About Weave Communications

Weave (NYSE: WEAV) is a leading all-in-one customer experience and payments software platform for dental, optometry, veterinary, and other local healthcare practices. The company serves customers at over ~35k locations. Weave Payments enables practices to accept card-present and online payments inside the Weave platform. Weave primarily uses Stripe Connect to power Weave Payments for its customers.

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Snapshot of Impact (AI at the core)

• ~88–89% reduction in daily triage volume, driven by AI-prioritized alert queues.
• ~33x jump in portfolio visibility to near-100% always-on monitoring, with AI checks across meaningful-volume merchants and their transactions.
  
• 4x faster incident response, via AI early-warning signals.
  
• 20–40 hours/week reclaimed, with each analyst hour 4–5x more impactful as AI narrows focus to the highest-risk decile.
  

Proactive merchant outreach, replacing reactive support.

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The Challenge

Before Coris, Weave’s payment-risk reviews were manual and fragmented:

• Analysts would export lists from the Stripe Dashboard (large/negative balances, issuer “high-risk” blocks, high-value tickets), paste them into Google Sheets, then hand-check websites, reviews, and Google Maps for each merchant.
  
• Work was duplicated, decisions varied among reviewers, and true coverage was minimal: ~3% of merchants and 1% of transactions received any manual scrutiny.
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“We spent hours every day looking at hundreds of accounts - mostly good ones. The painful misses were the ones we didn’t get to in time.” - Emereth Griffin, Payments & Risk Lead, Weave

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Why Weave Chose Coris

End-to-end merchant risk with AI. Point tools and native Stripe controls addressed portions of the problem. Coris combined AI-powered detection, decisioning, and monitoring across onboarding and the full merchant lifecycle. All with Stripe-specific depth and minimal lift from Weave’s teams.

Low-lift go-live. Coris’ no-code Stripe integration meant Weave could start in days, then iterate rules and thresholds without stalling its roadmap.

Flexible to policy. Coris tuned AI signals + rules to Weave’s risk posture and portfolio behavior - rather than forcing policy to fit a tool.

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“Coris covered our full risk surface, seamlessly worked alongside Stripe, and did it with almost no lift. The flexibility and willingness to customize sealed it.” - Emereth Griffin

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Implementation (Weeks, not Months)

Weave ran Coris in parallel with spreadsheets initially to validate parity, then flipped primary triage into Coris within one week.

• Go-live in days (Stripe key + sandbox validation)
  
• Started broad, then tightened Risk Rules as AI signal quality was observed
  
• MerchantProfiler automatically centralized the evidence analysts used to scrape manually
  
• Automated pausing of payouts for the merchants on Stripe and AI-triggered alerts turned reactive firefighting into proactive outreach
  
  

“Within a week, our daily list went from ~80 accounts to about 9–10. We focused only on the riskiest decile and moved faster.” - Emereth Griffin

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The Coris Solution at Weave

Where the AI shows up

• Portfolio-wide anomaly detection & scoring: Coris’ models continuously evaluate merchants and transactions, learning typical patterns (balance changes, refund spikes, large-ticket behavior, issuer risk signals) and surfacing outliers automatically.
  
• AI-prioritized alert queues: Analysts see the few accounts most likely to matter, ranked by model score, so reviews start with the top-risk decile.
  
• Rules + AI, not rules vs. AI: Weave’s policy rules run with Coris’ AI model, cutting false positives while catching edge cases.
  
• AI-triggered workflows: Events like suspected ATO behavior or failed payouts can trigger automatic interventions and outreach - often hours or days earlier than before.
  

Analyst-ready context in one place

• MerchantProfiler aggregates a merchant’s firmographics, online presence signals, processing patterns, payout health, declines/disputes, and behavior changes - no tab-hunting.
  
  

“We used to hunt for needles in a haystack. Now Coris hands us the needles - and the context to act.” - Emereth Griffin

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Results

1) Smaller, higher-signal workloads

AI triage cut daily reviews by ~89% (from ~80/day to ~9–10/day), so analysts spend time where judgment matters most.

2) Visibility at scale

Coverage jumped from ~3% to nearly 100% automated AI checks across meaningful-volume merchants and their transactions - roughly a 33x increase.

“We went from seeing almost nothing to seeing everything.” - Emereth Griffin

3) Faster detection and containment

AI early-warning signals and event automations made Weave ~4x faster on time-to-detect and time-to-act (e.g., 4 days → 1 day, 8 hours → 2 hours) for issues like suspected ATO or merchant-initiated risk.

4) Hours back, redeployed

Anywhere from 20 hours to 40 hours/week saved; each hour saved for the Weave team is 4–5x more impactful because AI narrows the funnel to the highest-risk accounts and provides ready-to-use context.

5) Better merchant experience

With AI-triggered proactive outreach (e.g., failed payouts), Weave calls merchants first - turning potential churn moments into trust-building conversations.

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Favorite Capabilities

• AI-assisted Risk Rules Engine: fast to author, easy to tune, all while aligned with Weave’s policies
  
• MerchantProfiler: a single location with all the signals analysts used to gather manually
  
• Stripe-specific depth: built to complement Stripe’s strengths and close its gaps for platforms
  
  

“Coris let us scale risk without scaling headcount - and gave our leadership real confidence in our payments program.” - Emereth Griffin

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Pick a partner that hands you the needles - not another haystack!

When it comes to advising other software companies, Emereth Griffin had a few things to say:

• Buy, don’t build (first). Building a risk stack sounds great until you factor in time, hiring, and opportunity cost. Start with a partner that can go live in days, then decide what (if anything) to insource later.
  
• Start broad, then tighten. Begin with permissive rules to over-alert for a short calibration window. Label outcomes, learn the patterns, then prune to a high-signal rule set. You’ll avoid blind spots and cut noise fast.
  
• Use rules with AI (not rules vs. AI). Let AI scoring prioritize the queue; use policy rules to encode your risk appetite by segment. This combo reduced our daily triage from ~80 → ~9–10 without missing the edge cases.
  
• Centralize merchant context. Put firmographics, online footprint, payout health, disputes/declines, and behavior deltas on one screen so analysts never have to tab-hunt. It turns each hour of review into 4–5x the impact.
  
• Monitor the whole portfolio, not a sample. Aim for ~100% automated checks across meaningful-volume merchants and their transactions. Add event automations (e.g., failed payouts) to shift from reactive to proactive outreach.
  
• Choose processor-aware tooling. If you’re on Stripe Connect (or Adyen, etc.), pick a platform with deep native integrations that closes processor-specific gaps instead of forcing your policy to fit a generic tool.
  
• Keep humans for the top 1–2%. Train analysts on patterns the AI surfaces and give them clear escalation SOPs. Human judgment should be reserved for the few cases where it truly moves the needle.
  
• Communicate in plain business terms. Translate risk performance into portfolio coverage %, SLA improvements, and exposure reduction - so Finance and Leadership see progress, not plumbing.

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Looking Ahead

Weave will continue to tune AI thresholds and rules by segment, expanding the set of AI-triggered workflows (e.g., payout health, refund behavior, issuer signals) while keeping analysts focused on the 1–2% of cases where expert judgment moves the needle most.