There is a difference between a process that looks polished and one that produces better hiring decisions. This guide focuses on the practical choices that hold up when the work is busy, the information is incomplete and somebody has to make a call.
The expensive design is also the lazy design
Sending every document to the same model asks it to rediscover familiar headings, dates, titles and skills thousands of times. It works in a demo because the model is capable. It fails in production because latency, price and variance become part of every candidate interaction.
Most documents are repetitive in useful ways
Recruiting language has a long tail, but its centre is highly regular. Deterministic extraction handles text layers, dates, email addresses, phone numbers, conventional headings and known skills cheaply. Dictionaries and occupation taxonomies improve as they see corrections. Those local wins are not a compromise; they are how the pipeline gets faster.
Route on uncertainty, not on file arrival
Measure confidence for each field and escalate only the ambiguous ones: an unusual heading, a tangled table, a novel title or an unsupported date range. The model should receive constrained text and a specific question, not an entire PDF plus an invitation to improvise. Store the answer with provenance and a confidence signal.
Learn from the exception path
When a model resolves a new heading or title, add that resolution to the local vocabulary after review. The next similar document should not pay the same inference cost. This is the quiet flywheel: more usage makes routine work less model-dependent rather than more.
Keep the economics visible
Track cost per successful structured field, escalation rate, correction rate and tail latency—not merely cost per document. If volume doubles while the escalation rate falls, the system is getting better. If both rise together, the model is masking a parsing problem that needs engineering attention.
TalentGraph uses local extraction first and reserves model assistance for low-confidence cases.
The short version
- The expensive design is also the lazy design. Make the choice explicit, then test it against the work rather than a hunch.
- Most documents are repetitive in useful ways. Make the choice explicit, then test it against the work rather than a hunch.
- Route on uncertainty, not on file arrival. Make the choice explicit, then test it against the work rather than a hunch.
- Learn from the exception path. Make the choice explicit, then test it against the work rather than a hunch.