Grounded, Not Generic
Agents and Now Assist reason over your knowledge, CMDB, and records — not the open internet — so answers stay accurate and relevant.
Ramisun implements ServiceNow Predictive Intelligence to bring machine learning into your workflows — auto-categorising, prioritising, and routing work from patterns in your own historical data. Paired with AI Search, users and agents get semantic results that understand meaning, not just keywords. The platform gets measurably smarter: faster routing, fewer misassignments, and answers found the first time — all from data you already have.
Predictive Intelligence & AI Search is ServiceNow's built-in machine learning and search intelligence. Predictive Intelligence trains models on your historical records to automatically categorise, prioritise, and route work, and to predict likely outcomes. AI Search adds semantic understanding, returning results by meaning rather than exact keywords. Ramisun trains, tunes, and monitors these models on your data — so routing gets faster and more accurate, answers surface the first time, and the platform keeps learning as new data arrives.
Each capability maps to a specific ML or search capability — with real outcomes.
Ramisun trains ML models on your historical records to auto-categorise and route incoming work, removing manual triage and the misassignments that slow resolution.
We build models that predict priority, likely resolution path, and risk — so the right work gets attention early and outcomes are anticipated, not discovered late.
Ramisun deploys AI Search so users and agents get results by meaning, not exact keywords — finding the right knowledge article or record the first time.
We enable similar-incident detection and resolution recommendations, so agents benefit from every problem the organisation has solved before — institutional memory on tap.
Static rules age badly; models trained on your data adapt and improve.
From your historical data to smarter, self-improving workflows.
Models learn from your historical records
Accuracy tested before go-live
New work auto-categorised and prioritised
ML assigns work to the right team
Semantic AI Search surfaces the right answer
Models retrain as new data arrives
Hand-maintained rules can't keep up with how work actually changes.
| Area | Static Rules & Keyword Search | ServiceNow AI + Ramisun |
|---|---|---|
| Categorisation | Manual or brittle rule sets | ML auto-classifies from real patterns |
| Routing | Rules that drift out of date | Predictive assignment, continuously accurate |
| Prioritisation | One-size-fits-all rules | Predicted priority from your data |
| Search | Keyword-only, misses synonyms | Semantic search understands meaning |
| Agent Speed | Reinventing past solutions | Similar-incident recommendations |
| Maintenance | Constant rule upkeep | Models retrain automatically |
| Accuracy | Guesswork, unmeasured | Measured, monitored accuracy |
An out-of-box-first, evolutionary approach that ships value from the first sprint — with policy, audit, and human oversight built in.
Agents and Now Assist reason over your knowledge, CMDB, and records — not the open internet — so answers stay accurate and relevant.
Working AI capabilities every two weeks. You see summarisation, then deflection, then agentic action go live incrementally.
Every skill and agent ships with policy controls, escalation paths, and human checkpoints — autonomy earned scenario by scenario.
Deflection, resolution time, adoption, and CSAT are tracked from day one — so AI value is proven, not assumed.
Every AI action is logged with model version and confidence via the AI Control Tower — provable governance for any reviewer.
Training and change management are built into delivery — so your teams trust and actually use the AI you deploy.
We partner with industry leaders to drive meaningful transformation.
“Ramisun didn't just implement ServiceNow — they redesigned how our teams work. Their AI agents now handle most routine requests.”
“Their marketplace expertise was the difference. We went from prototype to a certified ServiceNow Store listing our team could sell.”
“Transparent, agile, and genuinely business-first. Every sprint ended with something we could show our executives.”
“The CMDB work alone paid for itself. We finally trust our configuration data enough to automate against it.”
“Hypercare was real hypercare. Go-live week felt boring in the best possible way.”
“Ramisun didn't just implement ServiceNow — they redesigned how our teams work. Their AI agents now handle most routine requests.”
“Their marketplace expertise was the difference. We went from prototype to a certified ServiceNow Store listing our team could sell.”
“Transparent, agile, and genuinely business-first. Every sprint ended with something we could show our executives.”
“The CMDB work alone paid for itself. We finally trust our configuration data enough to automate against it.”
“Hypercare was real hypercare. Go-live week felt boring in the best possible way.”
An implementation is only as good as the consulting behind it. When the platform mirrors how your business actually works, adoption follows — and so does ROI.Vinnay Nigam, Founder & CEO, Ramisun
Predictive Intelligence is ServiceNow's built-in machine learning. It trains models on your historical records to auto-categorise, prioritise, and route work, predict outcomes, and find similar past incidents — making workflows smarter without hand-maintained rules.
AI Search uses semantic understanding, returning results by meaning and intent rather than exact keyword matches. A user searching in their own words still finds the right knowledge article or record — dramatically improving first-try success and self-service.
No. Predictive Intelligence is designed for configuration rather than coding, and Ramisun handles model training, validation, and tuning. Your team gets the benefits of ML without needing to build or maintain models themselves.
Generally a few thousand well-labelled records per category produce reliable models, though it varies by use case. We assess your data during scoping and, where needed, help improve labelling so models perform well.
Yes. Models can be retrained on new data on a schedule, so accuracy improves as patterns evolve. We set up monitoring so you can see model performance and retrain before accuracy drifts.
A focused categorisation-and-routing or AI Search deployment typically shows value in 6–10 weeks. We validate accuracy before go-live and phase additional models on top.
Responsible AI adoption needs guardrails. Our delivery model is designed around security, auditability, and compliance from the first workshop.
Delivery processes mapped to SOC 2 trust principles.
Information security management across every engagement.
Privacy-by-design data handling and processing controls.
Policy controls, auditability, and human oversight for every AI agent.
Every quarter without the right platform is revenue and efficiency left on the table. Tell us your challenge — get a real plan back, not a sales script.
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