
Building a demo is easy. Running retrieval reliably inside a real organization takes senior judgment, and that is the work we are hired to do.
Senior engineers only, with 50+ RAG systems shipped and every build owned end to end.
Every system ships with an evaluation harness and live quality dashboards, never as a demo.
Metadata-based access filtering and private cloud deployment keep each user's data where it belongs.
Monthly retrieval reviews and ongoing tuning as your documents and questions change after launch.
A chatbot answers from what it learned in training. A RAG system answers from your approved, current knowledge and shows where each answer came from.
| Traditional Chatbots | Production RAG Systems |
|---|---|
| Generic answers from public training data | Data-grounded answers with source citations |
| Limited knowledge that goes out of date | Private business knowledge kept current |
| Higher hallucination risk on unfamiliar topics | Improved accuracy through verified retrieval |
| Basic workflows with no system access | Enterprise automation across connected tools |
Straight answers to the questions engineering leaders ask before committing budget, time, and sensitive company data to a custom retrieval build for their team.
A RAG (retrieval augmented generation) system connects a large language model to your own data. It retrieves the most relevant passages for each question, then generates an answer from them, so responses are grounded in your documents instead of the model's memory.
The model answers only from passages retrieved for that question, so it has the right facts in front of it. Hybrid search, re-ranking, and citations reduce hallucinations, and every claim can be traced to a source document your team can verify.
Yes. RAG systems index your internal files, databases, and knowledge bases without retraining the model. Metadata-based access filtering limits each user to authorized documents, and we can deploy inside your own cloud or VPC when data must stay private.
Most custom RAG projects reach production in three to eight weeks. The timeline depends on data quality, the number of sources, and compliance needs. We start with a one-week data audit, then benchmark options on your real questions before building.
Any industry with large, changing knowledge benefits. We build RAG applications for healthcare, fintech, insurance, legal, e-commerce, real estate, and logistics, from clinical knowledge search and contract review to claims triage, support automation, and internal copilots.
Off-the-shelf tools rarely fit your document types, access rules, or accuracy targets. Custom RAG development tunes chunking, retrieval, and evaluation on your data, connects to your systems, and gives you ownership of the pipeline, its metrics, and its costs.
Tell us where your knowledge lives. A senior engineer will review your data, recommend an architecture, and outline a delivery plan in one working session.
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