What AIRA does with your query
When you submit a query, AIRA doesn't treat it as a keyword search. It reads the full sentence and breaks it into two types of criteria:
Quantitative filters: criteria that map to structured, indexed fields in Recruiterflow (job title, location, company, years of experience, skills, career stage, etc.)
Qualitative signals: criteria that require reading unstructured data like notes, call transcripts, and resume narratives (e.g. "strong closer", "high promotion velocity", "scaled a team from 5 to 50")
After parsing, AIRA shows you exactly what it understood via filter chips in the side sheet, one chip per extracted criterion. This is your chance to verify the interpretation before results load.
Quantitative filter chips
These chips correspond to structured fields AIRA can filter on directly. Common examples:
Job Title: 💼 Currently a Product Manager (+ variants) - AIRA generates title variants automatically (e.g. Product Lead, PM, Group Product Manager). You can remove unwanted variants before rerunning.
Current Company / Previous Company: 🏢 Currently at FAANG (Apple, Amazon, Netflix, Google, Meta) - group terms like FAANG, Ivy League, or Fortune 100 are expanded and shown explicitly.
Location: 📍 Bay Area (San Francisco, Oakland, San Jose +18 more) - regional terms are expanded to constituent cities or countries. You can remove individual values.
Years of Experience: ⏱ More than 8 years of experience
Added On / Last Contacted / Last Activity: Date-based filters extracted from time references in your query
The chip always shows the phrase you used. AIRA never silently replaces your words with expanded values.
AIRA Intelligence filter chips
These chips (marked with a ✨ badge) are AI-derived structured fields generated from the AIRA Talent Graph, not raw indexed fields. They go deeper than standard filters:
Career Stage: ✨ Career Stage is Senior - derived from total experience, title progression, and seniority signals. Shown as a label (Early / Mid / Senior / Leadership / Executive), never as a year range.
Promotion Velocity: ✨ Promotion velocity is Exceptional - derived from title progression patterns. Shown as a label (Slow / Below Average / Average / Good / Exceptional).
Function / Months in Function: ✨ Deep knowledge of Product Management (8+ years) - when you use phrases like "deep experience in" or "expert in", AIRA resolves both the function and the implied tenure.
Industry / Months in Industry: ✨ Deep expertise in Medical Devices (5+ years)
Inferred Skill Strength: ✨ "proficient in Python" (Strong in Python) - AIRA maps skill phrases to a strength level (Weak / Average / Good / Exceptional) and can filter by recency.
Median Tenure / Tenure Across Companies: Derived from career history patterns
Qualitative ranking chips
These chips (marked with a 🧠 prefix) are not hard filters, they are ranking signals. They don't exclude candidates; they influence how results are scored and ordered. Examples:
🧠 Closed $1M+ deals
🧠 Handled enterprise accounts
🧠 Built scalable systems
Each chip shows the exact phrase from your original query, never paraphrased. Candidates are scored 0–100 against these signals using AIRA Talent Graph data.