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This page explains the mechanics behind AI Recommendations — useful when a result looks surprising and you want to know why. If you just want a guide to the page itself (tabs, filters, buttons), start there instead.
Most “the AI is broken” reports turn out to be the pipeline working exactly as designed. This page walks through the four stages every opportunity passes through — Retrieval → Qualification → Scoring → Delivery — and ends with a checklist that maps common symptoms back to the mechanism causing them.
1

Retrieval

Gather candidate opportunities from every source you’ve enabled for recommendations.
2

Qualification

Decide whether each candidate is your kind of work at all, and whether anything disqualifies it.
3

Scoring

Score what survives qualification across four weighted dimensions.
4

Delivery

Save every scored result, and email only the ones that clear the score threshold.

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Stage 1: Retrieval

For each source, SamSearch combines candidates found three ways:
  • By your codes — recent opportunities that match your NAICS/PSC codes.
  • By your profile — searches generated from your business description and other profile fields.
  • By your keywords — a separate search for each positive keyword on your profile.

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The signal gate: the #1 reason recommendations don’t appear

Your Company Profile needs at least one of the following before recommendation generation runs at all: positive keywords, NAICS codes, PSC codes, or a business description of 40+ characters. Miss all four and generation is skipped silently — the run still reports success, so nothing in the UI tells you it didn’t happen.
This is the single most common cause of “I set everything up and I’m getting nothing.” If your Recommendations page is empty and a manual refresh reports success anyway, check these four fields first — see the checklist below.

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Keywords are literal substring matches

Positive keywords are one of the strongest signals in retrieval — every source supports them the same way, and each one runs as its own search. But matching is literal substring matching, not semantic. Phrasing has to appear close to verbatim in the opportunity text.
Prefer short, common phrases over long, specific ones. A keyword like HVAC or janitorial will fire far more often than a five-word phrase that has to match verbatim. Add several short keywords rather than one long one.
Negative keywords work the same way — a literal substring match lowers the score on every source. "construction" as a negative keyword suppresses “construction,” but won’t touch “site renovation” even if that’s construction work in practice.

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NAICS and PSC codes are trusted differently by source

Codes only filter candidates in the code-based search above; in the other two they act as a ranking boost, not a filter. How much a code match (or mismatch) matters also depends on where the opportunity came from: In practice: if you rely on NAICS codes to find SLED, Canada, or DIBBS opportunities, know that the code is SamSearch’s best guess, not the issuer’s — a wrong guess won’t hide a good match, but a matching guess won’t guarantee one either.

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Stage 2: Qualification

Every candidate that survives retrieval goes through a two-part AI decision:
  1. Is this the company’s kind of work at all? If not → reject. Rejected opportunities are never shown.
  2. Is anything blocking the award? (a set-aside you can’t hold, a location you can’t serve, a clearance you don’t have) If so → low fit. Otherwise → pass.
Two profile fields change how this decision gets made:
  • Operating region — if you haven’t set one, location is left out of qualification entirely. The AI doesn’t consider geography for your profile until you set a region.
  • Certifications — with none listed, set-aside eligibility is treated as unknown, not as “not eligible.” An opportunity requiring a set-aside you might hold won’t be auto-rejected for a missing certification — the AI weighs it with incomplete information.
Every qualification decision is remembered — one decision per profile, source, and opportunity. Once an opportunity has a final decision (pass, low fit, reject, or needs review), later runs skip it. Opportunities that were waiting on attachments are retried.

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The qualification cache: why recommendations stop changing

A cached decision expires at the opportunity’s response deadline plus 7 days — not on a fixed schedule. An opportunity with no deadline never expires from the cache and is excluded from future runs permanently.
If your Recommendations feed looks static run after run, this cache is almost always why: every previously-decided opportunity is skipped on the next run, deadline-less opportunities most of all. This is expected behavior, not a stuck refresh. If you open an opportunity that was rejected or marked low fit, and it currently has no live recommendation, the contract page shows a banner instead of hiding the decision:
  • Rejected → “We reviewed this and didn’t recommend it,” with the deal-breakers found.
  • Low fit → “We showed this to you, but it scored low,” with the issues found. A footnote reminds you these are surfaced deliberately — if the blocking issue is one you can work around (teaming on a set-aside, partnering in another region), it may still be worth pursuing.
The banner also shows a confidence percentage and how long ago the decision was evaluated, plus a standing note that it reflects your profile at the time of the run — updating your profile and refreshing will re-evaluate it. There’s no re-run, dismiss, or feedback control on the banner itself, just the explanation.

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Stage 3: Scoring

Whatever passes qualification (or is marked low fit) gets scored across four weighted dimensions: A low-fit opportunity has its final score reduced rather than zeroed, so it ranks below equivalent work without the blocking issue but keeps its relative position among other low-fit results.

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Fit tiers

Match score badge showing 82% and an Excellent Fit label
The Fit Ring on each card fills proportionally to each dimension’s weight, so the total filled arc equals the final score. When the AI’s confidence is low, the ring is drawn dashed and faded — a visual cue that the AI itself was less certain, not a rendering bug.

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Needs review and Low fit badges

  • Needs review — the AI couldn’t confirm fit from the available attachments. You’ll see one of two explanatory messages depending on why the attachments fell short.
  • Low fit — shown deliberately rather than hidden (when your Recommendation volume includes low-fit results). The card explains what’s blocking it: “Shown because it is related to your work, scored low because of this: [reason].”

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Learned insights: what thumbs feedback actually changes

Thumbs feedback doesn’t retrain anything in real time. A nightly digest reads feedback submitted since the last run and turns it into a capped list of learned patterns that the AI takes into account in future qualification, scoring, and retrieval. You can see and remove these patterns under Learned From Your Feedback in the Memories tab of your Company Profile.
Insights can only move a score — they are explicitly forbidden from causing a reject. A thumbs-down never removes an entire category of work from your feed.
Two details worth knowing:
  • Feedback is shared across your org. Insights are profile-scoped, not per-teammate, so a thumbs-down from any team member trains the same profile everyone else sees.
  • The pattern list is capped. Once it’s full, a new pattern can only get added by displacing an existing one. If your recent feedback doesn’t seem to be changing anything, this is often why — it’s competing with older patterns for a limited number of slots.

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Stage 4: Delivery

Every scored result is saved, whether or not it gets emailed.

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The email floor

Only recommendations scoring 50% or higher (“Possible” or better) are included in the email digest. Anything below that threshold is visible in-app but is never emailed — this is by design, not a delivery failure.

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Manual refresh: 5 per day, shared across your org

Recommendations Running button with the message Refresh in progress, new results will appear here automatically
Clicking Refresh Recommendations triggers a new run, capped at 5 manual refreshes per UTC day. The cap is shared across your organization, not per person — a refresh by any teammate counts against the same daily pool. Hit the cap and the button shows an error; start a refresh while one is already running and you’re asked to wait.
A manual refresh only regenerates profile-based recommendations (what you see under the Profile tab, and the profile-based portion of All). It does not touch Saved Searches-tab recommendations — those come from your saved searches’ scheduled email alerts instead. Refreshing won’t change them; see AI Qualification for how that pipeline runs.
Recommendations also regenerate automatically once a day as part of a scheduled batch that emails the digest — there’s no way to see or change the time this runs.

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The three dismissed surfaces

Dismiss all, Export, and Archive buttons above the Recommendations list
SamSearch has three places an opportunity can land after you say “not this one,” and they don’t share a Restore action: If you dismissed something from your Recommendations feed and are looking for a way to bring it back, it isn’t there — only opportunities dismissed from a manual Search have a Restore option.

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Feedback reasons

Both thumbs buttons open a dialog that requires at least one reason:
  • Thumbs up (adds the opportunity to Journey Hub): strong capability match, right location, right NAICS/certifications, right budget, agency relationship, realistic timeline, or other.
  • Thumbs down (dismisses the opportunity): wrong industry/NAICS, wrong location, budget mismatch, wrong contract type, capability mismatch, timeline too short, or other.
The plain Dismiss (✕) button on a card removes the opportunity with no reason required, and doesn’t feed the learned-insights digest.

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Why am I not seeing recommendations?

Check your Company Profile for the signal gate: you need at least one of positive keywords, NAICS codes, PSC codes, or a 40+ character business description. Without one of these, generation is skipped silently and the run still reports success.
Keyword matching is a literal substring match, not semantic. Check whether your exact phrase appears in the opportunity text — a close synonym or reordered phrase won’t match.
Code trust depends on the source. On SLED, Canada, and DIBBS, NAICS is SamSearch’s own inferred guess — a mismatch is ignored, not penalized. Codes also act mostly as a ranking boost rather than a strict filter.
If your Company Profile has no Operating Region set, location is left out of qualification entirely — the AI isn’t considering geography for your profile at all. Add an operating region to change this.
This is almost always the qualification cache. Every previously decided opportunity is skipped in future runs. Opportunities with no response deadline are excluded permanently, since their cached decision never expires.
Learned insights can only move a score, never cause a reject — a thumbs-down can’t remove a whole category of work. The insight list is also capped; if it’s full, your recent feedback may need to displace an older pattern before it has any visible effect.
Only results scoring 50% or higher are included in the email digest — this is the email floor. Lower-scoring results are visible on the page but intentionally never emailed.
Manual refresh is capped at 5 runs per UTC day, shared across your whole org — one teammate’s refresh counts against everyone’s quota. If a run is already in progress, you’ll see a different message asking you to wait for it to finish.
Manual refresh only regenerates profile-based recommendations. Saved Searches-tab results come from each saved search’s scheduled email alert instead — see AI Qualification.
Needs review means the AI couldn’t confirm fit from the available attachments. Low fit means the opportunity is related to your work but scored low for a stated reason — shown deliberately rather than hidden. Both are explained under Needs review and Low fit badges.
Check which of the three dismissed surfaces it’s in. Only opportunities dismissed from a manual Search (the Dismissed Opportunities page) can be restored — recommendations dismissed from the Recommendations page cannot.
That’s the qualification banner — it only appears when the opportunity was rejected or marked low fit and there’s no live recommendation for it. It explains the decision without adding the opportunity back to your feed.

Back to AI Recommendations

Return to the page reference — tabs, filters, and available actions.