AI That's Already Running. At Scale.
A curated view into how Seronia AI Solutions has designed, deployed, and scaled production-grade AI for teams that can’t afford experiments—only outcomes.
RESULT 01
Automated Triage for a Global Support Organization
Customer support at 12M+ tickets/year, with response SLAs collapsing under manual triage.
High-volume SaaS • Customer Support
Situation
A public SaaS company with a globally distributed support team was manually reading and routing every incoming ticket. Response times were slipping, high-value customers were stuck in the same queue as free users, and managers had zero real-time visibility into what was breaking in the product.
What we built
A production-grade triage layer that reads every inbound ticket, classifies intent and severity, and routes it to the right queue in under 400ms.
- Fine-tuned language model on 4 years of historical tickets and resolutions.
- Multi-label classifier for product area, severity, and customer segment.
- Policy-aware routing into existing Zendesk queues with no agent workflow change.
- Real-time risk flags for VIP customers and churn-risk accounts.
- Analytics layer exposing emerging issues and deflection opportunities.
Functional impact
- Support leadership sees, in real-time, what is breaking and where.
- Sales can proactively reach out to at-risk accounts before churn signals hit CRM.
- VIP and enterprise customers are auto-prioritized with strict SLA enforcement.
- Agents start on context-rich tickets instead of low-signal triage work.
- Ops team has a measurable lever to trade-off speed, cost, and quality.
Key numbers
68%
reduction in manual triage workload within 6 weeks.
3.4x
faster first-response time for top-tier accounts.
94%
routing accuracy on benchmarked labeled data.
11%
incremental CSAT uplift across prioritized queues.
RESULT 02
Revenue-Linked Recommendations for a B2B Marketplace
Enterprise marketplace • Personalization & Pricing
Situation
A B2B marketplace with a catalog of 200k+ SKUs was surfacing "most popular" items instead of context-aware offers. High-intent buyers were getting generic suggestions, average order value had plateaued, and merchandising teams were manually curating key pages every week.
What we built
An AI recommendation engine that optimizes not just for click-through, but for realized revenue and margin at the account level.
- Unified behavioral, transactional, and pricing data into a single feature store.
- Context-aware embeddings capturing account, seasonality, and contract terms.
- Real-time re-ranking API powering homepage, PDP, and cart suggestions.
- Experimentation framework to A/B test policies across geos and segments.
- Dashboard tying recommendation behavior to revenue, margin, and churn.
Key numbers
+19%
lift in average order value from recommendation-driven orders.
27 days
to first positive revenue signal in production experiments.
6 hrs/week
saved per merchandiser by removing manual page curation.
RESULT 03
Risk Scoring for a Fintech Underwriting Team
Fintech lender • Risk & Operations
Situation
A fast-growing fintech lender was relying on a mix of rules and analyst judgment to underwrite SMB applications. Approval times spanned days, risk was highly variable across analysts, and the operations team had no clean way to trace decisions back to data.
What we deployed
A credit risk scoring system that scores every application in seconds, with transparent features and guardrails that regulators and risk officers can inspect.
- Feature-engineered bureau, banking, and behavioral data into a unified applicant profile.
- Gradient-boosted models calibrated to existing risk appetite and loss targets.
- Explainability layer exposing top features driving each score for auditability.
- Dual-track workflow: auto-approve low-risk, route edge cases to senior analysts.
- Scenario simulator letting leadership adjust policy and see portfolio impact.
Outcome snapshot
Underwriting that scales faster than demand.
The risk team moved from opinion-based debates to portfolio-level trade-offs with measurable impact on loss rates and growth. New credit products now launch with an AI-native underwriting approach by default.
RESULT 04
Agent Copilot for Inside Sales
B2B SaaS • Sales Enablement
Situation
An inside sales team was juggling dozens of tabs—CRM, knowledge base, pricing sheets—while live on calls. Ramp time for new reps exceeded 90 days, talk tracks varied wildly, and leadership had no consistent way to inject new messaging into live conversations.
What we delivered
A real-time sales copilot that listens to conversations, surfaces the right content, and drafts follow-ups automatically—embedded directly into the existing sales stack.
- Live transcript and intent detection on every call with sub-second latency.
- Contextual snippets from battlecards, case studies, and pricing docs surfaced inline.
- Next-best-question suggestions tuned for discovery, not interrogation.
- Automatic call summaries pushed into CRM with key risks and opportunities.
- Personalized follow-up email drafts that reflect the actual conversation.
- Team-wide insight layer showing which talk tracks correlate with wins.
Key numbers
24%
increase in win rate on opportunities touched by the copilot.
−32%
reduction in ramp time for new reps to hit target productivity.
90 sec
average time saved per call on post-call documentation.
Ready to put AI to work in your organization?
AI that's deployed. Measured. Running at scale.
Seronia AI Solutions
1678 Montgomery Hwy Ste 104
Hoover, AL 35216
659.204.1960