Flocci Pulse: Nine Engagement Tools, One Engine an Agent Can Drive

The word appears on the projector before the speaker finishes asking the question. Then another, bigger. Then a third crowds in from the left, and within eight seconds a word cloud has bloomed over the stage — three hundred phones in a dark auditorium, no app installed, no account made, everyone just typing into a six-letter code and watching their own answer swell into a shared picture in real time. The presenter didn’t build that moment in five tools. She built it in one, from a sentence she typed that morning: “make me a word cloud asking the room what slows their deploys.” Something read the sentence, picked the format, generated the structure, and now the room is answering itself back onto the wall.

Why nine engagement formats collapse into one engine

Every team that runs on audience feedback pays a quiet tax, and the tax is measured in browser tabs.

One tool for live-event polls. A different one for the post-session quiz. A third for the quarterly survey. A fourth for the HR pulse check. A fifth for the personality assessment the People team runs on new managers. Each has its own login, its own data model, its own analytics dashboard that counts things slightly differently than the others, and its own line on the invoice. Getting from a raw interaction — a show of hands, a typed answer, a rating — to something you can actually analyze is a manual slog of exports and spreadsheets. And not one of these tools can be operated by the AI assistant now sitting in the same team’s other windows.

The result is that the easiest interactions to run are the shallow ones, and the insight that matters most stays trapped as unread free text in a column nobody clusters. The tooling shape actively discourages the depth.

Flocci Pulse’s thesis is that all nine of those tools are, underneath, the same tool. They all take a prompt from a creator, collect structured responses from an audience, and hand back an analysis. The formats differ; the engine doesn’t have to.

One engine wearing nine faces

Under the hood, Pulse is not nine apps stapled together. It is a single type-polymorphic data model — content_items, content_options, questions, responses — that every format writes into, with unified list, get and delete endpoints across all of them. A poll and a persona assessment are the same rows with different type discriminators. (The old polls/poll_options views are kept as compatibility shims, so nothing that once spoke the legacy shape breaks.)

That single spine is what makes the breadth honest rather than sprawling. Nine formats on one model is a feature you can trust; nine formats on nine models would just be the tab-sprawl problem moved inside one login.

AI on both ends of the loop, not bolted to the side

Most products that say “AI” mean a chatbot pinned to a sidebar. Pulse means something more structural: intelligence is wired into both the authoring end and the analysis end of the content lifecycle, and neither is a novelty.

On the way in, the Magic Wand takes a free-text prompt, classifies it into the right one of the nine content types, and emits a validated JSON structure for that type. If validation fails, it retries — up to three times — rather than handing you a broken draft. That retry loop is the difference between a demo and a tool: the output is guaranteed to be a correctly-typed, schema-valid content item, not a plausible-looking blob you have to repair. Generation runs through pluggable adapters (Gemini as the default, with DeepSeek and Nvidia NIM behind the same interface), now routed through Flocci’s shared intelligence service.

On the way out, the free-text answers — the part every other tool leaves as an unread column — get worked. XM waves run async AI sentiment scoring on a 0–100 scale, cluster verbatims into semantic groups, and produce an AI executive narrative that compares one wave to the last. The open text stops being a graveyard and becomes the headline.

The part that makes it agent-native

Here is the claim that separates Pulse from every incumbent in the category: an AI assistant can operate the entire product, unattended, over a standard protocol.

Pulse ships a built-in MCP server alongside a full OAuth 2.0 authorization server — implementing dynamic client registration (RFC 7591), server metadata (RFC 8414) and RFC 9728. That machinery is what lets an assistant like Claude.ai log in, create a poll or quiz or survey or persona assessment or even a branching HR wave, launch it, and read back the AI-clustered, sentiment-scored results — end to end, programmatically. This is not a chatbot that answers questions about your account. It is an engagement platform an autonomous agent can actually drive, because the same clean API the humans use is the one the agent gets.

1. The agent authenticates

   Claude.ai registers as an OAuth client against Pulse’s authorization server and gets a scoped token — the standards-compliant path, not a bespoke key handshake.

2. It creates and launches content

   Over MCP, the assistant calls the same unified endpoints a human would — spin up a survey, a quiz, or a branching XM wave, and set it live with a join code.

3. The audience answers, friction-free

   Participants join by six-letter PIN or QR, no registration, in a guest session. Their responses land on the shared engine regardless of who created the content.

4. It reads back real insight

   The assistant pulls results already sentiment-scored and semantically clustered — the analysis, not just the raw counts — and can compare this wave to the last.

The same intelligence runs the human path, too. Beyond the Magic Wand, Pulse generates adaptive branching journeys — one-question-at-a-time steppers that only submit the path a respondent actually walked — for both HR waves (Journey Intelligence, added this month) and personality assessments (Persona Intelligence). And the AI doesn’t just draw those branching graphs; it validates them, at both the generation gate and the launch gate, for forward-only routing, reachability, and acyclicity. A journey that could strand a respondent in an unreachable node, or loop them forever, doesn’t ship.

Built so the audience is never the one who pays

Two design decisions reveal what Pulse actually optimizes for, and both cut against the grain of how engagement tools usually behave.

The first is friction. Participants never make an account. They join by PIN or QR into a guest session that expires after 24 hours, answer, and leave. The creator’s reach is never gated behind a signup wall, which is the difference between three hundred phones answering and thirty.

The second is subtler and more principled. Pulse runs on a per-user credit wallet, and creators are charged up front — hit zero and your own actions are blocked with a clean 402 INSUFFICIENT_CREDITS. But participant responses are never blocked. The wallet is deliberately allowed to go into debt rather than turn an audience away mid-session. If you run out of credits while three hundred people are answering, the answers keep landing and you settle up after. The person who pays is the creator, never the room.

Standing on shared Flocci identity, payments and AI

Pulse doesn’t re-implement the plumbing every SaaS reinvents. It did a clean cutover to the shared Flocci Identity service — thin proxies, JWKS middleware, cross-app SSO verified in practice (register in Pulse, log in to Leads) — and moved email and OTP to the shared Notification service, PayU to the shared Payment service (its in-app hashing and Standing-Instruction engine deleted), and text generation to the shared Intelligence service on DeepSeek, with app-side AI charging removed and exactly-one-debit verified. This month it also adopted the Org Identity service for org registration, switching, members, teams and roles, with strict plane isolation between individual and organizational content. Every one of those providers is reached through the single Flocci gateway. A few things stay deliberately product-owned — Gemini-vision runs app-local, the admin-panel JWT is its own, and web push isn’t migrated yet — and the docs say so plainly rather than pretending the cutover is total.

Underneath, the discipline shows. Real-time emission all funnels through one SocketService with rate limiting (100 events/sec per content item), 100ms dedup windows and three-attempt retries, with optional Redis-backed horizontal scaling. Live presentations keep slide timing consistent across projector, presenter remote and every phone in the room via an NTP-based server clock. Participant analytics yield country, browser language, device class, UTM campaigns and drop-off funnels — without ever capturing a raw IP address or setting a participant cookie. Privacy-first isn’t a slogan bolted on; it’s a constraint the analytics were built under.

That’s the shape of the argument, and the product is the proof of it. The category has spent a decade convincing teams that polls, quizzes, surveys, HR pulses and personality tests are five different problems that need five different tools. Pulse says they were always one problem — content in, responses back, analysis out — and once you build them on one engine, the last question answers itself: why should a human be the only thing that can drive it? The word cloud blooming over that stage was made by a sentence. The next one might be made by an agent, while the person who used to wrangle five dashboards watches the room answer itself back onto the wall.