RAG Agent
Indexes private documents, retrieves grounded answers, evaluates quality, and exposes reliable RAG workflows to business teams.
91% grounded answer rate
expected outcome
45 days
estimated payback
$20,950
monthly savings model
5
integrations
58
Hours automated monthly
$20,950
Savings potential
From $790/mo
Starting price
Inputs, outputs, and limitations
Inputs
OpenAI, Anthropic, Pinecone, Qdrant feed the workflow with controlled permissions and source visibility.
Outputs
91% grounded answer rate, prioritized actions, audit notes, and reviewer-ready work packets for the team.
Limitations
The agent drafts, routes, and measures work, but sensitive decisions stay behind approval gates until a reviewer signs off.
Features
Document ingestion
Document ingestion is configured with scoped context, role-specific review rules, and clear analytics for rollout measurement.
Hybrid retrieval
Hybrid retrieval is configured with scoped context, role-specific review rules, and clear analytics for rollout measurement.
Evaluation harness
Evaluation harness is configured with scoped context, role-specific review rules, and clear analytics for rollout measurement.
Citation controls
Citation controls is configured with scoped context, role-specific review rules, and clear analytics for rollout measurement.
Architecture diagram
Connectors
Policy engine
Tool-using LLM
Workflow queue
Analytics layer
Workflow
Collect operational context from connected systems
Plan the next best action with guardrails and approval rules
Execute work through integrations and human checkpoints
Measure the outcome and update the knowledge layer
Example output and rollout plan
Example output
84/100
Automation readiness score
Critical issues
- Missing owner for review
- No exception queue
- Weak measurement baseline
- Integration access needs approval
Next actions
- 1Confirm workflow owner
- 2Add approval rule
- 3Connect source system
- 4Set baseline metric
Before / after
Before
51/100
manual, fragmented workflow
After
88/100
governed automation run
Implementation timeline
Day 1: connect data and define owners
Day 3: run first supervised workflow
Day 7: ship first approved fixes
Day 14: review quality and exception rate
Day 30: report ROI and rollout decision
Risk and guardrails
No publishing without human approval
RAG Agent can prepare and route work, but the workflow keeps sensitive actions visible, reviewable, and reversible.
No sensitive data export without scoped permission
RAG Agent can prepare and route work, but the workflow keeps sensitive actions visible, reviewable, and reversible.
No customer-facing response without policy match
RAG Agent can prepare and route work, but the workflow keeps sensitive actions visible, reviewable, and reversible.
Every action logs source context, reviewer, and outcome
RAG Agent can prepare and route work, but the workflow keeps sensitive actions visible, reviewable, and reversible.
Benefits
Reduces repetitive manual work without replacing business ownership
ProcessForge keeps ownership visible while giving the agent enough context and tools to remove repetitive work.
Creates an auditable trail for every automated decision
ProcessForge keeps ownership visible while giving the agent enough context and tools to remove repetitive work.
Improves cycle time while keeping sensitive steps reviewable
ProcessForge keeps ownership visible while giving the agent enough context and tools to remove repetitive work.
ROI Calculator
Estimate savings for one process.
Monthly savings
$11,741
Annual savings
$140,890
Estimated payback
9 days
FAQ
How does the RAG Agent stay safe?
ProcessForge applies scoped tool permissions, approval gates, audit logs, and retrieval-grounded instructions before the agent can act.
Can this agent work with our existing tools?
Yes. The agent is designed around connectors, webhooks, API calls, and human review queues so it can fit into an existing stack.
What should we automate first?
Start with high-volume, rules-heavy work where the input data is already digital and the success criteria are easy to measure.
Deploy a trusted RAG Agent.
Turn this agent into one supervised workflow with guardrails, approvals, baseline metrics, and an ROI report.