Resolution-grade automation
Order, billing and account actions executed — not just answers recited.
Customer Support · Case Study
AI Customer Support Agent
A support agent that resolves routine tickets, escalates cleanly, and stays aligned with product and policy updates.
- 70%of tickets resolved automatically
- +11 ptsCSAT on automated flows
- 24/7coverage without night shifts
Delivered
What we built
Automated 70% of support tickets with grounded enterprise knowledge.
Grounded answers
Product and policy responses cited from current documentation.
Clean escalation
Humans receive full context, conversation history and a suggested path.
CSAT instrumentation
Per-flow satisfaction tracking gating every scope expansion.
Inside the build
Deflection was the wrong goal
A subscription commerce company's chatbot deflected customers into rage. We rebuilt around a different metric: resolution.
The challenge
A chatbot customers had learned to bypass
The incumbent bot answered from a stale FAQ and couldn't touch order systems. Customers typed 'agent' immediately; support costs kept climbing with volume, and CSAT on bot interactions was dismal.
- Bot resolution rate under 15%
- No access to order or billing systems
- Support headcount scaling linearly with growth
Summarize open claims above $50k and draft escalation emails.
query_claims_db
draft_email
✓ policy_check
Found 12 claims. 3 need escalation — drafts are ready for your review.
Human approval requiredReview →
The solution
An agent wired into the systems that matter
Connected to orders, billing, shipping and the current knowledge base, the new agent checks status, processes changes, issues policy-bounded credits and confirms outcomes — escalating gracefully with context when judgment or empathy is needed.
- Live actions in order and billing systems with guardrails
- Policy-bounded authority with approval escalation
- Sentiment triggers routing to humans early
SalesforceSlackSAP
JiraSSnowflake
ServiceNowTeamsDrive
OAuthSSOPermissions-awareReal-time sync
The impact
Resolution up, costs down, customers calmer
Seventy percent of contacts now close without human touch — and CSAT rose eleven points on those flows, because problems actually get fixed. Peak season absorbed a 3× volume spike without seasonal hiring.
- 70% automated resolution within two quarters
- CSAT +11 on automated conversations
- Peak-season spike absorbed at flat headcount
99.9%Uptime
1.2sp95 latency
$0.021Cost / task
QualityCostLatencyDrift
Outcomes
What changed for the business
Cost per contact
Down 55% blended across all channels.
Response time
Instant, at 3am, in every supported language.
Agent experience
Humans handle interesting problems, not password resets.
Voice of customer
Every conversation mined for product signal.
Seasonal resilience
Black-Friday volume without the hiring scramble.
Expansion path
Same architecture now rolling into voice.
Delivery
From challenge to outcome
We publish the architecture decisions, delivery approach and measurable results — so you can see how similar outcomes could apply to your enterprise.
- Measurable KPIs
- Architecture transparency
- Cross-industry patterns
- Repeatable playbooks
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1
Challenge
Business pressure, legacy process and constraints.
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2
Solution
Architecture, agents and integration design.
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3
Delivery
Build, evaluate, secure and launch.
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4
Impact
Efficiency, cost, quality or revenue outcomes.
The old bot deflected; this one fixes things. Customers noticed the difference immediately — our CSAT on automated chats is now higher than on human ones.
Under the hood
The stack behind the agent
Integrated with the client's commerce and support platforms in AWS.
- Anthropic Claude
- OpenAI
- Zendesk
- Shopify
- Stripe
- Twilio
- Pinecone
- AWS
- LangGraph
- Datadog
FAQ
Common questions
How did you avoid the deflection trap?
By making resolution the gated metric: flows only automate when they can complete the task end-to-end, and CSAT per flow gates every expansion. Deflection without resolution was explicitly banned.
What stops it issuing refunds it shouldn't?
Policy guardrails encode hard limits — credit ceilings, verification steps, category exclusions — enforced in the action layer, with human approval above thresholds and full audit below them.
How fast could we see similar results?
First automated flows typically ship in six to eight weeks; reaching 70% resolution took this client two quarters of gated expansion.
Next step
Want support that resolves?
Share your top ten contact types — we'll map which ones automation can fully close.