AI Systems · Agentic Workflows · Production Delivery

Build the AI operating layer
your company needs next.

Rodos AI designs, ships, and operates production AI agents, workflow automation, and custom AI systems — connected to the tools your teams already use, with humans in the loop where it matters.

See what we build →

From scoped pilots to multi-team rollouts. How we deliver ↓

What we build

Production AI systems —
not demos, not dashboards.

We design, ship, and operate AI agents and automation that run inside your real environment — connected to the tools your teams depend on every day.

AI agents for real workflows

Goal-driven agents that triage, decide, and act inside your operations — with audit trails and human approval gates.

Support, sales, and ops triage
Engineering and DevOps agents
Internal back-office automation

Cross-tool workflow automation

Connect CRM, ticketing, docs, data warehouses, codebases, and APIs into end-to-end automated processes.

CRM ↔ ticketing ↔ Slack ↔ docs
Data warehouse + LLM pipelines
Webhook + API orchestration

Customer-facing AI features

Production AI products embedded in your app — copilots, assistants, semantic search, intelligent recommendations.

In-product AI assistants
Retrieval and grounded answers
Generative UI and content

Knowledge and decision systems

Turn fragmented internal knowledge into searchable, queryable, agent-accessible context for every team.

Retrieval over docs, tickets, code
Policy- and SOP-aware agents
Decision-support copilots

Legacy modernization without rewrite

Wrap monoliths and legacy tooling with an AI layer that bridges old systems to modern interfaces and integrations.

Adapter agents over legacy APIs
Document and form intelligence
Gradual migration paths

Production AI infrastructure

The plumbing that makes AI safe in production: evals, guardrails, observability, cost control, and rollback.

Eval suites and regression tests
Tracing, monitoring, alerts
Prompt and model versioning

Most engagements ship a first production workflow inside 4–8 weeks — and expand from there as confidence and ROI build.

Architecture

How an AI workflow
actually runs in production.

Five layers, every one of them observable, controllable, and replaceable. No black boxes between your business systems and your customers.

Existing systems

CRM
Tickets
Docs
Data warehouse
Codebase
APIs

Rodos AI layer

Retrieval & context
Tool / function calling
Memory & state
Policy & guardrails

Specialized agents

Triage
Research
Action
QA / review
Domain copilots

Humans in the loop

Approve / reject
Edit & override
Escalation paths
Feedback signals

Systems of record

Writes back to source tools
Audit log of every action
Evals + observability

AuditableEvery action logged

ReversibleRollback by design

ObservableTraces + evals

PermissionedYour IAM, your data

Why teams stall

Most companies have AI ambition.
Few have an AI operating layer.

The blocker is rarely models or budget. It is the gap between your existing systems and the agentic, automated workflows you want running on top of them.

Knowledge stays trapped

Critical context lives in docs, tickets, Slack threads, and code reviews — and the people who hold it become bottlenecks.

Tools don’t actually talk

Your CRM, ticketing, data warehouse, and codebase exchange data through humans copying and pasting between tabs.

AI experiments don’t reach production

Demos and notebooks impress leadership, but never connect to the systems where real workflows live.

We close that gap — design, build, integrate, and operate the AI systems that move work end-to-end through your stack.

How we deliver

Map. Ship. Scale.
With production discipline at every step.

A repeatable engagement model designed for high velocity and low blast radius — so AI moves into your operations safely, and stays there.

01Week 1–2

Workflow map

We connect to the systems involved in the target workflow, study the actual handoffs, and produce an architecture for what AI should and should not do.

Discovery across tools, data, and humans
Risk, security, and compliance review
Prioritized automation roadmap

02Week 3–6

First production workflow

We build the first end-to-end workflow with humans-in-the-loop, evals, and observability — and ship it into production behind safe rollouts.

Agent + tool integration
Eval suite + guardrails
Staged rollout with metrics

03Week 6–12+

Scale the AI layer

Once the first workflow proves out, we expand: more agents, more integrations, more teams — with shared infrastructure and continuous evaluation.

Multi-workflow orchestration
Shared eval + observability layer
Documentation, training, handoff

Engagements run as fixed-scope sprints or ongoing programs — with clear milestones, owners, and exit criteria at every phase.

Outcomes

What changes when AI
actually runs your workflows.

We work with what you already have — your stack, tools, and team. The shift you feel is operational: fewer handoffs, faster cycles, more leverage per person.

Hours/week of repetitive work moved off humans onto agents.

Cycle time on target workflows compresses as handoffs disappear.

AI in production workflows running, monitored, and evaluated end-to-end.

Capacity freed so engineering and operations focus on higher-value work.

Specific numbers depend on workflow complexity, integration surface, and baseline — we set targets together at the start of every engagement and instrument them in production.

See where Rodos fits — who we work with.

Trust posture

Production AI we are
comfortable putting our name on.

We build AI systems the way we would want them running on top of our own business — with controls, audit, and reversibility taken seriously.

Human-in-the-loop by default

Every workflow ships with explicit approval, override, and escalation paths. Agents act inside guardrails, not around them.

Security-conscious by design

We respect your IAM, secrets, and data boundaries. No data leaves systems it should not, and least-privilege scopes are the default.

Observability and evals

Tracing, metrics, and an evaluation suite for every workflow — so regressions surface before users feel them.

Reversible by construction

Actions are logged, batched where possible, and reversible. Rollback is a feature of the system, not a fire drill.

Works inside your stack

No rip-and-replace. We integrate with your existing tools, queues, and data — and hand off code, configs, and runbooks you own.

Built for handoff

Documentation, evals, and dashboards travel with the system. Your team can run, extend, and audit what we build.

Where we fit

We work with teams ready to
operationalize AI, not just experiment.

Rodos AI partners with companies from focused scale-ups through mid-market and enterprise — wherever real workflows, real systems, and real customers are involved.

Operations leaders

Drowning in cross-tool work, manual handoffs, and copy-paste between dashboards.

Engineering orgs

Velocity flat or falling — incidents, configs, reviews, and deploys eating capacity.

Product teams

Shipping AI features into real products with retrieval, evals, and observability.

Founders & GTM

Replacing fragmented experiments with one production AI layer that compounds.

Building something earlier-stage? We have a track for that too →

Why now

The automation gap is
widening, not closing.

The point is not urgency theater — it is leverage. Companies that build their AI operating layer now spend the next few years compounding it, instead of catching up to it.

Compounding advantage

Teams that put AI into production now build a learning loop — every workflow gets faster, every model gets more grounded.

The cost of manual work

Repetitive cross-tool work is a tax that grows every quarter as headcount, tools, and integrations multiply.

AI-native expectations

Customers and employees increasingly expect AI inside the products and tools they use. Catching up later is more expensive than starting now.

You do not need to rebuild everything. You need to start one workflow well. A 2-week workflow map is enough to see what is possible.

Earlier-stage tracks

Building from scratch — or restructuring under pressure?

We run two scoped engagement tracks for teams who are not yet mid-market scale but already need an AI-native foundation.

Ignition

For founders building V1

We help founders architect and ship their first product with AI-native tooling from day one — without hiring a full engineering team to do it.

→AI-native architecture from day one
→Stack and integration selection
→First production workflow shipped
→Evals, monitoring, and guardrails baked in

Formation

For teams preparing to scale (5–50 engineers)

Your product works but the org was built under pressure. We audit, automate, and add the AI layer — right-sized for your stage.

→Workflow + tooling audit
→AI layer on top of existing systems
→Cross-team automation rollout
→Documentation, evals, and handoff

Start the work

Bring us your hardest workflow.

We’ll map what can be automated, what should stay human, and what can ship to production first — across your existing tools, data, and security boundaries. Prefer email? hello@rodos.ai.

RODOSAI

The AI Operating Layer

hello@rodos.ai

Build the AI operating layeryour company needs next.

Production AI systems —not demos, not dashboards.