SOP recall and evidence software

Turn SOPs into simple team recall checks

RecallAI converts approved procedures into short typed-answer reviews. Managers approve the content, assign it to teams, and see where people remember the process correctly or need reinforcement.

Your SOPs do not help if your team cannot recall them when it matters.

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SOP recall evidence
Source: Refund handling SOP
Partial match
0
draft cards
0
approved
0%
pass rate
Question

When should a refund be escalated to a manager?

Typed answer
When the customer has already received a replacement or the order is older than the normal return period.
Approved answer
Escalate if the order falls outside standard policy, if there has already been a replacement, or if there is a fraud or payment-risk concern.
Result
Partial match. The answer caught replacement history and return-window risk, but missed fraud and payment-risk concerns.
Follow-up
Review again in 3 days.
How it works

From SOP to recall evidence

Upload or paste an approved SOP. RecallAI drafts review cards from the procedure. A manager reviews and approves the cards before assigning them to a team.

1

Add an SOP

Upload or paste an approved procedure.

2

Generate review cards

RecallAI drafts short questions and expected answers from the SOP.

3

Manager approves cards

Cards are reviewed before they are assigned to the team.

4

Team members type answers

Users answer from memory before seeing the approved answer.

5

Managers review evidence

See answer quality, weak topics, review history, and follow-up needs.

Why typed answers matter

Recall is stronger evidence than completion

Many training tools show that someone opened a document or completed a module. RecallAI checks whether they can produce the correct answer without seeing it first.

This makes it easier to identify unclear SOPs, weak process knowledge, and areas where managers need to reinforce training.

Managers approve cards before learners study them
Team members type an answer before reveal
Answer quality is tracked against the approved answer
Review history shows where knowledge is weak
Follow-up reviews reinforce missed or partial answers
CSV evidence exports support manager reviews
Best fit for

Lean teams with SOPs and visible process mistakes

RecallAI is built for teams that already have SOPs and want a simple way to check whether people understand and remember key procedures.

Best starting point

A strong rollout starts with one team, one process, and one approved SOP.

one written SOP
one manager to approve generated cards
one team to complete short recall checks
one process where mistakes are easy to identify

Best suited for

Customer support teamsOperations teamsOnboarding teamsProcess-heavy SMBsManagers responsible for recurring SOP mistakes

Strong first use cases

Refund handling
Escalation rules
Customer support policies
Internal QA checks
Onboarding procedures
Repeated process mistakes
Product or service knowledge

SOP mistakes are not just training problems. They become customer experience, rework, and revenue problems.

Customer experience quality is closely tied to revenue outcomes. Forrester's CX Index connects CX quality and loyalty measures to revenue drivers, Zendesk reports that 73% of consumers switch after multiple bad experiences, Qualtrics with ServiceNow found poor customer service was the top reason customers switched brands, and Bain's loyalty research connects retention gains with material profit impact.

Manager approval

AI drafts. Managers approve.

RecallAI drafts review cards from approved procedures, and managers keep final control over what gets assigned to the team.

Your SOPs remain the source of truth. RecallAI adds a practical recall loop around them so managers can reinforce knowledge before mistakes repeat.

Evidence view

See where process knowledge is strong, weak, or unclear

Managers can review typed answers, approved answers, match results, and follow-up timing. The goal is practical visibility into what people remember before the same mistake repeats.

Strong
Correct recall
Partial
Needs follow-up
Weak
Review again
FAQ

Common questions

How does the study rotation work?
Once a manager uploads a deck and approves the AI-generated cards, those cards automatically enter the team's rotation. In Auto-Rotate mode every active card is surfaced to all team members — no extra scheduling needed. Agents open the Study screen, answer the card, and RecallAI handles the rest.
How does RecallAI decide when to show each card again?
RecallAI uses FSRS (Free Spaced Repetition Scheduler), a research-backed algorithm that personalises review intervals per person. Cards answered confidently are shown less often; cards that caused hesitation come back sooner. Over time the system adapts to each agent's memory curve.
How are cards created from our SOPs?
Upload a deck and RecallAI's AI drafts question-and-answer recall cards from the content. A manager or admin reviews every card in the Pending queue and either approves or edits them before they go live. Nothing reaches the team without a human sign-off.
Can managers see who knows what?
Yes. The Stats screen shows per-card and per-deck performance across the team — average recall scores, review counts, and where individual agents are struggling. This makes it easy to spot knowledge gaps before they turn into mistakes.
What is the difference between Auto-Rotate and Manual mode?
Auto-Rotate (the default) pushes every approved card in the organisation into the team's daily rotation automatically. Manual mode gives managers fine-grained control, letting them flag specific decks as active for study while keeping others off the rotation.
Does RecallAI replace our existing knowledge base or LMS?
No. RecallAI sits on top of your existing documentation — your SOPs stay where they are. RecallAI converts that knowledge into short recall checks, tracks team retention, and surfaces gaps. Think of it as the layer that closes the loop between documentation and verified understanding.

Start with one SOP, one team, one process

Run a focused pilot and see where your team's process knowledge is strong, weak, or unclear.