AImpact is the AI-driven L1 support automation platform. Handle high-frequency tickets 24/7, route complex cases with full context, and pilot your service performance from one cockpit.
Three capabilities, one objective: lower support cost while improving speed, quality and visibility.
Classify, understand and resolve recurring L1 requests 24/7 using your knowledge base and support rules.
When automation isn't enough, route to the right L2 team with full context, history and intent already packaged.
Monitor activity, resolution quality and value creation through live support KPIs and a defendable ROI view.
Less repetitive workload. Faster first response. Cleaner escalation. Better support decisions.
When ticket volumes rise, the difference is made through speed, continuity and management visibility.
Higher cost per ticket, longer MTTR, more burnout and a weaker end-user experience.
A practical ecosystem to absorb repetitive demand, preserve context and pilot support performance.
Handle high-frequency L1 requests with fast, consistent AI-driven resolution.
Pass complex cases to L2 with issue summary, context and recommended next steps.
Oversee SLA, backlog, trends and ROI through a unified support cockpit.
The value doesn't come from a chatbot alone. It comes from learning quality, escalation continuity and governance.
Use your KB, resolved tickets and support patterns to improve relevance over time.
Carry intent, steps already attempted and resolution history into L2 workflows.
Oversee SLA, backlog, trends and ROI through a unified support cockpit.
Faster decisions. Lower operating cost. Better control over scaling and service quality.
One answer for IT leadership, support operations and end users — without adding friction, delays or fragmented tools.
Reduce Level 1 workload, improve service visibility and demonstrate measurable ROI from support automation.
Automate recurring tickets, route complex cases faster and improve SLA performance with better context.
Get faster answers, shorter waiting times and more consistent support for everyday IT requests.
A coherent answer to support efficiency, user experience and IT performance — through one AI-powered support layer.
AImpact keeps repetitive L1 flows moving with instant classification, knowledge retrieval and guided resolution.
Portal, email, MS Teams or your ITSM — every channel funnels into one engine.
Intent, urgency and likely resolution path — recognised in natural language.
Knowledge search, instant answer or guided action — without queue dependency.
Send to L2 with packaged context when AI confidence isn't enough.
Reduce manual effort on password resets, access requests, FAQ-like incidents and standard support routines.
Less manual handling, lower MTTR and more consistency across high-volume requests.
When L2 must step in, the objective is not just routing — it's eliminating repeated diagnosis and dead time.
Better L2 productivity and fewer back-and-forth loops on unresolved cases.
A single view for activity, quality, trend signals and business value — no more hunting through dashboards.
Pilot benchmarks observed across enterprise IT support scopes — to be confirmed on your own measurement.
A pilot must prove value, but it must also show how the solution fits enterprise support governance.
Resolution logic, escalations and workflow decisions can be audited over time.
Deployment choices aligned to your security model and regulatory constraints — private LLM, on-prem or hybrid.
Roll out by perimeter, team or use case with controlled scaling — no big-bang risk.
Integrations scoped against your current ticketing, channel and knowledge landscape — ServiceNow, Jira, Zendesk, others.
A commercial path designed to reduce risk and accelerate stakeholder alignment.
Use cases, ticket typology, data sources, integration constraints, KPI baseline.
Priority L1 flows, escalation validation, support cockpit and measurement setup.
Controlled deployment by perimeter, support enablement and continuous improvement.
A scorecard with success criteria, decision rationale and targeted actions for scale-up.
Share of eligible L1 requests resolved automatically.
Time to first useful answer across pilot flows.
Tickets passed to L2 with sufficient context.
Perceived clarity and usefulness of support answers.
Avoided effort and cost on targeted flows.
Impact on queue pressure and manual workload.
A scorecard with success criteria, decision rationale and targeted actions for scale-up.
A short demo to prove the workflow, followed by a measurable pilot on one relevant business case.