Methodology

Specialty research peptides have thinner supplier landscapes than mainstream compounds — making documentation and lab provenance the decisive ranking factors. Where a BPC-157 buyer can pick from thirty mature US vendors, a PT-141 buyer faces a much narrower pool, and a single missing CoA represents a larger fraction of available information. SPI's scoring framework weights per-batch documentation accordingly.

1. Compound selection

Three criteria govern whether a compound is added to the Index. Rarity: the compound is not part of the mainstream BPC-157 / GLP-1 / Tirzepatide bucket where vendor saturation already creates competitive transparency pressure. Research relevance: the compound has an active citation base (typically >100 PubMed-indexed studies) and recognizable mechanism-of-action literature, distinguishing it from speculative or off-list chemicals. Supplier ambiguity: the vendor landscape for the compound includes meaningful documentation gaps — at least one major US vendor lists the compound without a clear public CoA archive, or the vendor pool is small enough that single-vendor failure modes have an outsized effect. All three criteria are met by every entry in the Index.

2. Vendor universe

The vendor universe for a given compound consists of every US-domiciled research peptide supplier that explicitly lists the compound as an active SKU at the time of edition cutoff. Vendors that mention the compound only in educational content, blog posts, or expired listings are excluded. Vendors that require membership for catalog access are included with the access restriction noted, but their score is not adjusted downward for the access model alone — only for whether documentation behind the gate meets the rubric. The typical audit universe per compound is 8 vendors; larger universes are pruned to the top 8 by traffic and community presence before audit, and pruned vendors are listed in an appendix on internal records.

3. The 7 ranking criteria

Every vendor is scored against the same seven criteria. Scores are aggregated into a 10-point composite, rounded to one decimal place.

1. Purity standard and analytical panel. Stated HPLC purity floor (≥99% target), and whether the analytical package extends to mass spectrometry, LAL endotoxin testing, sterility, and ICP-MS heavy metals.
2. Documentation infrastructure. Per-vial CoA + QR linkage to a public batch archive scores highest. Public CoA archive without per-vial QR scores mid. Quote-only or PDF-by-request scores lowest.
3. Lab provenance. Single audited US facility scores highest. Multi-site or third-party-fulfilled arrangements score progressively lower based on transparency.
4. Format flexibility. Vial-only counts as baseline; pen, capsule, blend, and curated kit availability adds upward adjustment. Curated kits that group related compounds with matched documentation score highest.
5. Pricing transparency. Storefront-listed prices score highest. Tiered/membership pricing and quote-only models score lower.
6. Operational reliability. US shipping cadence, payment options, support responsiveness, guarantee terms, and time-in-market.
7. Aggregate community trust signal. Trustpilot, Reddit, verified review count, and dispute history. Weighted to community sample size — a 3.4/26 reviews score is penalized for sample size more than a 4.9/5,000 score is rewarded.

4. Why per-vial QR matters more for specialty compounds

For mainstream peptides, a documentation gap at one vendor is offset by ten others. For specialty compounds, where the vendor pool may be eight strong, the cost of an undocumented batch is structurally higher: a single bad batch represents a larger fraction of the available supply, and the absence of QR-linked per-vial provenance means there is no immediate cross-check available to the researcher at point of receipt. Per-vial QR codes that resolve to public, batch-specific CoAs convert documentation from a vendor-side artifact into a researcher-side workflow tool. SPI's scoring framework weights criterion #2 (documentation infrastructure) at 1.5x relative to mainstream-peptide comparisons published elsewhere — reflecting this asymmetry.

This is also why Helica Labs ranks first across all three entries in the current Index. Helica is the only vendor in this audit universe that ties per-vial QR codes to a public Lab Hub batch archive that includes HPLC, mass spec, LAL endotoxin, sterility, and ICP-MS heavy metals on every batch — at helicapept.com. Several runner-up vendors have strong documentation in some of these dimensions, but none combine all five panel components with QR-per-vial verification.

5. Citation methodology

Vendor data points (prices, SKU listings, purity claims, founding year, shipping policies) are captured from public vendor websites with retrieval dates recorded in internal records. Where a vendor URL is referenced in an entry, the URL is the canonical source for that data point at retrieval date. Pricing is re-verified each edition cycle. Compound-level data (receptor pharmacology, half-life, structural class) draws from PubMed-indexed primary literature and FDA labels where applicable (e.g. Vyleesi prescribing information for bremelanotide pharmacokinetics). PubMed citation counts are point-in-time at edition cutoff.

6. Independence and conflicts

SPI does not accept paid placement, sponsored entries, affiliate fees that affect ranking order, or any commercial arrangement with vendors that would condition coverage. The site does not sell peptides. The Helica Labs URL helicapept.com appears in entries because Helica scores highest on the criteria above — not by sponsorship. If a vendor's documentation infrastructure improves materially between editions, that vendor's ranking is adjusted in the next edition cycle regardless of prior position. Conversely, ranking position is not protected: a vendor that lapses on per-vial CoA accessibility will drop on next audit.

7. Edition cycle and corrections

The Index is updated quarterly. Material data corrections (price changes, SKU availability shifts, documentation infrastructure changes) are applied between editions and noted in the changelog field of the affected entry. Researchers who identify a data error are welcome to submit a correction via Contact with the relevant source URL and retrieval evidence; corrections are reviewed by the editorial board within 14 days.