Internal knowledge
Retrieve relevant documents and working context before the model answers a question for an internal workflow.
Retrieval-augmented generation / grounded AI
Ground AI in the context that matters.
We connect retrieval to your selected agent or workflow so responses use relevant proprietary information available at query time—not model memory alone.
01 / Problem
Work that should not stay manual
- 01
A general-purpose model cannot reliably answer questions that depend on proprietary documents, internal language, or information that changes after training.
- 02
People search across scattered sources and manually assemble the context needed to answer a question or complete the next workflow step.
- 03
An AI assistant can produce a fluent response without grounding it in the most relevant information available at query time.
02 / Use cases
Where the system earns its place
Support context
Bring the most relevant current information into a drafted response before a person approves or sends it.
Agent grounding
Give a tool-using agent the proprietary context it needs before it decides, drafts, routes, or escalates.
Document workflows
Connect unstructured text to classification, retrieval, and the next guarded step in an operational process.
03 / Delivery
What moves from plan to production
Deployment output
- A mapped question or workflow and its required sources
- Retrieval connected to the documents and systems in scope
- Grounded responses inside the selected agent or workflow
- Approval and confidence gates for uncertain output
- Observable execution and a measurable operating target
- 01
Map the context
We identify the questions, workflow steps, proprietary sources, and freshness requirements the system must support.
- 02
Build retrieval
We connect the sources in scope and retrieve the most relevant context at query time for the selected workflow.
- 03
Wire the response
The retrieved context is passed into the model or agent step that drafts an answer or prepares the next action.
- 04
Guard and measure
We add confidence and approval gates, observable runs, rollback paths, and an operating target for the pilot.
04 / Controls
Autonomy with a kill switch
- Answers grounded in retrieved workflow context
- Confidence gates for uncertain output
- Human approval where the workflow requires it
- Observable retrieval and response steps
- Named owners and rollback paths
- A scoped set of sources and operating questions
05 / FAQ
Questions before deployment
What is retrieval-augmented generation (RAG)?
RAG retrieves relevant information at query time and supplies it to an AI model before the model responds. This lets the workflow use proprietary or changing information instead of relying only on what the model learned during training.
When is RAG useful?
RAG is useful when an answer or workflow step depends on current documents, proprietary information, internal terminology, or context spread across the systems in scope.
Does RAG eliminate hallucinations?
No system should promise that. Retrieval gives the model relevant source context and can reduce unsupported answers, but production workflows still need confidence gates, human approval where appropriate, observability, and a defined exception path.
Can RAG be part of an AI agent?
Yes. Retrieval can supply context before an agent classifies work, drafts a response, or prepares the next action. The agent can then continue through the connected workflow behind the agreed controls.
Deploy / next step
Bring the workflow that drains your week.
We will map the loop, define the guardrails, and scope a measurable deployment.
Put an agent on it →