Client
Atlas Core
Industry
Enterprise SaaS / AI Systems
Duration
3 Months
Services
AI & Intelligent Systems, Data Engineering
Atlas Core operations staff spent an average of 24 minutes per ticket reading raw customer support briefs, extracting client contract numbers, checking order status tables in their ERP, and forwarding them to appropriate service lines. They needed an AI agent system to handle classification and lookup tasks autonomously with a strict zero-hallucination rate on financial data.
We engineered an intelligent agent workflow. First, incoming ticket data is parsed and vectorized using dense embedding models. Second, we query vector databases for context matching (extracting contract clauses and status codes). Third, we run LLMs using strict JSON structures, validating output variables with Zod schemas. If the output does not conform to the database schema, it is routed to human operators.
We design clean system flows that separate concern layers. Check out how data moves securely through this operational framework.
We built an validation loop using Instructor to parser outputs against Pydantic definitions, requesting model auto-corrections on structural failures.
We designed a reciprocal rank fusion algorithm, combining vector embeddings queries with standard keyword search to locate documents correctly.
We stay accountable for database performance and business outcomes.
“Their automation workflows eliminated manual ticket processing queues completely. Their engineering outcomes were fully measurable and auditable within a single quarter.”
We maintain LLM prompt updates, vector index tuning, and database monitoring services under a recovery SLA agreement.
Discuss your system constraints, data structures, and compliance rules with a senior software architect.