AI coworker · lives in Slack

Not another AI tab.
A coworker in the thread.

Your whole company @mentions the same Franky, right in the Slack threads where work already happens. It remembers your org, cites every source, and gets used by the people who'd never open an AI tool.

Book a demo

95% of our own company uses Franky every week — and Sales adopts it faster than Engineering.

Multiplayer · Org-memory · Source-cited · Used by non-engineers

#ask-anything 204 members
M
mei.tan Marketing 3:42 PM
@franky do free-trial users get the onboarding webinar invite? and who owns that flow?
F
Franky App 3:42 PM
Pulled the decision from #growth-decisions:
  • Yes — trials with 5+ seats auto-get the webinar invite
  • Decided 2026-03-14; solo trials get the email sequence instead
  • Owner: @kane.lin — automation lives in the lifecycle doc
Want the link to the playbook?
Sources: Slack · Drive · Notion decision-log
Built for
B2B SaaS (payments & data API) Vertical SaaS Marketing & DX agencies Electronics distribution Finance · insurance · securities E-commerce platforms

If your team handles repetitive customer-facing work with high variability — proposals, renewals, support replies — Franky is built for you.

"Isn't this already solved?"

The model isn't the moat.
Your org context is.

Franky runs on Claude — the model is the easy part. Bolting MCPs onto a chat app gets your 5 engineers a powerful single-player tool. It doesn't live in the thread where the other 95% work, doesn't remember your org between sessions, and breaks the day someone changes a schema. Here's what that gap actually looks like.

AI in a tab
A
B
C
D

One person learns.

One brilliant answer in one private window. Closes the tab — the context dies. Teammates never see the question, the answer, or the trick.

AI in the thread
A
F
B

The whole channel learns.

Same question, asked where everyone works. The answer is public, cited, and remembered — the next person searches the thread instead of re-asking.

ChatGPT / Claude Claude + MCPs
(build it yourself) 		Another hire Franky
In the thread, multiplayer ✗ single-player ✓ ✓
Remembers your org over time ✗ per session in 3 mo ✓
Adopted by non-engineers ✗ power users only ✓ ✓
Cites sources for every answer ✗ ✗ sometimes ✓
Someone maintains it for you n/a you do ✓ ✓
Cost / month $20 $50–500 + eng time $8K+ $1–15K

Every model can plan, write SQL, call tools now. The moat is whether the agent lives in your org — and remembers it.

Where Franky lives

In the thread — not a tab you switch to.

This is why non-engineers actually use it. No new app, no new dashboard — @mention Franky in the channel you're already in. Everyone in the thread sees the answer, and it's cited every time.

Slack — Crescendo Lab
cs-tickets-active
M
maya.f 10:48 AM
@franky Acme is asking about extending their legacy SMS plan. What was last quarter's grandfathering decision?
F
Franky App 10:48 AM
Found it in #pricing-committee 2026-Q1:
  • Legacy SMS grandfathered through 2026-09-30
  • 30% discount on migration to WA bundle
  • Owner: kane.lin
↪ Pricing policy · Decision doc

Native Slack integration

@mention Franky in any channel, DM, or thread. Works on desktop and mobile. Every answer cites its source, links the doc, and names the owner.

Microsoft Teams — Marketing
Marketing
General
Q3 Campaigns
Brand
Analytics
Posts Files Notes
K
Krystal · 9:14 AM
@Franky — pull the May campaign report. Spend, conversion, NPS. One slide.
F
Franky Bot 9:14 AM
May "Summer Awakening":
  • Spend $24,810 (GA4 + Meta)
  • Conv 412 tickets · CTR 3.2%
  • NPS 8.4 (Typeform, n=128)
Drafted slide → Marketing/May-report.pptx
↪ GA4 · Meta Ads · Typeform · Ticket backend

Microsoft Teams support

Add Franky to any Teams channel or group chat. Cross-platform answers stay consistent — same memory, same citations, same trust loop.

Reads from
GitHub
Asana
BigQuery
Drive
Metabase
GA4
Meta Ads
Gemini
+ 200 SKILLs
Trust, by architecture

No source, no answer.

Every reply follows the same pipeline — and if Franky can't ground a claim in a real source, it says so instead of guessing.

1 · You ask
@franky why did Acme's usage drop in May?
→
2 · Parallel retrieval
Queries every connected source at once
BigQuery · usage events Slack · #cs-acme thread Asana · open tickets Drive · QBR notes
→
3 · Cited answer
"Usage dropped 31% after their May 12 API migration — two failing webhooks, ticket open since May 14."
↪ 4 sources linked · owner named

You don't have to trust Franky. You verify the links.

Proof · Internal Dogfooding

This isn't a pilot demo.

Four months of real production usage inside Crescendo Lab. The numbers below are from our own BigQuery telemetry.

95%
Employee adoption
(92 of 138 in last 30 days)
4,708
Mentions in 30 days
35
Power users
(10+ mentions / week)
4 mo
Continuous production
since Jan 2026

Source: Cloud Logging · franky-franklin-production · 2026/4/13 — 5/13. B2B SaaS industry median adoption is 30–40%.

Heavy users · in their own words

What changes when Franky's in the workflow.

Paraphrased from interviews with Crescendo Lab's top 20 most-active Franky users. Roles preserved, names withheld.

"

BD proposals used to be 3–4 hours each. Now I close the loop in under one — Franky brings the 60%, I shape the rest.

BD Lead, Japan · 2026 Q2
"

I stopped waiting for the data team. I just pull it myself with two messages. The 'I'll get back to you' delay is gone.

Senior CSM, Taiwan · 2026 Q2
"

Franky guesses the customer's real ask 60–70% of the time. That's the slowest part of CS work — gone.

CS Operations Manager · 2026 Q2
Real customer · May 2026

An event-production team.
50 people. Five data platforms.

Marketing runs ticketed campaigns across GA4, Meta Ads, the ticket-sales backend, Looker Studio, and Typeform surveys. Every campaign report used to be a 4-hour cross-platform assembly. Production, marketing, and engineering each owned a piece — no single person had the full picture.

We installed Franky in their Slack workspace and connected the five sources. Their marketing lead now asks in one line: "Pull this campaign's spend, conversion, and survey responses." One question. Five tools. Every answer cites its source.

Onboarded in 2 weeks · production usage since 2026-05-08 · token-based billing

5
Data platforms unified through one Slack mention
2 wks
From kickoff meeting to org-wide rollout
50
Employees across three departments
Before Franky
4 hours
Per campaign report · 3 people involved
GA4 Meta Ads Ticket backend Looker Studio Typeform
Manual cross-platform pulling
Spreadsheet copy-paste
Wait on Eng for the ticket export
Re-check totals before sharing
A spreadsheet nobody trusts on the first send.
After Franky
~8 minutes
Per campaign report · 1 person, in Slack
@franky Pull May "Summer Awakening" — spend, conv, NPS, top survey themes.
Franky queries all 5 sources in parallel, cites every number.
Returns a ready-to-paste report skeleton.
Marketing lead polishes the framing, sends.
Same report. Trustworthy on the first send.
How Franky actually works

Franky gives you the 60%.
You bring the 40%.

Heavy users don't ask Franky and ship the answer. They clarify the question first, ask Franky, verify the response, then polish for the customer. The slow middle — finding, fetching, assembling — disappears. The 40% that stays is the part that needs human judgement anyway, and you keep your name on it.

STEP 01

You translate the ask

Customer language is rarely the question Franky can act on directly. You compress it into one clear request — the same step a senior on your team would do.

Human work
STEP 02

Franky finds + returns

Pulls from connected sources, cross-references, returns a structured intermediate answer with every source linked. No source, no claim.

Franky's job
STEP 03

You polish + ship

Tone, customer context, brand voice — added by you in minutes. Same final quality, fraction of the time on the slowest steps (finding, fetching, framing).

Human work
Org memory Someone asks Cited answer Memory grows Better next answer
The flywheel

Every question makes the next one better.

Each interaction becomes proprietary memory for your org — who owns what, how your team phrases things, which decisions were made and why. None of it leaves your workspace.

That's why month 6 looks nothing like week 1. Generic tools reset to zero every session. Franky compounds — and the longer it runs, the harder it is to replace with anything that starts cold.

What Franky Does

Real jobs. Real teams. Asked every day.

Six jobs validated by Crescendo Lab's most active users — Customer Success, BD, Marketing, Engineering.

Customer Success

Draft a customer reply

"@franky customer X is asking about pricing migration — Pulls ticket history, finds the playbook, drafts the response."

Marketing / Product

Pull cohort metrics on demand

"@franky how many MAAC trials converted in March? — Queries BigQuery, returns the cohort breakdown with source SQL."

BD / Sales

Assemble a customer proposal

"@franky draft a proposal for Acme — they sell B2B logistics, evaluating CAAC. Pulls past similar deals, current pricing matrix, competitive playbook. Returns a 60% draft."

Customer Success Manager

Build a renewal context pack

"@franky pre-renewal context for Beta Co — Pulls usage trend, last 90 days of tickets, NPS, open feature requests. Flags the two risks I should address first."

Eng / SRE

Root-cause a prod incident

"@franky why did checkout error rate spike at 3pm? — Looks at deploys, Sentry, recent PRs. Names the commit."

Customer Support

Look up history before replying

"@franky what did we tell this customer about migration last quarter? — Finds the ticket history, the playbook applied, the decision owner. One reply, one consistent answer."

Who Franky is for

Three signs Franky will pay for itself.

From interviews with our heaviest users, the pattern is consistent: Franky matters most where repetitive-but-custom work meets customer touchpoints and complex knowledge.

↻

Same job, different details — every week

BD assembles a proposal per prospect. CSM tailors a renewal per account. Support adapts an answer per ticket. The shape repeats; the contents never do. That's exactly the work Franky's 60% covers.

5+

Many customer touchpoints

Engage → propose → sign → onboard → support → renew. Every touchpoint pulls on org knowledge — and every handoff between them loses context someone has to refetch.

SOT

Knowledge too complex to memorize — but it's written down

Pricing rules, product edge cases, past decisions live in docs, threads, and tickets. Too complex for anyone's head; rich enough that an agent with access can answer correctly — with the source attached.

If the repetitive work already hurts enough that you'd pay to make it stop — and the answers exist somewhere in your org — Franky's first job is to fetch them: fast, cited, in Slack.

Pricing

Land cheap. Expand on usage.

Three tiers that map to a natural buyer journey: try → adopt → standardize.

Pilot
$1–2K/ month

1 team · capped usage

Less than a part-time intern.

  • 30-day trial
  • Slack workspace install
  • Onboarding skill seed
  • Email support
Start a pilot
Most customers land here
Growth
$5–15K/ month

Org-wide · usage-based

Less than one entry-level hire — works across your whole org, 24/7.

  • Unlimited employees
  • Pay-per-token over baseline
  • Self-serve skill authoring
  • Shared Slack support channel
Enterprise
$30K+/ month

Private deploy · SLA · SOC 2

Replaces a team's worth of context-fetching.

  • Custom GCP deploy
  • Dedicated CSM
  • Custom SKILL development
  • SOC 2 & security review
Talk to sales

All tiers are token-based managed pricing — AI model usage at cost × 1.4 (transparent 40% margin covering orchestration, permissions, monitoring, and support). You see exactly what you spend, scaled to actual usage. POC trials start at cost price.

FAQ

Common questions.

What buyers usually ask before installing Franky.

How long does it take to onboard Franky?
One afternoon for the pilot install — Slack app + 2–3 read-only integrations (GitHub, Drive, Metabase). Org-specific SKILLs ship over the following 2–4 weeks as your team's actual usage informs what to build.
What data does Franky read?
Only the sources you connect, with the same access rights as the person asking. Franky inherits Slack, GitHub, and Drive permissions at query time — it never sees more than the requester is already allowed to see.
What if Franky gives the wrong answer?
"No source, no answer" is built into the system prompt — every claim links back to the source it came from. A persona-drift detector flags when Franky reaches beyond its grounding. Honest take: single-domain queries are our strongest area. Cross-product, cross-domain queries are our weakest — we surface confidence, we don't hide it, and we're rebuilding this for Q3.
How fast does Franky respond?
Sub-second for cached SKILLs and direct lookups. 10s to a few minutes for cross-source synthesis (BD proposals, renewal context packs). Heavy users treat Franky as async — they fire the question, switch context, come back to a structured answer. Async-by-default in the UI ships Q3 2026.
Is Franky SOC 2 / GDPR compliant?
SOC 2 Type II is on the Enterprise roadmap for 2026 Q4. Enterprise deployments run entirely inside your own GCP project — data never leaves. Pilot and Growth tiers run on Crescendo Lab's managed GCP with regional data residency.
What happens to our data if we churn?
You take everything with you. Connected source data was never copied to our infra — Franky queries it live. The org memory built from your team's interactions is exportable as structured JSON on request, deleted from our systems within 30 days of cancellation. No lock-in by design.
How does pricing scale with usage?
Pilot ($1–2K) is flat-rate, 1 team. Growth ($5–15K) is org-wide with a baseline usage allowance plus pay-per-token over it — most customers settle around $8K/month. Enterprise is custom based on private deploy + SLA needs.
Can different teams run different sub-agents in one workspace?
Yes — each team can configure its own SKILL set, persona, and data sources under one Franky workspace. CS team's Franky and Eng team's Franky see different worlds; permissions inherited from Slack channels. One bill, many specialized agents.
Can we build our own SKILLs?
Yes — Growth and Enterprise tiers include self-serve SKILL authoring. Customers regularly contribute SKILLs back to the library (currently 200+). New orgs benefit from every SKILL that ships.
Franky

Stop asking people.
Start asking Franky.

Install Franky in your Slack workspace and turn your org's scattered knowledge into one source of truth.

or email [email protected]