AI Workflow Automation That Actually Ships
Richo Systems designs, builds, and maintains production AI workflows for operations teams — turning manual, repetitive, error-prone processes into auditable automated systems. No pilots that die in committee. No black boxes. Just measurable throughput.
Why teams call us
Most AI projects fail at the operations layer, not the model layer
The technology is rarely the bottleneck anymore. What breaks projects is unclear process ownership, brittle integrations, no evaluation strategy, and no plan for the day a model returns something unexpected. Richo Systems starts at the workflow — the actual sequence of decisions and handoffs your team performs — and automates it end to end with observability baked in from day one.
Process-First, Model-Second
We map your real workflow before selecting a single tool. The output is a documented process graph with decision points, exception paths, and owners — valuable even if you never automate a step.
Integration Without Lock-In
Workflows are built on open, portable primitives. Swap model providers, queue systems, or data stores without rewriting orchestration logic or renegotiating your architecture.
Governance From Day One
Every run is logged, versioned, and replayable. We align implementations to recognised risk frameworks so your compliance review is a formality, not a fire drill.
Measured, Not Assumed
Baseline metrics captured before build. Golden-set evaluation suites run on every change. You get a dashboard showing cost per run, latency, deflection rate, and error class distribution.
Human-in-the-Loop by Design
Confidence thresholds route uncertain cases to a review queue instead of guessing. Reviewer decisions feed back as labelled training signal, so accuracy compounds over time.
Handover, Not Dependency
You receive source, runbooks, architecture decision records, and live training. We are engaged because you want us there — never because you can't operate without us.
Context: McKinsey — The State of AI · Gartner AI Research · MIT Sloan Management Review
Capabilities
What we build
Document Intake
Invoices, POs, contracts, claims — classified, extracted, validated against source systems, and routed with a confidence score.
Support Triage
Inbound tickets categorised, prioritised, enriched with account context, and drafted with a suggested reply for agent approval.
Knowledge Retrieval
Retrieval systems over your internal documentation with citation enforcement, freshness checks, and access-scoped results.
Data Reconciliation
Cross-system matching, anomaly surfacing, and exception queues that replace spreadsheet-based month-end grind work.
Reporting Pipelines
Scheduled aggregation and narrative summarisation with deterministic numbers and generated commentary kept strictly separate.
Approval Routing
Policy-aware routing that reads thresholds, delegations, and org structure, then escalates with full context attached.
Evaluation Harnesses
Golden datasets, regression gates, and drift alerts so a prompt or model change can never silently degrade quality.
Legacy Bridging
Adapters for systems with no modern API, using structured scraping, file drops, and scheduled sync contracts.
The Richo Method
Four phases from audit to autonomy
A deliberately short loop. Each phase produces an artefact you own, and every phase has an explicit exit criterion.
Audit
Two weeks of process observation and data profiling. Deliverable: a ranked automation backlog with effort, risk, and annual hours-saved estimates per candidate.
Prototype
The highest-value candidate built against real data in a sandbox. Deliverable: a working workflow plus an evaluation report against your baseline.
Harden
Error handling, retries, idempotency, access control, logging, alerting, and cost ceilings. Deliverable: production deployment with runbook.
Scale
Templating the pattern across adjacent processes and training your team to build independently. Deliverable: internal capability, not a retainer.
Outcomes
What changes in the first quarter
These are the changes clients report most consistently within ninety days of a first production workflow.
- Cycle time collapses. Multi-day queues become same-hour turnarounds.
- Error rates drop and become visible. Mistakes surface as logged exceptions, not customer complaints.
- Capacity is freed, not cut. Teams move from processing to exception handling and improvement work.
- Cost per transaction becomes measurable. You can finally answer "what does one of these actually cost us?"
- Institutional knowledge is captured. Rules that lived in one person's head become executable logic.
- Audit readiness improves. Every decision has a timestamped trail with inputs, outputs, and version references.
Is this a fit?
Richo Systems works best with teams who recognise at least three of the following:
- A process runs more than fifty times a week and follows written or unwritten rules.
- Staff routinely copy data between two systems that should talk to each other.
- A spreadsheet is load-bearing infrastructure for a core business function.
- Backlogs grow whenever volume spikes, and the only lever is headcount.
- A compliance or audit requirement demands a paper trail that doesn't currently exist.
- A key person holds critical process knowledge with no documented backup.
Client outcomes
What teams say after going live
"We went from a 3-day invoice processing backlog to same-day clearance. The workflow has run 14,000 times without a single unhandled exception."
"The audit alone was worth the engagement. We discovered three processes we thought were automated that were actually being done manually by one person."
"What I valued most was the evaluation harness. We can now change prompts with confidence because we know immediately if quality regressed."
Common questions
Frequently asked questions
Do we need to have our data in order before starting?
No. The audit phase is specifically designed to assess data readiness alongside process readiness. We have worked with teams whose source data was entirely in email threads and PDF attachments. Data quality issues become part of the backlog, not a prerequisite.
Which AI models and platforms do you work with?
We are model-agnostic. Current engagements use OpenAI, Anthropic, Google Gemini, and open-weight models depending on latency, cost, and data-residency requirements. Orchestration is built so the model layer is swappable without rewriting business logic.
What does a typical engagement cost?
The free workflow audit is a fixed-scope, no-cost engagement. Prototype and production phases are scoped after the audit, so pricing is based on actual complexity rather than a catalogue rate. Most first-workflow engagements land between $18,000 and $55,000 AUD depending on integration depth.
How do you handle sensitive or regulated data?
Data handling requirements are captured in the audit and drive architecture decisions — not retrofitted. We have delivered workflows under Australian Privacy Act obligations, healthcare data requirements, and financial services regulatory constraints. All processing options, including fully on-premises deployment, are available.
What happens after the workflow is live?
You receive full source code, architecture decision records, runbooks, and a handover session. Ongoing support retainers are available but optional. The goal is that your team can operate and extend the workflow independently.
Explore further
Everything you need to get started
Ready to start?
Book your free workflow audit
A two-week, no-cost engagement that produces a ranked automation backlog, data readiness assessment, and a clear picture of what's worth building first. No sales pitch. No obligation.