Data connects in
Documents, tools, email, code, policies, customer context, and operating data become permissioned memory.
Let's talkThe production layer for AI labor.
Turn AI agents into governed business capability.
Ragu gives AI agents the memory, action pathways, and promotion discipline to operate on real systems: sandboxed before they act, reviewed before they ship, and audited after they do.
Production-gradeSandbox, review, and rollback before agents touch customers, data, or revenue
Battle-tested platformRAG, orchestration, and governance hardened across four years of enterprise deployments
Client-controlledDeployable into AWS environments clients own
Promotion-readyEngineering, business, and documented skill-file gates for work moving toward production
The operating loop
Everyday work becomes AI capability.
Data in. Action pathways out. Operators in control.Since 2022, Ragu has built and deployed this loop inside client-controlled AWS. Permissioned memory, governed tools, and a promotion path that earns its way into production. Employees teach the system by doing the work; operators decide what becomes reusable, executable, and safe enough to ship.
Agents act through pathways
OpenClaw, Claude Cowork, or other orchestrating agents use approved tools, drafts, diffs, tasks, and workflows.
Operators teach by operating
The system sees what employees repeat, correct, approve, reject, escalate, automate, and refine.
Ragu promotes what earns trust
Useful patterns move through sandbox, review, skill-file gates, audit, rollback, and production discipline.
The cycle compounds. Every approved pattern expands what the business can ask Ragu to do next.
The output
Operational knowledge compounds instead of disappearing.
The old way is simple: experienced people keep the business moving because they know the exceptions, approvals, workarounds, timing, and repeated moves their role requires. Ragu gives those patterns a path to become permissioned memory, governed action, and reusable AI capability.
Velocity.Efficiency.Productivity.ROI that compounds.
Before RaguKnow-how lives inside repeated work.
Judgment, exceptions, and fixes happen every day, but most of the pattern disappears back into the role.
With RaguThe pattern earns its way into the operating layer.
Observed, proposed, reviewed, promoted, audited, and reused as AI capability.
Ways to engage
Map. Build. Promote.
Map
Identify where AI agents can create the most value in your business, where they create the most risk, and what governance path should come first.
Build
Deploy the private RAG, orchestration, and review platform your company needs before agents can touch real work.
Promote
Bring AI-prototyped, vibe-coded, or vendor-built systems under production discipline: architecture, security, observability, audit, and rollback.
Who it is for
Companies where AI work has consequences.
For enterprises with something to lose
We deploy AI inside businesses that cannot afford to ship the wrong sentence, workflow, commitment, or code path.
For operators serious about agents
If you have built or bought AI tools and need them to behave like part of the business, Ragu is the production layer underneath.
For private equity
Help portfolio companies cut costs, improve efficiency, and turn repeatable operating improvements into governed AI workflows.
BlackTomato
Ragu has been a critical partner in guiding and supporting our AI journey.
Tommy Marchant, Founder and CEO, Black Tomato
Operator-led
Built by an operator who has done this before.
Ragu is led by Robertson Price, a serial founder who has spent 25+ years building through major internet platform shifts: search, streaming video, content networks, e-commerce, blockchain infrastructure, and AI.
His track record includes iWon / Interactive Search Holdings, MyVideoDaily, Answers Network, Rebates.com, and multiple AI ventures. He is a named inventor on granted and pending patent filings related to content identification and biometric-response AI.
Ragu exists because the AI transition is not another software feature. It is a new operating layer, and the companies that navigate it well will pair speed with governance and leverage with control.
More on RobertsonThinking
Production is a discipline, not a state.
FAQ
Questions serious AI buyers ask first.
What does Ragu do?
Ragu is the production layer for AI labor: memory, orchestration, governance, review, promotion, audit, and rollback for agents doing real work.
Is Ragu a product or a services firm?
A services firm with a production-grade platform underneath. We're hired to map, build, and promote AI work inside live businesses. The engagement is delivered by our team and runs on Ragu's own platform — battle-tested RAG, AWS-deployed governance, and the orchestration layer we've spent four years hardening. The platform is what lets us deliver; the team is what you're buying.
What is enterprise RAG?
Enterprise RAG is the memory layer that makes company knowledge AI-readable, permissioned, citable, fresh, and useful inside real workflows.
Why does AI need RAG to accelerate a company?
AI cannot accelerate a business if it cannot understand the business. RAG gives agents the context they need from documents, systems, customer history, policies, and operating data.
What is an AI understudy?
An AI agent that learns beside an operator inside governed context. It can observe patterns, retrieve the right memory, draft work, call approved tools, and propose repeatable improvements while operators stay in control of what gets promoted.
What is an agent orchestrator?
An agent orchestrator turns operator intent into tasks, tool calls, drafts, diffs, and workflow changes while staying connected to governed company memory.
How does Ragu keep agents from freestyling in production?
Ragu routes novel or risky changes through sandbox, engineering review, business review, documented skill-file gates, permissions, audit logs, and rollback paths.
Can Ragu run in a client's AWS environment?
Yes. Ragu's enterprise platform is designed for private AWS deployment patterns and infrastructure that clients can control.
How does Ragu promote AI-prototyped systems?
Ragu takes AI-prototyped or vibe-coded systems through production hardening: architecture review, refactoring, security, testing, observability, governance, and release discipline.
How does Ragu help private equity portfolio companies?
Ragu helps portfolio companies reduce costs, improve efficiency, and turn repeatable operating improvements into governed AI workflows.
For the CTO
Four years of platform code, not a prompt wrapper.
Ragu is the production control plane for enterprise AI agents: the infrastructure beneath governed AI work, built and deployed since 2022.
The machine includes ingestion, ACL-aware enterprise memory, citation-backed retrieval, MCP orchestration, review workflows, skill-file gates, audit trails, rollback, and client-controlled AWS deployment patterns.
Since2022Platform built and deployed inside live businesses
ControlAWSDeployment patterns for client-owned environments
SurfaceAgentsMemory, orchestration, review, audit, and rollback
Platform surfaceThe machine we built to capture operating knowledge.
Ingestion jobs
Documents, tools, email, code, tickets, dashboards
Permissioned RAG
Parsing, embeddings, ACLs, vector indexes, citations
Agent runtime
MCP tools, drafts, diffs, tasks, workflow proposals
Review control plane
Sandbox, skill files, approvals, audit, rollback
Client-controlled AWSTenant-aware servicesSSO / RBAC-ready identityAudit eventsRollback paths
Ingestion pipelines
Connector-driven ingestion for documents, policies, customer context, operating data, code, and tool output, with provenance and source boundaries intact.
ACL-aware memory
Permission-filtered retrieval with citations, source visibility, freshness checks, vector indexes, and reusable operating knowledge.
MCP orchestration
Coordinate assistants, MCP tool contracts, drafts, diffs, workflow proposals, and approved actions from operator intent.
Review workflows
Route risky work through sandbox, engineering review, business review, and documented skill-file gates before promotion.
Audit and rollback
Make AI work observable, attributable, reviewable, and reversible when it touches systems that matter.
Client-controlled AWS
Deploy into environments clients control, with tenant-aware services, identity patterns, security boundaries, and operational ownership.
Let's talk
The companies we work with are not running experiments anymore.
They have customers, regulators, reputations, and operating data they will not put at risk. They also intend to be faster and harder to compete with twelve months from now than they are today.