Retrieval-Augmented Generation systems that ground LLM outputs in your documents, databases, and knowledge bases with citations, accuracy, and guardrails built in.
of enterprise GenAI use cases now rely on RAG, wherever accuracy, transparency, and source attribution are required. Gartner has identified RAG as a core capability for enterprise generative AI.
hallucination rate for LLMs depending on domain, according to the Stanford AI Index. Without grounding in verified data, LLMs fabricate confidently - making ungrounded AI unusable for anything business-critical.
Use Case & Data Audit
Data Ingestion & Chunking Pipeline
Vector Store & Retrieval Architecture
LLM Integration & Prompt Engineering
Evaluation & Accuracy Testing
Monitoring & Continuous Improvement
We sit with you, map your workflows, users, and constraints. You leave with a scoped brief — not a proposal full of assumptions.
System design and UX decisions made before a line of production code.
Sprint-based delivery with demos every cycle and one accountable lead.
Deployment to your infrastructure with docs and a clean handover.
30 days of post-launch stabilization while real usage settles in.
Founders, CTOs, Product Leaders building AI features grounded in proprietary data
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Don't see your stack? We've shipped on 15+. Tell us what you use
Docucoder captures the procedure in real time and delivers a ready to review, CPT coded surgical report by the end of the case. No paperwork relay, no lost detail.
A role based ERP that put procurement, inventory, budgets and approvals for a multi site construction firm into one platform, from the C suite to the job site.
A telecom and IT services brokerage was running catalog, orders, and multi party commissions across a generic CRM and a sprawl of Excel. We rebuilt it as a single operational platform with the commission engine at its core.
A high end household and estate staffing agency was running its pipeline across spreadsheets, email, and WhatsApp. We replaced it with a two sided, stage gated platform where each side sees only the view it needs.
Westrow brokers perishable food into every major Canadian retailer. We rebuilt the system that connects their client, their retail customers, and every product spec in between, retiring a legacy database and a wall of spreadsheets for a single source of truth.
RAG retrieves information from your data at query time and feeds it to the LLM as context. Fine-tuning changes the model itself by training it on your data. RAG is better for factual accuracy, source citation, and frequently changing data. Fine-tuning is better for teaching the model a specific tone, format, or domain vocabulary. Most enterprise use cases need RAG — or RAG combined with light fine-tuning. We help you decide during the assessment.
Book a 30-minute scoping call. We'll give you honest direction, not a sales deck.
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