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 business capability you can keep.
Every employee can now spot friction and ask agents to reshape workflows, tools, documents, and code. Most enterprises keep those taps closed because the output can be sloppy or dangerous. Ragu lets the creative flood reach a sandbox, then promotes what earns trust into your Business Operating System.
Sandbox to shipA review path before agents touch customers, data, or revenue
Deployed since 2022RAG, orchestration, and governance hardened in live businesses
Your AWS, your dataDeployment patterns for environments clients control
Gates before productionEngineering, business, and documented skill-file gates for work moving toward production
The problem we named
The Promotion Gap
The hidden opportunity cost of keeping employee-and-agent invention bottled up.
Open the creative floodgates. Keep production governed.
Ragu gives every employee and agent room to generate aggressively in the sandbox, then filters, hardens, and promotes their best work into your Business Operating System.
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.
Faster cycle time.Reusable work patterns.Lower operational drag.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.
How Ragu helps
Map. Build. Promote.
Each engagement turns AI ambition into a decision-ready operating plan: what to test now, what to govern before scale, and what deserves production next.
Map
Start inside the work: leadership alignment, stakeholder sessions, system and data review, user pain, and risk posture. Ragu inventories AI opportunities and decides which workflow becomes the first working surface.
Build
Deploy the memory, agent pathways, integrations, and review surface needed to test real work with real users. Ragu measures time saved, output quality, adoption signals, failure modes, and promotion readiness.
Promote
Move the best work toward production through governance, data access controls, approval paths, security review, hardening, rollout, audit, and rollback. Quick wins and foundational work stay sequenced.
What an engagement produces
Decision-ready output, not just a promising demo.
A repeatable model for comparing initiatives by value, feasibility, readiness, cost, risk, data dependency, and user demand.
A real assistant, workflow, or operating surface tested with the people closest to the work before anything touches production.
Performance, output quality, adoption signals, failure modes, integration gaps, and promotion requirements.
A sequenced path for governance, access, ownership, rollout, audit, rollback, and the next promotion tranche.
The decision system
A repeatable system for what to test, fund, govern, and scale.
Ragu gives leadership the model underneath AI decisions: compare opportunities, measure early evidence, understand the control surface, and pace adoption around how teams actually change.
Turn ideas into choices.
Ragu gives leadership a repeatable decision model for value, feasibility, readiness, cost, risk, data dependencies, and user demand.
Test what actually changed.
Early working surfaces are evaluated for performance, output quality, utility, adoption signals, failure modes, and promotion readiness.
Know what scale will require.
The path to production includes data classification, access controls, approval paths, privacy posture, deployment tradeoffs, and ownership.
Make change take root.
Visible wins, protected testing, and careful rollout pacing help teams move past legacy fatigue, firefighting culture, and perfection paralysis.
The Proof
Case in production
BlackTomato
The Feelings Engine
Black Tomato operates in high-touch luxury travel, where AI work has to respect customer context, brand voice, and operator judgment. Ragu built the AI tooling behind the Feelings Engine — an emotion-led trip-planning experience running in production.
+13%Conversion rate uplift
6,500+Personalized prompts in production
2025Travel Marketing Awards — judged a first for the industry“Ragu has been a critical partner in guiding and supporting our AI journey. Our relationship began with Ragu creating AI tools to help make our team more effective and continues to go from strength to strength.”
Tommy Marchant, Founder and CEO, Black Tomato
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
We work with portfolio companies on operating leverage — turning repeatable improvements into governed AI workflows that survive the management team that ordered them.
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.
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.
What is the Promotion Gap?
The Promotion Gap is the distance between what a company can now imagine with AI and what it can safely keep in production. Ragu engineered the first system to close it — turning an employee's intent and an agent's work into governed proposals that earn their way into production.
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 happens first?
Ragu starts by mapping the operating knowledge, value drivers, data boundaries, agent pathways, and production risks already inside the business. The output is a decision framework, first working surface, test plan, and roadmap for memory, orchestration, review gates, and promotion.
How does Ragu decide which AI initiatives should go first?
Ragu builds a decision framework around value, feasibility, readiness, cost, risk, data dependencies, user demand, and the customer or employee outcome that matters. Leadership gets a repeatable way to compare ideas instead of a pile of disconnected pilots.
How does Ragu measure whether early AI work is worth scaling?
Promotion readiness comes first. Ragu evaluates early work across performance, output quality, utility, adoption signals, failure modes, integration gaps, and the governance required before scale.
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.
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 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
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.