Services
AI engineering shaped around the system you need.
From a focused validation sprint to full production delivery, Jalpa connects AI capability to an operational problem, integrates it properly and measures how it performs.
Customer Operations AI
AI systems that respond, qualify, route and follow up across voice, web and messaging channels.
Common problemCustomer-facing teams lose opportunities to slow response times, inconsistent follow-up and repetitive enquiries.
Systems we can build
- Lead qualification and appointment booking
- Customer-support resolution and escalation
- Voice AI agents and multilingual interactions
- CRM-integrated follow-up and enquiry routing
Expected outcomes
- Faster response times
- Consistent follow-up
- Lower support workload
- Better customer availability
Typical integrations
CRM, telephony, calendar, help desk, messaging.
Delivery approach
Validate the riskiest assumptions, build against measurable criteria, integrate into the real workflow, then monitor quality after launch.
Internal Operations AI
Reliable assistants and workflows that help teams find information, process documents and complete repetitive work.
Common problemOperational knowledge is fragmented across systems while manual processing creates delay and inconsistency.
Systems we can build
- Enterprise knowledge assistants and RAG
- Document processing and data extraction
- Reporting and workflow automation
- Human-in-the-loop back-office agents
Expected outcomes
- Less manual work
- Faster access to information
- More consistent processes
- Shorter turnaround times
Typical integrations
document stores, databases, internal APIs, ERP, collaboration tools.
Delivery approach
Validate the riskiest assumptions, build against measurable criteria, integrate into the real workflow, then monitor quality after launch.
AI Product Engineering
End-to-end engineering for dependable AI products, from technical validation through deployment and observability.
Common problemAI product teams need to validate quickly without creating fragile architecture or unpredictable model behaviour.
Systems we can build
- AI MVPs and production applications
- RAG and conversational AI systems
- Voice AI and multimodal products
- Model evaluation, monitoring and integrations
Expected outcomes
- Faster product launches
- Lower technical risk
- Production-ready architecture
- More reliable AI behaviour
Typical integrations
model providers, vector stores, product APIs, cloud infrastructure, analytics.
Delivery approach
Validate the riskiest assumptions, build against measurable criteria, integrate into the real workflow, then monitor quality after launch.
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