Proof of Execution
From healthcare and therapy to education, retail, pet care, and enterprise operations, our AI systems have improved efficiency, increased engagement, reduced operational costs, and accelerated growth.Every engagement starts with a business outcome and ends with measurable impact.
Trusted across multiple industries
Each story pairs a quantified outcome with the architecture that produced it. Filter by industry to find the engagement closest to yours. Outcomes are representative and vary by program scope and data.
Challenge
Clinical teams spent excessive time on documentation and progress reporting, pulling hours away from care.
Solution
AI-assisted documentation, a RAG knowledge assistant over guidelines and policies, and workflow automation for reporting.
Technologies
Outcomes
Architecture: RAG + AI Agents + Workflow Automation
Challenge
Therapists carried heavy session-planning and progress-reporting overhead, with goals scattered across documents.
Solution
A therapist-reviewed goal recommendation engine and session planning assistant drawn from each child's history.
Technologies
Outcomes
Architecture: Domain LLM + Recommendation Engine + Human-in-the-Loop
Challenge
Students struggled with one-size-fits-all content and inconsistent practice; educators lacked early signals.
Solution
Adaptive learning paths, an AI tutor and study planner, and student-success analytics with intervention alerts.
Technologies
Outcomes
Architecture: Adaptive Engine + AI Tutor + Outcome Analytics
Challenge
Buyers struggled to discover the right products and providers, dragging conversion and retention.
Solution
A recommendation engine with AI-augmented search and onboarding automation across a two-sided marketplace.
Technologies
Outcomes
Architecture: Recommender + Hybrid Search + Trust & Safety Agents
Challenge
Pet owners disengaged between visits, with care knowledge scattered across providers and channels.
Solution
A personalized AI care assistant spanning feeding, training, behavior, and clinic visits, tied to the care timeline.
Technologies
Outcomes
Architecture: Personalization Model + RAG + Marketplace Loop
Challenge
Manual, multi-step operational workflows created bottlenecks and slow decision cycles across systems.
Solution
Multi-step agentic workflows coordinating tasks across CRM, ERP, and ticketing, with human approval on high-risk actions.
Technologies
Outcomes
Architecture: Planner + Tool-Using Agents + Approval Gates
Challenge
Answers were buried across documents, policies, FAQs, and internal systems, driving repeat queries to staff.
Solution
A RAG assistant grounded in enterprise knowledge with hybrid retrieval, reranking, and permission-aware citations.
Technologies
Outcomes
Architecture: Hybrid Retrieval + Reranking + Permission-Aware RAG
The pattern repeats across engagements — from manual, fragmented operations to unified, automated intelligence.
Business outcomes don't happen by accident — they're engineered, on a predictable timeline with hard outcome gates.
Week 1 — lock the business outcome and success metrics.
Week 2 — reference architecture and data plan.
Weeks 3–5 — build the vertical slice against real data.
Weeks 6–8 — ship to real users; measure against the metric.
Week 9+ — harden, observe, and continuously improve.
Business outcomes don't happen by accident. They're engineered.
The same proven pipeline turns raw data into measurable business outcomes — the pattern behind every engagement above.
We don't reinvent the wheel each time. The same accelerators redeploy across industries — which is why delivery is fast and predictable.
| Capability | Healthcare | Therapy | Education | Retail | Pet Care |
|---|---|---|---|---|---|
| RAG | |||||
| AI Agents | — | — | |||
| Workflow Automation | |||||
| Recommendations | — | ||||
| Predictive Analytics | — | ||||
| Customer Intelligence | — | — | |||
| Knowledge Systems |
These engagement patterns are battle-tested on five AI platforms we build and operate ourselves — not borrowed slides.
AI value lands in specific, trackable places. These are the levers our engagements move most often.
Up to 50% less time on notes and reporting.
Automation removes repetitive manual work.
Up to 75% fewer repeat queries to staff.
Discovery and matching lift conversion.
Up to 2× engagement across surfaces.
Up to 60% faster operations.
Up to 50% faster decision cycles.
Faster acquisition and higher retention.
Representative outcomes from across the portfolio. Actual results vary by scope, data quality, and organizational readiness.
Representative feedback from the teams we've worked alongside.
The biggest surprise was how quickly the team adopted the system — it felt like part of the workflow within days, not months.
We reduced documentation effort significantly while improving visibility into progress for both clinicians and parents.
The AI workflows didn't sit in a sandbox — they became part of daily operations and kept improving after launch.
Hard-won lessons from shipping production AI — the principles that separate systems that stick from demos that don't.
Pick a measurable business metric first; the tech follows the outcome, not the other way round.
Scoped, job-specific assistants beat a do-everything bot that does nothing reliably.
Keep humans in the loop on high-stakes actions — trust is what drives adoption.
The fastest path to better AI is usually better data, not a bigger model.
If you can't trace and measure it, you can't trust it in production.
A used system with three features beats an unused one with thirty.
Most AI work stalls at pilot. Every engagement showcased here reached production — running, monitored, and depended on.
Level 1
Level 2
Level 3
Level 4
Level 5
Every showcased engagement reached production.
Most AI vendors stop at the demo. We start where they stop.
Representative surfaces from production engagements — dashboards, workflow builders, assistants, and operational cockpits teams use daily.
Outcome and usage metrics tied to business KPIs.
The control surface operators run the day from.
Grounded, cited answers inside the workflow.
Agentic workflows with human-approval gates.
Cohort and trend analysis for decision-makers.
Stakeholder apps for the field and the home.
The combination is the moat — product builders and AI engineers who operate real platforms, delivering against outcomes.
For investors, partners, and enterprise buyers: Splendens has repeatedly built, deployed, and scaled production AI across industries — on reusable platforms.
Whether you need AI assistants, RAG systems, workflow automation, customer intelligence, or an industry-specific AI platform, we start with measurable outcomes and deliver production-ready solutions.
Production AI · Measurable Outcomes · Enterprise Scale