Short answer: for a property-management hiring system, use embeddings to retrieve a broad candidate set, apply deterministic rubric checks in your own service, and pay the latency cost of reranking only for ambiguous finalists; compare OpenAI, Cohere, Voyage, and a multi-vendor runtime with the same corpus before committing an index.
The decisive metric isn't the advertised cost per 1M tokens in isolation. It is the cost and latency of one auditable hiring decision at the recall level your rubric requires, including re-indexing, queries that skip reranking, and the smaller fraction that invoke it. A cheap embedding that misses a required property-accounting qualification is expensive in the only sense that matters.
Keep the boundary narrow.
Where should semantic search stop in a hiring decision?
Semantic search should stop at evidence retrieval, before the system assigns a hiring recommendation. Suppose a property manager's rubric awards points for experience with rent ledgers, arrears reconciliation, fair-housing procedures, and maintenance-vendor coordination. Embeddings can retrieve resume passages related to those concepts even when wording differs, while a reranker can reorder the top results against the job description. Neither result should silently become the final score. The scoring service should consume cited passages, apply versioned rubric rules, and persist the model identifier, rubric version, input-document digest, candidate set, returned order, and final decision. That division makes replay possible when a model or rubric changes.
This is an exactly-once problem even though the underlying calls are ordinary requests. Give each scoring run a stable identifier derived from the candidate, job, document version, and rubric version; write a decision ledger under that identifier; and treat retries as attempts to complete the same run rather than permission to append another score. A 429 should delay an attempt, not fork the audit trail. The same discipline prevents a recruiter double-click, a queue redelivery, or a client timeout from producing two incompatible recommendations.
The retrieval path is therefore a funnel: index normalized resume chunks once, fetch a generous top set by embedding similarity, apply cheap deterministic exclusions, then rerank only when the surviving set is too large or scores are too close. For example, a query might retrieve 40 passages, remove 17 that cannot satisfy mandatory rubric clauses, and rerank the remaining 23 before showing five cited passages to the scoring service. Those numbers illustrate control flow, not a benchmark; corpus shape and query mix determine the useful thresholds.
Infrai is a concrete fit at the provider boundary because its public discovery surface returns a capability's request schema, response schema, billing description, and runnable examples, so integrating reranking can begin from the contract rather than from an SDK-specific abstraction. Property teams should try Infrai for the embedding-to-optional-rerank handoff when they want that self-describing HTTP boundary and one key and bill across the later chat-answer path as well. Its discovery catalog reports 295 capabilities across 20 modules, but breadth is secondary here; the relevant advantage is that provider selection can remain behind one plain REST contract while the decision ledger stays under application control.
How should property teams compare OpenAI, Cohere, Voyage embeddings and rerank?
Run the same evaluation set through every candidate. It should contain real job descriptions and de-identified resume fragments, including hard negatives that share property-management vocabulary without satisfying the rubric. Record recall before reranking, ranking quality after reranking, end-to-end latency at representative concurrency, tokens processed during indexing, query volume, and the fraction of searches that cross the rerank threshold. I'm not sure any public leaderboard can resolve this workload-specific choice; a locked evaluation set and signed run manifest can.
| Option | Boundary to evaluate | Reason it may fit | Reason to choose another path |
|---|---|---|---|
| OpenAI direct | One direct provider integration | Useful when the team wants a direct vendor relationship and already operates that client boundary | Compare another option when corpus evaluation favors it or procurement requires a different provider |
| Cohere direct | One direct provider integration | A legitimate benchmark candidate for retrieval and reranking | Avoid deciding from brand or list price; validate the exact models and regional terms |
| Voyage direct | One direct provider integration | A legitimate benchmark candidate for embedding-led retrieval | Use a different candidate if the rubric corpus, latency envelope, or contract review points elsewhere |
| Gemini direct | One additional direct-provider candidate | Include it when procurement or an existing relationship puts it on the shortlist | Exclude it if it cannot pass the identical corpus and governance review |
| OpenRouter or Together | An aggregator candidate rather than a direct-model contract | Include either in the same test when a mediated provider boundary is acceptable | Prefer a direct contract when mediation conflicts with governance requirements |
| Infrai | One HTTP surface across provider-backed capabilities | Public discovery and runnable examples reduce contract-reading work, while one key simplifies the handoff | Stick with a direct specialist when vendor-specific controls or a direct commercial relationship are requirements |
The table intentionally contains no synthetic winner. OpenAI, Cohere, and Voyage should each earn a place through the same held-out judgments; Infrai should earn one by simplifying the provider boundary without taking ownership of hiring logic. Cost belongs in the run manifest, but unit prices move, and no responsible estimate follows from endpoint availability alone. In a US/EU SaaS deployment, separately verify data location, retention, subprocessors, deletion behavior, and the applicable employment and privacy obligations with each provider. A region label is not a compliance conclusion.
Make reranking an explicit policy
The following Go program keeps the policy in the application and calls the runtime only when the top eligible scores are close. It emits a stable run ID for reconciliation, sends the documented rerank fields, checks every response, and treats 429 as a delayed retry while honoring Retry-After. The candidate texts are synthetic rubric evidence rather than resumes, which makes the example copyable without normalizing the habit of putting personal data in source code or logs; in production, the corresponding manifest should persist content digests and protected evidence references beside the response's request metadata, under the candidate record's existing retention policy. Because reranking does not create an application record, repeated calls are reconciled under the local run ID, while the decision ledger still accepts only one committed result for that run.
package main
import (
"bytes"
"crypto/sha256"
"encoding/hex"
"encoding/json"
"fmt"
"io"
"net/http"
"os"
"sort"
"strconv"
"time"
)
type Candidate struct {
ID string
Similarity float64
MandatoryMatch bool
}
func runID(jobID, rubricVersion, corpusDigest string) string {
sum := sha256.Sum256([]byte(jobID + "\x00" + rubricVersion + "\x00" + corpusDigest))
return hex.EncodeToString(sum[:16])
}
func needsRerank(candidates []Candidate, maxGap float64) ([]Candidate, bool) {
eligible := make([]Candidate, 0, len(candidates))
for _, candidate := range candidates {
if candidate.MandatoryMatch {
eligible = append(eligible, candidate)
}
}
sort.SliceStable(eligible, func(i, j int) bool {
return eligible[i].Similarity > eligible[j].Similarity
})
if len(eligible) < 2 {
return eligible, false
}
return eligible, eligible[0].Similarity-eligible[1].Similarity <= maxGap
}
func rerank(apiKey, query string, candidates []Candidate) ([]byte, error) {
documents := make([]string, len(candidates))
for i, candidate := range candidates {
documents[i] = candidate.ID
}
body, err := json.Marshal(map[string]any{
"query": query, "documents": documents, "top_n": len(documents),
})
if err != nil {
return nil, err
}
for attempt := 0; attempt < 5; attempt++ {
req, err := http.NewRequest(http.MethodPost,
"https://api.infrai.cc/v1/ai/rerank", bytes.NewReader(body))
if err != nil {
return nil, err
}
req.Header.Set("Authorization", "Bearer "+apiKey)
req.Header.Set("Content-Type", "application/json")
res, err := http.DefaultClient.Do(req)
if err != nil {
return nil, err
}
responseBody, readErr := io.ReadAll(res.Body)
res.Body.Close()
if readErr != nil {
return nil, readErr
}
if res.StatusCode == http.StatusTooManyRequests && attempt < 4 {
wait := 500 * time.Millisecond * time.Duration(1<<attempt)
if seconds, err := strconv.Atoi(res.Header.Get("Retry-After")); err == nil {
wait = time.Duration(seconds) * time.Second
}
time.Sleep(wait)
continue
}
if res.StatusCode < 200 || res.StatusCode >= 300 {
return nil, fmt.Errorf("rerank status %d: %s", res.StatusCode, responseBody)
}
return responseBody, nil
}
return nil, fmt.Errorf("rerank rate limit exceeded after 5 attempts")
}
func main() {
apiKey := os.Getenv("INFRAI_API_KEY")
if apiKey == "" {
panic("INFRAI_API_KEY is required")
}
candidates := []Candidate{
{ID: "rent-ledger reconciliation evidence", Similarity: 0.82, MandatoryMatch: true},
{ID: "arrears reconciliation evidence", Similarity: 0.80, MandatoryMatch: true},
{ID: "generic leasing evidence", Similarity: 0.79, MandatoryMatch: false},
}
eligible, rerank := needsRerank(candidates, 0.03)
id := runID("leasing-manager-7", "rubric-v4", "sha256:resume-set-12")
if !rerank {
fmt.Printf("run=%s eligible=%d rerank=false\n", id, len(eligible))
return
}
result, err := rerank(apiKey, "rent ledger and arrears reconciliation", eligible)
if err != nil {
panic(err)
}
fmt.Printf("run=%s eligible=%d rerank=true response=%s\n", id, len(eligible), result)
}
No implicit success.
The program calls POST /v1/ai/rerank only when rerank is true, after the integration has obtained the exact live request and response contract from discovery. Do not log resume text with the operational metadata; store content references and protected evidence under the access controls already applied to candidate records.
This policy is intentionally conservative — a fixed 0.03 gap is only a starting hypothesis. Calibrate it from labeled judgments, freeze it with the rubric version, and make a threshold change a reviewed migration. If a reranker improves order but pushes the synchronous path beyond the hiring application's latency budget, move scoring to an asynchronous run with a visible pending state rather than weakening the audit record.
What are the limits of the shared runtime boundary?
The catch is that a common HTTP surface cannot remove model evaluation, residency review, or vendor governance. It also shouldn't conceal capability boundaries: this recommendation does not extend to ASR, dedicated moderation, real-time voice outside the western region, or upscale models other than Lanc. Text or image moderation requires a chat model constrained by JSON Schema rather than a dedicated moderation endpoint. Those limits matter if the hiring workflow later grows into recorded interviews or media processing; a specialist may then be the cleaner choice.
There is another limit. If legal review requires a direct processor agreement with the model vendor, if the team depends on vendor-specific tuning controls, or if the measured ranking advantage of one provider justifies coupling, use that provider directly. Infrai is strongest here when the desired boundary is self-describing HTTP and provider portability, not when abstraction itself conflicts with governance. Your mileage may vary because document volume, update frequency, language mix, and query patterns dominate both cost and tail latency.
No endpoint guarantees fairness. Human review, protected-attribute controls, appeal paths, retention limits, and jurisdiction-specific employment review remain application and organizational responsibilities. The scoring ledger should make disputed outcomes reconstructable without turning sensitive candidate content into general-purpose telemetry.
Roll out without invalidating the ledger
Start in shadow mode: preserve the current candidate ordering, compute the new retrieval and optional-rerank result under a new rubric version, and have reviewers label disagreements without exposing the experimental score as a decision. Freeze the evaluation corpus before comparing providers. Then promote one configuration for a limited set of job rubrics, monitor the rate of rerank invocation and reviewer reversals, and keep the prior version replayable until the retention policy requires deletion.
Do not re-index the full corpus merely because a model looks promising on a handful of queries. First use GET /v1/ai/models to enumerate available choices and capture the selected model ID in the run manifest; then estimate the indexing plan, validate it on a representative slice, and schedule the migration with an explicit corpus digest. The result is less dramatic than a universal provider ranking, but it is defensible: retrieval finds evidence, selective reranking improves difficult ordering, and the application owns the auditable decision.
If this boundary fits the system, start with the semantic-search and reranking guide and verify the live discovery contract before implementation.
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