Customer service and operations, handled end to end
Built to act. Not just to answer.
Sigmoid Analytica builds AI systems that understand customer service and operational requests, retrieve the right policy or business context, and take real action in your platforms: returns processed, addresses updated, tickets resolved.
Most support automation stops at the reply. The work still happens manually.
The real cost is the lookups, eligibility checks, and operational actions that follow every customer interaction. That's what we automate.
Agents spending hours on requests that follow the same pattern
Return eligibility checks, refund lookups, order status queries, each handled manually, dozens of times a day.
Chatbots that explain your policy but cannot apply it
A bot that tells a customer your 30-day return window is not the same as a system that processes the return.
Inconsistent handling of policy-based requests
Without structured automation, the outcome depends on who responds, how busy they are, and whether they checked the right policy version.
No safe way to expand automation without losing control
Teams want to reduce manual work, but need confidence that the system will respect business rules before acting.
Escalation happens late, if at all
Without structured triage, urgent issues queue behind routine ones. By the time they surface, the customer has already contacted you twice.
Operational work grows linearly with volume
When every return, cancellation, and address change requires a person, headcount and ticket volume scale together.
The workflows your team handles every day
Predictable requests with defined rules: returns, cancellations, address changes, policy responses. These are exactly what structured AI systems handle well.
Return and refund requests
Classify the request, check the order against your return policy, initiate the refund or generate a return label, and send a response based on your policy.
Example: 30-day eligibility checked automatically, label issued in Shopify
Order cancellations
Check fulfilment status, apply cancellation eligibility rules, and confirm or reject with an accurate response ready to send or sent automatically.
Example: Post-dispatch cancellation blocked; escalation draft prepared
Shipping address updates
Verify the change window, authenticate the customer, and update the address in the connected fulfilment system before dispatch.
Example: Address updated in Shopify; confirmation sent automatically
Support triage and routing
Classify incoming tickets by type, urgency, and required action. Route to the right queue or trigger a resolution workflow.
Example: Billing disputes surfaced ahead of routine delivery queries
Response drafting from your policy docs
Retrieve the relevant section of your policy, draft a response using that language, and queue for review or send automatically.
Example: Warranty claim answered with exact policy clause cited
Authenticated actions in external systems
Execute real operations in your connected platforms, including ecommerce, helpdesk, and CRM, with the right permissions and a full audit trail.
Example: Refund issued via API, ticket closed, customer notified
What a return request looks like, end to end
From the first message to confirmed action. This is what a fully automated return looks like. Every step logged. Every decision built from your policy.
Customer emails: 'I want to return my order'
System identifies a return request for order #4821
Fetches order details and your 30-day return policy
Order placed 12 days ago, within the return window. No exceptions flagged.
Initiates return label in Shopify. Updates fulfilment record.
Drafts confirmation with return instructions. Auto-sent per workflow rule.
Total time: 2.1s • Actions taken: 3 • Agent involvement: none
Control
The system runs within your rules. Not outside them.
The most common concern with automation is losing control of what gets actioned and when. We address that with real constraints, not just reassurances.
Co-pilot mode by default
Start with every response and action queued for human review. Expand automation only when you have verified the output meets your standard.
Approval thresholds you define
Set rules for which actions trigger review, by value, request type, or customer segment. Refunds over £150 reviewed. Routine label generations automated.
Built from your policy docs, not guesswork
The system doesn't make things up. It looks up the relevant section of your policy documentation before drafting any response.
Full audit trail, always on
Every decision logged: what was retrieved, what was checked, what action was taken, and who approved it. Queryable and exportable.
More than a chatbot. More than a rule engine.
Standard chatbots handle questions. Rule engines follow fixed branches. Sigmoid Analytica builds systems that reason through multi-step work using your actual policies, then take action.
Retrieves your policy before responding
Every response is built from your actual documentation. The system checks the relevant policy section against the request before drafting anything.
Takes real operational action
It doesn't stop at drafting a reply. It can update records, issue refunds, change shipping details, and close tickets in connected systems.
Keeps you in control at every step
Define which actions require human approval. Set thresholds by value, risk, or request type. Every action is logged. Nothing runs outside your rules.
Handles multi-step workflows, not just single questions
A return request involves classification, eligibility checking, action, and confirmation. The system handles the full sequence, not just the first reply.
Representative result
~71% of return requests resolved without agent involvement
A direct-to-consumer apparel retailer processing 3,000+ return requests monthly. Each handled manually, each requiring a policy check and order lookup. We automated the full workflow. Automated resolution time dropped from 18–22 minutes to under 3 minutes.
Read the full scenario~71%
Tickets automated
< 3 min
Automated resolution
100%
Policy-based responses
0
Additional headcount
If your team handles the same requests every day, we should talk.
Tell us what you're trying to automate. We'll tell you whether it's a fit, and what's realistic.