What AlifZetta looks like in production.
Three deployments — one government-tier, one healthcare-tier, one financial-services pilot. Different verticals. Same stack. Same economics.
Nepal government citizen-services portal
production- Vertical
- Government · Sovereign
- Scale
- Nation-scale · 30M citizens · 77 districts
- Deployed
- 2026 Q2 · live at demo.axz.si
- Stack
- CLLM cluster + NEXUS (94k entries) + LATTICE + PRISM · bilingual EN + NE
The Nepal-facing citizen-services deployment is where AlifZetta first proved the paradigm at nation scale. A ministry needed to answer thousands of citizen questions per day about passport renewal, citizenship application, tax filing, vehicle registration, and hundreds of other services — in both English and Nepali — with citation to the correct government notice for every answer. The frontier-lab option was legally out (data residency), and expensive (per-token pricing at citizen scale). The AlifZetta CLLM + NEXUS stack ships every answer with a link to the source ministry notice, runs on a single CPU node at forty-seven dollars a month, and refreshes the ministry-notice substrate every 30 minutes. Sub-3ms partner-API latency. Zero external LLM calls.
Bilingual clinical decision support
production- Vertical
- Healthcare · Regulated
- Scale
- 319 clinical pathways · 18 specialties
- Deployed
- 2026 Q3 · integrated into HTE partner network
- Stack
- CLLM + NEXUS medical KB + PRISM audit trail + 9 interactive triage scorers
Clinical decision support in emerging-economy settings has a specific problem: guidelines exist, but they are trapped in PDFs the clinician does not have time to search. The AlifZetta clinical stack surfaces the right pathway in seconds, in the clinician's language, with a citation to the source guideline (NEML, GOLD, KDIGO, WHO). 319 evidence-based clinical pathways cover the top ICD-11 presentations, with Nepal-endemic layers for snake bite (Krait/Cobra/Viper stratification), rabies category triage, altitude sickness, dengue severity scoring, and neonatal red flags. Every answer ships with a PRISM proof-tree that a hospital audit committee can walk end-to-end.
Sovereign inference for a regulated financial partner
pilot- Vertical
- Finance · Sovereign inference
- Scale
- Two-week pilot
- Deployed
- 2026 Q3 · partner NDA
- Stack
- CLLM + NEXUS + LATTICE + PRISM on partner-owned CPU hardware
A regulated financial partner had built a chatbot on OpenAI + Pinecone and hit the same wall a lot of financial-services teams hit: their compliance function could not sign off on outputs that had no auditable derivation. The pilot took their existing knowledge base, ingested it into NEXUS as typed entities with citations to internal policy documents, and stood up a CLLM cluster on their hardware. Every answer now traces to a policy paragraph. The compliance sign-off that had been pending for six months completed in nine days after the AlifZetta migration.
Common pattern across every deployment
The workflow is always the same. Enumerate the partner's knowledge sources. Ingest them into NEXUS as typed entities with citations. Wire the CLLM cluster on the partner's hardware. Point the partner's existing frontend at the AlifZetta endpoint. Compare answers side-by-side for one hundred queries. Cut over one workflow at a time. Two weeks, fixed fee, working endpoint at the end.
What we can not case-study yet
Several partner deployments are under NDA. Some ministry deployments are covered by data-residency clauses that prevent public naming. We will publish those case studies as and when the partners release attribution rights — and never before.
Book your case study
Every two-week pilot is a case study waiting to be written. Fixed-fee, on your hardware, working endpoint at the end.
Book a pilot → See pricing →