Our Product
AI RAG-LLM
Resume Matcher
A proprietary candidate-matching engine that Triumph Systems licenses to hiring teams. It reads résumés for demonstrated evidence rather than keywords, is queried in plain English instead of rigid boolean filters, and consistently returns stronger shortlists than the applicant-tracking systems it replaces.
Natural-language querying
Ask for what you actually need.
There is no query syntax to learn and no keyword list to maintain. Describe the role in the same language you would use briefing a colleague — “senior backend engineer who has run production systems in a regulated environment” — and the engine resolves intent, weighs evidence, and ranks the field accordingly.
Legacy ATS vs. RAG-LLM precision
Conventional applicant-tracking systems rank on exact keyword overlap, discarding qualified candidates who describe the same work in different terms. The Matcher reads for meaning.
Brittle filtering
Rejects a résumé listing “Lead Software Engineer” when the requisition strictly demands “Principal Developer.”
Context blindness
Cannot distinguish “managed a team delivering in Python” from “completed an introductory Python course.”
Semantic comprehension
Recognises that “architected distributed systems in Go” correlates strongly with senior backend requirements, whatever the job title.
Experience vectorisation
Maps a career trajectory to infer genuine competency, rather than trusting the wording a candidate happened to choose.
Architectural advantages
Built on a foundation of vector retrieval and fine-tuned language models, delivering analytical depth without the keyword guesswork — and without sending your candidate data anywhere it should not go.
Instant screening
Large candidate batches are processed in seconds. The retrieval layer keeps the language model focused on the relevant extracted evidence, containing both latency and cost.
Bias reduction
Configurable abstraction layers can strip demographic identifiers before inference, so ranking rests on demonstrated capability rather than proxy signals.
Deep skill mapping
Surfaces latent competencies a candidate never spelled out — someone who built a high-throughput API in Rust almost certainly understands concurrency and memory management.
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See it run against your roles.
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