Service
Eliminate the repetitive decisions, handoffs, and data-shuffling that drain your team's day. AI handles the judgment calls; humans handle what matters.
Midus builds end-to-end automations that go beyond trigger-and-forward. We use n8n, Temporal, and queue-based architectures to orchestrate the moving parts — and drop LLMs into the steps that used to need a person reading, classifying, or deciding. The result is a workflow that actually finishes, not one that dumps a notification in Slack and waits.
Who this is for
Your operations team copies fields between systems, re-enters the same customer info in three places, and spends half their day being a human ETL job. The work is important, but none of it actually requires a person.
Invoices arrive in email. Line items get keyed into the ERP. Exceptions get chased down in Slack. AP runs on spreadsheets held together with willpower. Month-end closes burn nights and weekends because the tooling stops where the judgment starts.
Your CS leads triage each incoming escalation by hand — classify severity, identify the right owner, pull in account context, draft a response. They're good at it. They're also the bottleneck, and the team can't scale without cloning them.
What we build
The components below are what we typically compose together. The exact mix depends on your stack, your data, and where the current process breaks.
Incoming tickets, leads, emails, or documents get classified, prioritized, and sent to the right queue — with the right context attached. No more round-robin assignment to whoever's on shift. The workflow knows what this is and who should see it.
Emails, PDFs, scanned invoices, contracts, onboarding forms. An LLM pulls out the fields you actually need — vendor, amount, due date, PO number, signature status — and pushes structured data into the next step. Works on the messy real-world inputs OCR tools choke on.
Your CRM, ERP, ticketing system, billing platform, and data warehouse all need to stay in sync. We wire them together with reliable queues, retries, and idempotent writes — so an API hiccup doesn't create duplicate records or silent drops.
The spots where traditional automation fails — "is this refund request reasonable?", "does this contract clause deviate from policy?", "which category does this expense belong to?" — get an LLM making the call with your rules and examples as context. Reasoning is logged so you can audit later.
For high-stakes or low-confidence steps, the workflow pauses and asks a human. Approve, reject, edit, or escalate — via Slack, email, or a lightweight web UI. The workflow resumes from exactly where it stopped, with the human's decision recorded.
Every workflow run is tracked end-to-end: inputs, each step's output, timing, retries, errors. When something goes wrong, you can see exactly where and replay from that point with fixed input — no archaeology through logs to figure out what happened.
Outcomes
Example scenarios below reflect the range we typically see. Your numbers will depend on ticket volume, data quality, and how deeply the workflow integrates with your systems.
Support triage
A support org where every escalation waited for a human to classify and route it now sees tickets land in the correct queue within seconds, with account context and suggested priority already attached. Agents spend their time solving, not sorting.
Example scenario.
Invoice processing
A finance team that manually keyed invoices from email into the ERP now receives them, extracts line items, matches against POs, flags exceptions for review, and posts the clean ones automatically. Month-end stops being a crisis.
Example scenario.
Ops headcount
Instead of hiring two more ops coordinators to keep up with growth, the existing team pivots to projects that actually move the business — partnerships, process redesign, customer work — while the automated pipeline handles the throughput.
Example scenario.
How we work
We sit with the team doing the work, map the real (not the documented) flow, find the decision points and handoffs, and measure today's cost — minutes per case, volume per week, error rates. Output is a written plan with realistic ROI math.
We pick the one workflow with the best cost-to-effort ratio and build it end-to-end — extraction, decisions, integrations, human checkpoints, observability. It runs in parallel with your current process first, so you can compare outputs before cutting over.
Once the pilot is stable, we roll to the next workflow and the next. We instrument everything, review exceptions weekly, and tune the LLM prompts and decision rules based on what actually goes wrong — not what we expected to go wrong.
FAQ
Zapier (and Make, Workato, and friends) are great at the easy third: "when X happens, do Y." They fall over on the middle third — the steps that need judgment, extract data from messy inputs, coordinate across long-running processes, or pause for human approval and resume days later. We build on tools like n8n and Temporal for real orchestration, with LLMs doing the steps that used to require a person. You can think of it as the grown-up version of the same idea.
Traditional automation is the right tool when inputs are structured and rules are deterministic — "if status = paid, update the record." AI earns its keep on judgment steps: classifying an ambiguous ticket, extracting fields from an unstructured email, deciding whether a refund request fits policy, summarizing a conversation for the next owner. We use the cheapest tool that solves each step. Most workflows are a mix — deterministic glue with LLM calls at the hard spots.
A few layers. First, confidence thresholds — below a set score, the step escalates to a human instead of guessing. Second, human-in-the-loop checkpoints on anything high-stakes (refunds over $X, contracts with non-standard terms, customer-facing messages). Third, full replay: every run is logged step by step, so when something does go wrong you can see exactly where, fix the input or the prompt, and re-run from that point without starting over. We design for mistakes — they happen in manual processes too.
Almost always, yes. We've integrated with Salesforce, HubSpot, NetSuite, QuickBooks, Zendesk, Intercom, Jira, ServiceNow, Shopify, Stripe, Postgres, Snowflake, and a long tail of in-house systems with REST or SQL access. If a system has an API or a database we can read, we can wire it in. If it doesn't, we'll tell you that up front — sometimes the right answer is to replace or wrap the system first.
For a single well-scoped workflow: 4–6 weeks from kickoff to running in parallel with your current process, another 2–4 to cut over fully. Broader rollouts across multiple processes typically run 3–6 months in phases. We don't do year-long transformation engagements — we ship the first workflow fast and prove value before expanding.
You do. The code lives in your repos, runs on your infrastructure (or a managed n8n/Temporal instance we set up for you), and uses your API credentials. We document every workflow, hand off runbooks, and train your team. Most clients keep us on a light retainer for tuning and new flows; some take it fully in-house. Either works — no lock-in.
Related services
Tell us which process is burning the most hours, what systems it touches, and what "done" looks like. We'll come back with a realistic plan, cost range, and timeline — typically within two business days.