LLM & RAG Implementation
Turn your data into a model your team can actually use.
Vector + structured + first-party data, blended properly. No 'throw documents at OpenAI and pray.'
In plain English.
We build retrieval-augmented generation systems that let an LLM answer from your knowledge (documents, databases, tickets, product data) accurately and with citations. Done right, RAG turns scattered institutional knowledge into a tool your team trusts; done lazily, it hallucinates and erodes that trust fast.
We blend the data types that actually matter: vector search over unstructured content, structured queries against Postgres and Snowflake, and first-party signals from your CRM. That combination is what separates a system that answers correctly from one that confidently makes things up.
We build on Claude and OpenAI with a proper retrieval pipeline, evaluation, and citation surfacing, integrated into where your team already works, whether that's Slack, your help center, or an internal tool. You get answers grounded in your data, not the open internet.
When you need this
- Your team wastes hours hunting for answers buried in docs and tickets.
- You want an AI assistant grounded in your data, not generic web knowledge.
- A naive 'chat with your PDFs' attempt hallucinated and lost trust.
- Support, sales, or ops needs instant, cited answers from internal knowledge.
The deliverables, plainly stated.
- Retrieval pipeline blending vector, structured, and first-party data
- LLM integration on Claude or OpenAI with citation surfacing
- Connectors to your sources: Postgres, Snowflake, Notion, Confluence
- Evaluation harness measuring answer accuracy and grounding
- Delivery into where work happens: Slack, help center, or internal tool
- Guardrails for freshness, access control, and hallucination reduction
Typical duration
30-day cycles (1 to 2 cycles typical)
Investment band
$$$Significant investment
We scope in bands, not fixed numbers. Final pricing follows a quick scoping call.
A process built for this service, not a generic playbook.
- 01
Inventory the knowledge
We map your sources (documents, Postgres, Snowflake, Notion, Confluence) and decide what to index and how to chunk it.
- 02
Build the retrieval pipeline
We combine vector search with structured queries and first-party CRM signals so retrieval pulls the right context.
- 03
Ground and cite
We integrate Claude or OpenAI to answer strictly from retrieved context, with citations and access controls enforced.
- 04
Evaluate and deploy
We measure accuracy and grounding with an eval harness, tune retrieval, then ship into Slack or your help center.
Team composition
A lead AI engineer with RAG experience, a data engineer for connectors and indexing, and a solutions architect on retrieval design.
Tools & frameworks
- Claude and OpenAI for generation
- Vector search plus Postgres and Snowflake for structured retrieval
- Notion and Confluence connectors
- Native Bridge RAG evaluation harness
What we tie this engagement to.
Every engagement carries a revenue-tied KPI. These are the outcomes this service typically anchors on.
Accurate, cited answers grounded in your own data
Hours of search time reclaimed across support, sales, or ops
A retrieval system that holds trust because it's measured, not guessed
Works with your stack
We deliver LLM & RAG Implementation inside the tools you already run.
LLM & RAG Implementation: common questions
What is RAG (retrieval-augmented generation)?
RAG is a technique where an LLM answers using context retrieved from your own data rather than only its training. Native Bridge builds RAG systems that blend vector search, structured queries against Postgres and Snowflake, and first-party CRM data so answers are accurate and cited.
Why do most 'chat with your documents' projects fail?
They throw documents at an LLM with naive retrieval and no evaluation, so the model hallucinates and loses trust. We blend data types properly, surface citations, and measure answer grounding with an evaluation harness.
Which models do you use?
We build primarily on Claude and OpenAI, choosing per use case based on accuracy, latency, and cost, and we keep the architecture model-agnostic so you can switch as the landscape changes.
Where can our team access the system?
We deliver into where work already happens, whether that's Slack, your help center, or an internal tool, and connect to sources like Notion and Confluence, so adoption doesn't require a new habit.
How do you control for hallucinations and stale answers?
We constrain the model to answer from retrieved context, surface citations, enforce access controls, and run an evaluation harness that scores accuracy and grounding, plus freshness rules so the index stays current.
Often paired with this.
Ready to put LLM & RAG Implementation to work?
Tell us where you are and we'll tell you what's blocking revenue.