We built Implicit because knowledge keeps breaking at the same places.
Every company, regardless of industry, hits the same five walls:
Creation. Someone knows something critical. It never gets written down.
Storage. It gets written down somewhere. Three conflicting versions now exist.
Discovery. You find the docs. Search returns 14 results.
Understanding. You read them. The answer still requires three follow-up questions to a senior teammate.
Application. You get the answer. Next quarter, the same question starts from scratch.
For most teams, this is a productivity problem.
For maintenance and support teams, it's a safety and compliance problem. A wrong answer on a service procedure grounds an aircraft. A wrong answer on a customer issue costs the account. The stakes make the knowledge layer matter more, not less.
That's the environment we're building for. Navigators that pull from your actual sources, return the actual answer, and show you exactly where it came from.
Knowledge that doesn't break.
#implicitcloud #knowledgeOS #AI #maintenance #customersupport
A developer tickets this at 2am.
Card declined. Customer says their balance is fine. They're asking why.
The answer isn't in one place. It's spread across the API reference, the error handling docs, and issuer behaviour that Stripe doesn't control. Finding it under pressure takes longer than it should.
We built the Stripe Expert Navigator for exactly this.
Q: A customer is getting card_declined with insufficient_funds but their card has available balance. What should I tell them?
A: insufficient_funds comes from the card issuer, not Stripe. It can appear despite an available balance for a few reasons. Pending authorizations holding funds not yet reflected in the balance, per-transaction limits lower than the charge amount, FX conversion buffers, or issuer-side risk rules flagging the transaction.
Advise the customer to check pending charges, confirm any limits with their bank, and try an alternative payment method if needed.
Source: Stripe API Reference / Handling errors
That answer took seconds. Try it yourself: https://t.co/GMxLX4wmrQ
What's the Stripe question your support team gets asked most?
#stripe #technicalsupport #developerexperience #fintech #implicitcloud
Generic AI reads your docs. Implicit's Navigators understand them.
A general purpose AI will search your files, pull a few relevant chunks, and give you something to work with. For most use cases, that is genuinely useful.
For maintenance teams working from technical manuals, and support teams answering customers mid-call, "something to work with" is not enough.
The difference comes down to two things.
First, grounding. Every answer from a Navigator traces back to a specific source document, section, and page. Your technician is not trusting the AI. They are trusting the AMM. The Navigator just found it faster.
Second, calibration. A well built Navigator knows when the evidence in your documentation is unclear or incomplete, and says so. Confident wrong answers are the most dangerous kind in maintenance and support environments.
Most AI tools make your team faster at searching. Implicit makes your team faster at knowing.
What would that look like in your workflow?
hashtag#AI hashtag#knowledgemanagement hashtag#maintenance hashtag#customersupport hashtag#implicitcloud
In aircraft maintenance, a wrong answer isn't just inefficient. It's a safety incident.
AMMs are hundreds of pages.
Technicians are under time pressure.
And AI that confidently makes things up is worse than no AI at all.
That's why every answer from the Cessna Navigator includes a source citation — the exact document, section, and page.
So the technician isn't trusting the AI. They're trusting the AMM.
The AI just found it faster.
What's your team's policy on AI in safety-critical maintenance workflows?
#aviation #aircraftmaintenance #MRO #aviationsafety
There are two kinds of knowledge in every company.
The kind that's written down.
And the kind that lives in someone's head.
Most companies are reasonably good at the first. Notion pages, Confluence wikis, shared drives full of docs nobody updates. It exists. Somewhere.
Almost none have a plan for the second. The procedural instincts, the judgment calls, the "I just know from experience" answers that never make it into any doc.
In manufacturing maintenance and customer support, that second kind is what actually keeps things running. It's the technician who knows which component fails first. The support lead who knows which edge case breaks the integration. The CS manager who knows why that one customer is always about to churn.
When that person leaves, gets promoted, or just gets pulled into a three-hour meeting, everything slows down.
The companies building a durable advantage aren't just documenting more. They're building systems that capture what their best people know before it walks out the door.
Which type of knowledge is holding your team back most right now?
hashtag#knowledgemanagement hashtag#manufacturing hashtag#customersuccess hashtag#implicit
Your best maintenance engineer just retired. What left with them?
10 years of knowing which part fails first. Which manual to trust. Which shortcut saves 3 hours.
That knowledge isn't in any doc.
Most companies don't realize it's gone until a $2M machine goes down.
This is the knowledge problem nobody talks about in manufacturing, and it's not a people problem. It's a systems problem.
The companies that solve it don't just hire better. They build a layer that captures what their best people know, so the next person doesn't start from zero.
What's your plan when your most experienced person walks out the door?
Your support team answered the same question 847 times last quarter.
Not an exaggeration. Pull the ticket data — you'll find a handful of questions that come up again and again, answered slightly differently each time, by whoever happened to be on shift.
That's not a people problem. That's a knowledge problem.
When the answer lives in a Confluence page from 2022, a Slack thread from Q3, and your team lead's brain — you're not doing support. You're doing archaeology.
The cost isn't just time: → Inconsistent answers erode customer trust → New reps take months to get up to speed → Your best people burn out answering the same questions on repeat
The fix isn't more documentation. It's a knowledge layer that actually works — one your team can query mid-call and get the right answer, sourced, in seconds.
How many hours does your support team spend finding answers vs. giving them?
#customersupport #customerexperience #knowledgemanagement #CX
Nigeria isn't currently on Stripe's supported-country list, so direct merchant accounts aren't an option there. The standard workaround is Stripe Atlas, which incorporates a U.S. entity and gets you a Stripe account through that. Stripe Connect is the other path if you're integrating with a platform that handles cross-border onboarding for sellers. Free Stripe expert chatbot if you want to walk through the Atlas option in more detail: https://t.co/rQyZeV67Yj
At your volume, skip the public sign-up loop. Go to https://t.co/sEsS6fW7Kc, then click "Contact sales" and mention your INR run rate, Stripe's team grants high-volume invites this way and can also negotiate custom pricing. If you need a U.S. legal entity for cleaner cross-border payouts, Stripe Atlas spins one up and unlocks the full product suite. Free Stripe expert page for the rest of the integration questions: https://t.co/rQyZeV6FNR
Most common reason: reserve releases or pending adjustments got added to your balance right before the payout generated, and late-posted refunds or fee accounting can bump the gross too. Click the payout in Payments > Payouts, open the linked Balance transaction, then filter Balance > Transactions by that payout ID to see every line that contributed. Free Stripe expert chatbot if you want help walking through it: https://t.co/rQyZeV67Yj
@disclosetv i think the FIS pairing is the tell. vertical agents work when someone wires curated domain knowledge in, otherwise it's just chatgpt with steps. https://t.co/dxLVslT8tc has been trying this pattern across navigators
@AviationAll_ Delays like this are usually less about the building and more about who owns the manuals and SOPs on day one. Our Cessna Navigator at https://t.co/dxLVslT8tc, takes that exact piece off the table.
@TextronAviation@NetJets@Cessna first three out is when the line techs and the SBs they'll be writing start mattering. fleet ops is where docs really compound. the cessna navigator at https://t.co/dxLVslT8tc, fits the lifecycle
@BrendanFalk Have you noticed even when docs are public they're written for the 80% case and break the moment CS needs an unusual flow? Been chewing on the HubSpot Navigator at https://t.co/dxLVslT8tc, less brittle there.
@bhalligan i wonder if the AI-native version is less culture code and more 'knowledge code': which agents your team trusts, what they can read. been mucking around with the hubspot navigator from Implicit, similar bet
@RevenueCat@stripe Have you considered how docs split between RC and Stripe Billing when devs hit edge cases? That's where teams I've seen get stuck. Been digging into our Stripe Navigator, fits that gap.