Repetitive tier-one volume
A large share of tickets were variations of the same handful of questions, each consuming an agent's full attention for a routine answer.
B2B SaaS
An AI layer that handles the tickets a support team dreads
A fast-growing SaaS company's support team was drowning in repetitive tier-one requests. We built an AI automation layer that triages, answers, and resolves the routine tickets — escalating everything else into a custom CRM with the full context attached.
Illustrative case study. This engagement is representative of the work we do and the way we do it — it is not a named client. Figures describe the class of outcome our engineering targets, not a specific measured result. Approved client case studies replace these before launch.
Project summary
AI-assisted customer support
An AI automation layer that triages and resolves repetitive tier-one support tickets, escalating the rest into a custom CRM with full context.
IndustryB2B SaaS
Project typeAI automation & custom CRM
TimelinePhased, expanded as trust grew
TeamCross-functional delivery team
DeploymentCloud, API-integrated
Client background
Support cost scaling faster than revenue
The client is a B2B SaaS company whose customer base was growing quickly. Support volume was growing with it — but the team could not hire fast enough, and the work that arrived was overwhelmingly repetitive: password resets, plan questions, how-do-I tickets, and status checks.
Every one of those tickets still went through a person. Skilled agents spent their day on questions the product had already answered somewhere, while the genuinely complex problems — the ones only a human could solve — waited in the same queue. Response times were slipping, and the cost of support was climbing faster than revenue.
The challenge
A queue full of work the team shouldn't be doing
The problem was not effort — the team worked hard. It was that most of the queue never needed a human at all.
Slow first response
Because everything queued together, customers with simple questions and customers with urgent problems waited the same length of time.
Context scattered across tools
Agents assembled the picture of a customer by hand from several tools, so even simple escalations were slow to act on.
Cost scaling with headcount
The only lever for more support capacity was more people, so support cost rose in lockstep with growth.
Solution architecture
Automate the routine, escalate the rest with context
We designed an AI layer that sits in front of the queue: it resolves what it safely can and hands everything else to a human with the work already done.
01
Intake & triage
Every incoming ticket is classified by intent and urgency, so routine requests and genuine problems are routed differently from the first second.
02
AI resolution layer
For well-understood requests, an AI agent drafts or completes the resolution using approved knowledge sources, with guardrails on what it is allowed to act on.
03
Human-in-the-loop escalation
Anything outside the agent's confidence or authority is escalated to a person — with the customer history, the attempted resolution, and the context already assembled.
04
Custom CRM of record
Escalations land in a custom CRM that holds the unified customer view, so agents act immediately instead of reconstructing the situation.
How we delivered
Shipped cautiously, widened as trust grew
With AI touching customer conversations, we deliberately started narrow and expanded scope only as the results earned it.
Discovery & ticket analysis
We analysed historical tickets to find the safe, high-volume intents worth automating first and the ones that must always reach a human.
Triage & CRM foundation
We built the classification and routing layer and the custom CRM that would hold the unified customer view and every escalation.
Guardrailed AI resolution
We introduced AI resolution for a narrow set of intents behind clear guardrails, with a human reviewing outcomes before scope widened.
Measured expansion
As accuracy and satisfaction held up, we widened the automated set and tuned escalation thresholds against real outcomes.
Technology stack
AI where it helps, deterministic everywhere else
The AI does the language work; the routing, guardrails, and system of record are conventional, testable software.
Frontend
- Next.js
- React
- TypeScript
Why: The agent console and CRM share one typed codebase, so the team works in a single, fast interface.
AI & automation
- LLM API
- Retrieval over approved sources
- Intent classification
Why: Answers are grounded in approved knowledge rather than free invention, which is what makes automated resolution safe to ship.
Backend & routing
- Node.js
- Rules-based triage
- Queue workers
Why: Classification and routing are deterministic and testable, so the AI never decides who to escalate to on its own.
Data
- PostgreSQL
- Prisma
Why: The CRM's customer view and audit trail live in a relational store with typed access.
Integration
- Helpdesk API
- Webhooks
- Product data APIs
Why: The layer connects to the client's existing helpdesk and product so it augments the current stack rather than replacing it.
Observability
- Resolution logging
- Confidence metrics
- Human review queue
Why: Every automated resolution is logged and reviewable, so the team can audit and trust what the AI did.
Business results
The team back on the work that needs them
Routine volume stopped reaching people, and the tickets that did arrive came with the context to act fast.
Routine tickets resolved automatically
A large share of tier-one requests are answered without a person, so the queue that reaches agents is smaller and more meaningful.
Faster first response
Triage means simple questions are answered immediately and urgent problems jump the queue instead of waiting behind them.
Escalations that arrive ready
When a ticket reaches a human, the customer history and attempted resolution are already attached, so agents act instead of assembling context.
Capacity decoupled from headcount
Support can absorb growth without hiring in lockstep, because the routine load no longer lands on people.
Key metrics
The class of outcome we engineered for
These figures are illustrative of the results this kind of automation targets — not a specific measured client result.
64%Tier-one tickets auto-resolved
85%Faster first response
+12Customer satisfaction improvement
Lessons learned
What the engagement taught us
Shipping AI into customer conversations is as much about restraint as capability.
Guardrails earn the scope
Starting with a narrow set of intents and clear limits built the trust — and the evidence — needed to safely widen what the AI handled.
Escalation is a feature
The value was not only in what the AI resolved, but in how well it handed off the rest; a good escalation with context is a great outcome.
Ground every answer
Retrieval over approved sources, rather than open-ended generation, was the design decision that made automated resolution defensible.
In their words
The AI automation they built handles the repetitive work our team used to dread, and it scaled with us instantly. Our agents finally spend their time on the problems that actually need a person.
Services behind this work
The services behind this work
This engagement was delivered through these core capabilities, adapted to a customer support operation.
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