Managing reviews for one brand is manageable. For twelve, it isn't.
Three things sellers said, in their own words (public posts, linked).
The urgent one is buried in the pile
“By the time you notice sales dropped 40%, the reviews have been up for days.” a commenter on a seller thread about fake 1-star reviews, r/FulfillmentByAmazon, April 2026 (thread)
A safety complaint looks like a routine three-star gripe until someone reads it. Every review you send gets its own urgency field, and you can wire an alert to anything flagged urgent.
Product feedback, buried under delivery noise
“I'm trying to read the reviews, but 90% of them are about 'late delivery' or 'rider bhaiya'. Actual product feedback kaise filter karu?” owner of a small D2C snack brand on Blinkit/Zepto, r/indianstartups, August 2025 (thread)
Each review is tagged with topics, so a delivery complaint is its own tag you can filter on instead of a line you have to skim past. Topic tags are one of the weaker fields we measure; the per-field numbers are in the README.
Tired of reading them by hand
“I am new to D2C business and tiered of reading reviews manually.” a seller on Amazon/Flipkart, r/IndiaBusiness, May 2026 (thread). The top reply there says to “just dump all your reviews into Claude or ChatGPT”.
Pasting reviews into a chatbot works once. Samidha runs on every review you send it, not only the ones you paste, publishes how often it is right with error bars (below), and answers “unclear” on sentiment and buy-again instead of guessing.
See it work, on real reviews
Every example below is a real, unmodified API response — extraction, cross-review trends, and bulk CSV upload. No network needed, works with JS disabled.
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How it works
Three steps, no ML team required on your side.
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Send us a review
One API call or a CSV upload. Handles English and Hinglish, no pre-translation needed.
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We extract the signal
Sentiment, urgency, topics, pros/cons, and competitor mentions — structured JSON back in under a second per review.
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You see what matters
Query the dashboard, pull trends across every brand you manage, or wire an alert for anything flagged urgent.
The honest accuracy story
Field-level scoring (exact match + fuzzy + set F1) on the CI regression fixtures.
These are the fixtures the prompt was developed and tuned against, so this table
is a change detector, not a measure of accuracy on unseen reviews; the held-out
figure, with the exposure history of that corpus and the share of it that could
not be scored, is in
the README.
Prompt v2.3, measured under the models production actually runs
(openai/gpt-oss-20b /
openai/gpt-oss-120b). 95% CIs are paired
bootstrap over the 43 fixtures. Gates were reset 2026-09-10 to the measured
baseline after the previous Llama-era targets no longer held under these models
— see the eval runbook
for the full diagnosis.
llama-3.1-8b-instant /
llama-3.3-70b-versatile) on 2026-08-16. The numbers below
are the current, honest measurement against what's actually deployed. Nothing was tuned to
recover a prior number.
| Metric | Accuracy | Gate | Status |
|---|---|---|---|
| Overall extraction accuracy | 78.6% (95% CI 73–83%, n=43, eval 2026-09-19) | ≥76% | ✓ PASS |
Scoring note: free-text fields (product, topics, competitor mentions) are compared after normalization. On 20 Sep 2026 we corrected the comparator so that different correct spellings of “no product named” and near-identical topic labels are no longer scored as wrong. That raised the figures above; the model’s outputs did not change. Details.
It doesn't guess when it's unsure
Two fields (sentiment, buy-again) are allowed to say "unclear" rather than commit to a wrong answer. Measured on real marketplace reviews that the prompt-development process had not seen (the review count, and what was excluded and why, are in the last row of the table):
| Field | Coverage | Accuracy-on-answered | Wrong-committed |
|---|---|---|---|
| sentiment | 71.4% [60.0%, 81.4%] | 88.0% [78.0%, 96.0%] | 6/50 = 12.0% |
| buy again | 24.3% [14.3%, 34.3%] | 70.6% [47.1%, 88.2%] | 5/17 = 29.4% |
| n=70 reviews the prompt-development process had not seen; the 36 of 106 it had seen are excluded (ADR 0032). Over all 106 (the earlier published basis): sentiment coverage 77.4% / accuracy-on-answered 87.8%, buy again coverage 43.4% / accuracy-on-answered 76.1%. On the 36 seen reviews alone buy-again coverage is 80.6%, against 24.3% on the unseen ones, so including them flattered it. | |||
Your customers' words, handled carefully
Every claim below is verified against the running system or a provider's own primary legal document — not marketing copy. See the full trace in our public repo.
Default Stateless by default
On the default plan, submitted review text and extraction output are never written to our database or application logs — the response you get back is the only copy. Proven, not asserted: verified by sending a distinctive sentinel string through the live API and confirming it appears in zero tables and zero logs.
Read the verification →No training on your data
Quoted directly from our LLM provider's own Services Agreement: "Groq is not permitted to use Inputs or Outputs for training or fine-tuning any AI Model Services or other models, unless explicitly granted permission or instructed by Customer." Independently verified against their primary legal document, not a summary page.
Zero data retention on inference
Inference-APIs Zero Data Retention is enabled on our LLM provider account. In the interest of the same honesty standard we hold every claim on this page to: our provider exposes no API-level way to independently re-verify this setting (no header, no endpoint) — this is our account holder's own Console confirmation, not something we can prove from outside.
Full sub-processor list, published
Every third party that touches review data, what it receives, and its own verified data-handling commitments — kept current in our public repo, not a static PDF.
View sub-processors →Want the data to live somewhere?
Opt into retained mode per organization — pick a 30 or 90-day window, get the dashboard, review search, and trend insights. Purge on demand at any time, no waiting for the window. Bulk CSV upload requires retained mode (rows are staged durably so an interrupted upload can resume).
Simple, per-review pricing
USD and INR shown side by side. Annual billing = 2 months free on every paid tier.
Free
1,000 reviews/mo
$0 /mo
₹0/mo
Starter
5,000 reviews/mo
$29 /mo
₹1,499/mo
Growth
25,000 reviews/mo
$79 /mo
₹4,999/mo
Scale
100,000 reviews/mo
$199 /mo
₹12,999/mo
Agency
200,000+ reviews/mo
Talk to us
Custom, multi-brand billing
First customers are invoiced manually while we're in early access — no card required to talk to us.
Talk to us
Tell us how many brands you manage and roughly how many reviews a month — we'll reply with a plan and a real API key, not a sales deck.
Rather use email? Write to hello@samidhareviews.xyz.
Prefer to just try it first? See it work or paste your own review above — no signup needed.
Thanks, we've got your details
We'll reply to with a plan and a real API key, not a sales deck.
Something to add? Write to hello@samidhareviews.xyz.