RAG Development
Answers Grounded in Your Actual Content
Retrieval-augmented generation systems that connect language models to your documents, product data, and operational knowledge—with chunking, evaluation, and governance your team can operate.
When This Solution Fits
When generic chatbots and static search stop answering real questions.
Teams outgrow copy-paste into ChatGPT when answers need to cite authoritative sources, respect permissions, and stay current as content changes.
Scattered knowledge
Policies, product specs, and support articles live in CMS, wikis, PDFs, and ticket history—with no single place to ask.
Answers without sources
Staff do not trust AI replies they cannot trace back to a document, record, or approved knowledge base.
Stale or wrong retrieval
Embeddings built once drift as content updates—answers cite outdated versions or miss new material entirely.
Permission boundaries
Internal docs, partner portals, and customer-facing help need different retrieval scopes—not one open corpus.
Evaluation gaps
Demos look fine; production needs test sets, human review loops, and metrics before anyone relies on the system.
Beyond FAQ chatbots
You need synthesis across multiple sources—not scripted responses from a fixed Q&A list.
How We Build It
Corpus, retrieval, and evaluation before models go live.
We define what “correct” means for your use case, design retrieval that respects freshness and permissions, and instrument review loops so quality improves with real use.
Corpus & intent review
Map authoritative sources, high-value questions, and what success looks like per audience.
Chunking & metadata
Structure documents for retrieval—titles, sections, tags, and freshness signals that improve precision.
Retrieval architecture
Hybrid search, embeddings, reranking, and access rules tuned to your content types and latency needs.
Evaluation & rollout
Golden-question sets, citation checks, human review, and phased expansion once retrieval quality earns trust.
Stack & Integration
Platforms and patterns under expert judgment.
RAG is not a single vendor product—it is ingestion, retrieval, orchestration, and UX your team can own. We choose components that fit your stack and operational model.
Vector & search stores
PostgreSQL pgvector, OpenSearch, or managed retrieval services—selected for tenancy, scale, and ops familiarity.
Content pipelines
ETL from CMS, PIM, ticketing, and file stores with chunking, metadata enrichment, and re-indexing when sources change.
LLM orchestration
Citations, guardrails, and tool use in application code—often alongside agent orchestration when retrieval feeds operational workflows.
MCP & governed tools
Scoped connectors when agents need both document retrieval and transactional tools—built as MCP server development when that fits your stack.
Application layer
Operator UX, audit logs, and APIs on database-driven web apps when retrieval sits inside a broader platform.
Custom software delivery
Senior engineers using modern AI-assisted workflows to plan, build, and support RAG programs end to end.
Related Solutions
Orchestration, MCP, data apps, and the custom software hub.
RAG programs usually pair with governed tool layers, multi-agent workflows, operational data, and the broader custom software practice.
AI agent orchestration
Hierarchical specialists wired into CRM, CMS, and ops—with human-in-the-loop governance.
MCP servers
Governed tool layers that connect AI assistants to your systems with scoped permissions.
Database-driven web apps
Operational applications on reliable data models, workflows, and reporting.
Custom software development
Custom platforms, integrations, and operational software—the custom software pillar hub for workflow-heavy teams.
Start With the Corpus
Still answering the same questions from scattered docs?
Share the sources, the audiences, and how you will know an answer is correct. We will outline a retrieval pilot that fits how your team operates.