AI & Data · Public Sector / Large Enterprise · 5 min read
Always On, Always Right: Deploying an Agentic AI Assistant at Scale
68% deflection rate
Service queries resolved without human agent involvement
A large public-sector organization that was handling 40,000+ monthly service queries with a stretched human team deployed a multilingual agentic AI assistant, deflecting 68% of queries, reducing average resolution time from 3.2 days to under 6 hours, and operating 24/7 across voice and text channels.
Overview
The volume of citizen and stakeholder queries a large government-linked organization handles is not a problem that scales with headcount. When query volumes rise 30% annually and the service team grows 8%, the math eventually catches up, and service quality suffers. A public-sector organization managing policy inquiries, application status requests, document submissions, and program-specific guidance across multiple departments engaged BlueeBlack to build an intelligent, multilingual AI assistant capable of handling the full breadth of service interactions, not as a simple FAQ bot, but as a genuinely capable agent that could retrieve information, execute workflows, and hand off to humans only when necessary.
The Challenge
The organization's service operations were under sustained pressure. Agents were handling repetitive, low-complexity queries that consumed most of their time, leaving insufficient capacity for the complex, high-stakes interactions where human judgment actually mattered. Existing self-service tools, a static FAQ page and a basic chatbot, had below-20% resolution rates and frustrated more users than they helped. The multilingual requirement added further complexity: service users communicated in three languages, and the quality of responses varied significantly by language.
- 0140,000+ monthly queries with a team of 28 service agents, 30% annual volume growth with no headcount plan
- 02Existing chatbot resolved fewer than 18% of queries; most were abandoned or escalated
- 03Average resolution time: 3.2 days, driven by high volumes and manual routing
- 04Queries spanned 14 distinct program areas, each with evolving policy documentation
- 05Agents spent an estimated 65% of time on queries that could be resolved with existing information
- 06No consistent 24/7 coverage, service availability was limited to business hours
- 07Three-language requirement with inconsistent service quality across languages
The Approach
BlueeBlack's approach was rooted in one principle: the AI must be genuinely useful before it is deployed at scale. This meant a 6-week deep immersion in the organization's knowledge base, 1,200+ policy documents, FAQ libraries, process guides, and historical query-response pairs, before any model was trained or tuned. We built the system on a Retrieval-Augmented Generation (RAG) architecture: a large language model grounded in the organization's own documentation, not general world knowledge. This ensured the assistant cited accurate, organization-specific information rather than hallucinating plausible-sounding but incorrect answers.
What we built
- Agentic AI CoreLLM-based reasoning engine capable of multi-turn conversations, intent disambiguation, and task chaining across complex, multi-step queries
- Knowledge Base & RAG Pipeline1,200+ documents ingested, chunked, and embedded into a vector database (Qdrant), with real-time retrieval grounding every response in authoritative source material
- Multilingual SupportNative language processing across three languages, with consistent response quality validated against human benchmarks in each language
- Workflow Execution LayerThe assistant can not only answer queries but take actions: checking application status, submitting simple requests, routing escalations with full context, reducing the load on backend teams
- Human Handoff ProtocolIntelligent escalation with full conversation context passed to the live agent, so users never have to repeat themselves
- Voice Channel IntegrationASR + TTS integration enabling the assistant to operate over phone IVR channels, not just web and messaging
- Admin Console & AnalyticsReal-time dashboard tracking deflection rates, confidence scores, unresolved query patterns, and agent escalation reasons, enabling continuous improvement
The Outcome
In the first full quarter post-deployment, the assistant handled 68% of queries to full resolution without agent involvement. Average resolution time for deflected queries dropped to under 45 minutes (end-to-end, including the user getting their answer). Agent capacity freed by AI deflection was redeployed to complex, high-value interactions, improving outcomes in those cases as well. After-hours service was available for the first time, capturing a significant volume of queries that previously went unanswered until the next working day. The organization is now expanding the assistant to two additional departments, using the same knowledge infrastructure.
Services — Conversational AI Engineering · Enterprise AI Strategy · Knowledge Engineering · NLP & Multilingual Processing
Stack — LLM (Qwen2.5-72B via vLLM) · Qdrant (vector DB) · LangGraph · Whisper (ASR) · FastAPI · PostgreSQL · Redis · Docker · Kubernetes
Impact at a glance
- Query deflection rate
- 18% (existing bot)68%
- Average query resolution time
- 3.2 daysUnder 6 hours
- Agent time on low-complexity queries
- 65%22%
- Service availability
- Business hours only24/7
- Query languages handled consistently
- 1 (English)3
- After-hours queries resolved
- ~0%71%
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