Classify, extract, route, write back. The agent has a job with a system of record.
AI automation
Agents that classify, route,
and act in your tools
Workflows that extract, decide, and write back to CRM, ERP, and internal APIs — with approval where the cost of being wrong is high.
Payments, legal, and customer-visible writes wait on approval. Everything else can run.
Retries should not duplicate invoices or tickets. We design for failure, not the happy path demo.
Every tool call is attributed. Repositories and runbooks transfer at handover.
- HITLApproval on irreversible steps
- APIsCRM, ERP, internal tools
- 6–12 wksTypical production delivery
- 100%Code & IP ownership
Queues we empty
Automation that replaces a queue, not a person
The wins are repetitive, high-volume, and already sitting in email or a spreadsheet. We automate those first.
Queue 01
Invoice and PO capture
Extract, match, and post — with a person on mismatches instead of a shared mailbox.
Queue 02
Lead and ticket triage
Classify, enrich, and assign before the SLA clock runs out.
Queue 03
Contract and onboarding packs
Pull fields, open the checklist, and block go-live until the missing doc arrives.
Queue 04
Internal ops agents
A constrained agent that looks up, drafts, and waits — then writes when you say so.
Human in the loop
What runs alone, what waits, what never auto
Automation without a risk map is how you get silent duplicate invoices. We classify steps before the first tool call.
Runs alone
Reversible & low-risk
Classify, enrich, draft, and route. Retries are safe because nothing customer-facing or financial has moved yet.
- Ticket triage
- Field enrichment
- Draft replies
- Queue assignment
Needs a human
High cost of being wrong
Money, legal, and customer-visible writes wait in an approval inbox with the source file and proposed fields.
- Invoice posting
- Contract send
- Refunds
- Account changes
Never auto
Hard stop list
Some actions stay out of the agent entirely. We encode that as policy — not a prompt the model can ignore.
- Wire transfers
- PII export
- Delete production data
- Legal notices
Workflow nodes
What AI automation includes
Agents and pipelines that sit on top of the systems you already run. If a rules engine or a Zap is enough, we will say so.
Intake
Document and data extraction
Invoices, POs, emails, and PDFs into structured fields with confidence scores and a review queue for the rest.
- Layout-aware extraction
- Schema you control
- Exception queue for ops
Decide
Classify and route
Tickets, leads, and documents to the right queue, owner, or playbook — with an explanation the assignee can trust.
- Routing rules + model scores
- SLA-aware queues
- Override that trains the next version
Act
CRM, ERP, and internal APIs
Create, update, and close records through the APIs you already have. No shadow database the finance team cannot see.
- Salesforce, HubSpot, SAP, custom
- Idempotent writes
- Dry-run mode before go-live
Agents
Tool-using agents
Multi-step jobs with a planner, tools, and a stop condition. Not an unbounded loop that emails your customers at 3 a.m.
- Allow-listed tools
- Step budgets and timeouts
- Kill switch and replay
Control
Human-in-the-loop
Approval screens for money, legal, and customer-facing actions. Bulk accept for the obvious cases once the error rate is known.
- Approval inbox
- Dual-control on high-risk tools
- Metrics on auto vs reviewed
Ops
Monitoring and replay
You can see which step failed, with which payload, and replay it. Silent drift is how automation gets turned off.
- Per-run traces
- Alerting on tool errors
- Replay from the last good step
Systems of record
Write back where your team already works
Agents sit on top of CRM, ERP, billing, and internal APIs — with your auth, not a parallel database finance cannot see.
Salesforce
HubSpot- ServiceNow
- Oracle
- Stripe
AWS
Postgres
Gmail
Custom REST and SQL are fine too. We map the system of record first, then allow-list the tools the agent may call.
After go-live
Outcomes that hold up past the demo
The goal is not a flashy agent screen. It is a quieter queue, inspectable runs, and an auto-rate you can defend.
Cycle time on the queue
Intake that sat in a shared mailbox becomes structured records with an owner before the SLA clock burns.
People on the hard cases
Ops spend time on mismatches and edge cases — not retyping the same invoice fields for the hundredth time.
Failures you can inspect
Every run leaves a trace. When a tool call fails, you see the payload, fix the mapping, and replay from the last good step.
Auto-rate you can grow
Start narrow. After error rates are known, expand the allow-list with numbers — not a hope that the model got smarter.
Delivery
From the first call to code handover
Discuss requirements
A short call on the decision you need, existing systems, data, and whether custom AI is the right spend. Mutual NDA first.
→Define the solution
Architecture, scope, timeline, and cost in a written proposal — including evals, cloud, and what you will own at handover.
→Develop the solution
Engineers build and test with regular working releases on shared staging. You see retrieval quality, vision accuracy, or agent tool use on real examples.
→Deploy and handover
We deploy into your cloud, transfer repositories, documentation, and IP, and leave monitoring so the system does not silently drift.
Case studies
Related work we have shipped
Education · Retrieval + matching
AI Learning Platform
Students find experts, book live sessions, and learn on their schedule — Next.js, Python, and AWS behind the marketplace.
- RAG
- Knowledge + matching
- AWS
- Production cloud
- Web
- Full-stack app
Marketplace · Intelligent matching
My Guru
Expert sessions and paid live events where recommendations and scheduling have to feel instant on a phone.
- Match
- Expert routing
- Live
- Paid sessions
- Mobile
- Native-class client
Before we start
What makes an automation project ready
If these are missing, we will say so on the first call — better than building against a queue nobody owns.
A named queue owner
Someone who lives in the inbox or spreadsheet today and can say what “done” looks like for a single item.
System of record access
Sandbox credentials for CRM, ERP, or the API that must receive the write — not a parallel shadow database.
Sample volume
A few dozen real documents or tickets (redacted is fine). Templates and edge cases beat a polished demo PDF.
Risk classification
A short list of what may auto-run, what needs approval, and what must never be automated.
Why Zupitrix
Tangible reasons to choose Zupitrix
Senior engineers only
The people who scope the project build it. You are not handed off from a sales deck to a rotating bench.
Honest feasibility
If a SaaS tool or a rules engine is enough, we say so on the first call. Custom AI has to earn the spend.
Your code and IP
Repositories, prompts, eval sets, and models we train for you sit in your accounts. No vendor lock-in tax.
Direct communication
Backlog, pull requests, and weekly demos. You always know what shipped, what is blocked, and why.
FAQ
Questions about ai automation
Need ai automation?
Show us the queue you want emptied
We will look at volume, systems, and where a human must stay — then say whether an agent, a RAG assistant, or a rules engine is the right spend.
Keep exploring
Related AI services
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