apply_loantenure?amount?credit_check
Sketch the conversation
Draw a flow in Draw.io the same way your team whiteboards it. Boxes, arrows, decision diamonds, and service-call labels all become structured flow primitives.
✣sketch -> bot
Sketch your bot.
Ship it in five minutes.
Drop a Draw.io diagram, PDF, Word file, or image. Convoship extracts intents, entities, decisions, and service calls into a runnable agent. Edit on the canvas, simulate live, then embed anywhere with one line of script.
The same workspace also offers AI Agents (mission, tools, guardrails) and Workflows to chain assistants, agents, and webhooks — for when a sketch is the start, not the whole story.
Audit log & MFA·Workspace roles·Encrypted secrets·Omni-channel deploy
how convoship works
From sketch to shipping in three moves.
A deterministic Draw.io parser gives Convoship the baseline. A single model pass cleans labels, infers slots, and lifts service calls. You review the result on a familiar visual canvas.
Claude Sonnet 4.6
anthropic · claude api
✓12 nodes
✓14 edges
✓4 labels cleaned
✓2 slots inferred
412ms · 1.2k tokens · $0.004
Convoship parses it
Run the extractor, clean labels, infer entity slots, and wrap HTTP nodes as real tool calls with auth, retries, and response mapping.
<script src=
"https://embed.convoship.org/v1.js">
</script>
ConvoshipQueue.push(['init', agent])
Edit and embed
Tune prompts and decisions on the canvas, simulate live in the corner panel, then paste a one-line script tag on any website.
try it yourself
Sketch to running agent, in three clicks.
No signup, no sandbox to configure. Walk the exact path your team will: drop a diagram, watch Convoship parse it, then talk to the agent it produced — runtime trace and all.
loan-application.drawio
apply_loanamount?tenure?credit_checkyour whiteboard
Deterministic parser first, then one Claude pass to clean it up.
product tour
One workspace for the whole agent lifecycle.
Build, simulate, deploy, and measure in one workspace. No tab-juggling, no second tool. The same canvas that holds your sketch is where you review production analytics. Need LLM reasoning or multi-bot orchestration? AI Agents and Workflows live here too — without replacing the sketch-first path.
Visual builder
React Flow canvas with library and inspector
Live simulator
Send messages, inspect context, and replay history
Channels
Embed widget, voice, WhatsApp, and webhook
Analytics
Sessions, completion, fallback, and funnel
AI Agents
Agentic runtime with mission, tasks, tools, persona, and guardrails
Workflows
Chain triggers, assistants, agents, decisions, and webhooks
app.convoship.org/agents/loan-app/builder
Intent · Entry
apply_loan
12 phrases · 2 params
Entity
Capture amount
@number -> loan_amount
Tool
credit_check
POST /credit/check
Script
Compose reply
templates {{rate}}
built for builders
The product details, in one screen.
Everything in Convoship is built around the same canonical conversation-flow schema. Import, edit, simulate, deploy, and measure all read the same JSON. AI Agents and workflow chains sit alongside scripted assistants in one workspace.
Multi-source import
Draw.io, PDF, Word, and image uploads. One shared flow schema with source-specific parsers.
Visual flow editor
React Flow canvas with library and inspector. Intents are first-class. Subflows compose.
Sandboxed code nodes
Python with session-scoped variables. No more glue scripts living elsewhere.
Real tool integration
HTTP nodes with auth, templated body, JSONPath output mapping, retries, and encrypted secrets.
Entity-aware slot filling
System types for numbers, dates, emails, and durations. Custom list entities with synonyms.
Embed anywhere
Small IIFE bundle, one script tag, queue snippet, tunable theme, lifecycle hooks, public API.
Built-in analytics
Conversation counts, session health, latency, fallback rates, completion, and audit history.
Live simulator + debug
Open any conversation history in the simulator and inspect the full runtime context in a popup.
Production foundations
Workspace auth, MFA, audit log, secrets vault, versioned drafts, and usage metering.
AI Agents
LLM playground with tool calls, daily spend caps, evals, and knowledge sources when flows are not enough.
Workflow chains
Orchestrate assistants, agentic agents, webhooks, and branches — e.g. qualify with an agent, hand off to a scripted intake flow.
Median extraction latency
~500ms
Claude Sonnet · Anthropic
Cost per Draw.io import
$0.004
~1.2k tokens
Node F1 on eval gold
>= 0.99
CI gate · 30 gold flows
Embed SDK bundle size
~15 kB
IIFE · gzip
under the hood
One schema. Every surface reads it.
The importer, runtime engine, embed SDK, and eval harness all share a single canonical conversation-flow schema. Export it, version it, and migrate it across workspaces.
loan-application.drawioXML
<mxGraphModel>
<root>
<mxCell id="n1" type="intent" value="apply_loan"/>
<mxCell id="n2" type="decision" value="amount > 50000?"/>
<mxCell id="n3" type="api" value="credit_check"/>
</root>
</mxGraphModel>agents/loan-app/v8.jsonJSON
{
"id": "agt_loan-app",
"version": 8,
"intents": [{
"name": "apply_loan",
"phrases": 12,
"parameters": ["loan_amount"]
}]
}~/acme-bank
Studio · Deploy · loan-application
-> import · drawio · claude-sonnet-4-6 (anthropic) · 412ms · $0.004
-> validate · 12 nodes · 14 edges · 0 errors
-> publish · version 8 · simulate ok
-> token · cdp_8c2...ke7 · embed snippet ready
✓ live on acme.bank · 42 sessions today
how it stays correct
Deterministic where it matters, LLM where it helps.
Convoship doesn't ask the LLM to invent your conversation. A deterministic parser turns the diagram into structured nodes; a single Claude pass cleans labels, infers slots, and lifts service calls. A validator + auto-repair pass catches the trivial stuff before any LLM round-trip — so the only thing the model is asked to do is the part where models are actually good.
- 1
Deterministic Draw.io / PDF / Word / image parser
Pure code, no LLM. Reads the source into a typed AST of nodes, edges, and labels.
- 2
Validator + auto-repair
Rule-based pass fixes missing entries, dangling edges, and duplicate ids before validation. Trivial issues never consume a model call.
- 3
Single Claude pass (claude-sonnet-4-6)
Cleans labels, infers entity slots, lifts service calls. Cost is bounded per import; output is constrained to the canonical schema.
- 4
Validator again, then commit
Any leftover errors are surfaced as actionable issues on the canvas, not silently shipped. Drafts are versioned; publish is explicit.
- 5
Gold-eval regression suite
Thirty real-world gold flows gate the extractor in CI. Node F1 ≥ 0.99 is a release blocker, not a best-effort metric.
why we built convoship
We were tired of whiteboard flows that never shipped.
Every team we talked to had the same drawer full of conversation diagrams — loan flows, triage scripts, return policies — mapped out in Draw.io and then stuck there for months waiting on engineering. Convoship exists to close that gap: the diagram your team already drew becomes a running, editable agent in an afternoon, not a quarter.
Fayaz · Founder, Convoship
Deterministic first, LLM second
We don't ask a model to invent your conversation. A real parser does the structural work; the model only does the part models are actually good at. That's why imports are cheap, fast, and repeatable.
Honest metrics, in public
Every number on this page — latency, cost per import, node F1 — comes from our eval harness and CI gates, not a pitch deck. If a metric isn't measured, we don't claim it.
Enterprise controls on day one
Workspace isolation, MFA, audit logging, and encrypted secrets aren't a future enterprise tier. They're in every workspace, because we'd want them before trusting a vendor too.
chat widget
One line of script. Any website.
The embed SDK renders the same runtime your team tested in Studio, so what passes in the simulator is the behavior your customers see.
acme.bank
ACME BANK
Personal loans, on your terms.
Rates from 6.9% APR. Apply in five minutes. No hidden fees.
Apply now
Loan Application
How much would you like to borrow?
25 thousand
solutions
Built for the conversations your team already runs.
Convoship works wherever a team has already mapped the conversation on a whiteboard, in a runbook, or in a support macro. The same sketch-first path serves regulated industries and consumer-grade self-service.
Banking
Onboarding, lending, and account servicing
Capture KYC, qualify loan applications, and answer balance / transaction questions. Decision diamonds gate credit checks; service calls reach core banking APIs through HTTP nodes with encrypted secrets, retries, and JSONPath output mapping.
- KYC slot-filling
- Credit decisioning
- Audit log + MFA
Retail
Order status, returns, and product search
Pull live order data from your OMS, surface return windows, and route shoppers to PDPs that actually match what they described. Knowledge collections cover policies; intents cover transactional tasks.
- OMS integration
- RAG for policy lookup
- Web + WhatsApp deploy
Healthcare
Triage, scheduling, and benefits questions
Run pre-visit triage flows, book or reschedule appointments through EHR APIs, and explain benefits without exposing PHI to the LLM. Guardrails restrict outputs to approved language; secrets stay vaulted.
- PHI-safe prompts
- EHR API tool calls
- Custom guardrails
Hospitality
Reservations, concierge, and upsell
Take bookings, recommend room upgrades, and answer property questions across web, voice, and WhatsApp from the same agent. Localized copy per intent; analytics expose conversion by funnel step.
- Voice + chat parity
- Localized intents
- Funnel analytics
Customer support
Deflection and live-agent handoff
Resolve top-N FAQs with knowledge collections, run intent-driven self-service for the next tier, then hand off to a human with full session context. Workflow chains coordinate AI Agent reasoning with scripted intake.
- Knowledge (RAG)
- Workflow chain handoff
- Live transcript export
IT helpdesk
Ticket triage and self-serve fixes
Categorize incoming tickets, walk users through known runbook fixes, and open a ticket in your ITSM with the full diagnosis when the bot can't resolve it. Tools call Jira / ServiceNow / Zendesk under workspace secrets.
- Runbook flows
- ITSM tool calls
- Workspace audit log
security & compliance
Production foundations on day one.
Convoship was built for teams whose security review never gets skipped. Every workspace ships with the controls your auditors expect — no add-on tier, no add-on price.
Identity & access
Workspace roles (owner, admin, developer, editor, viewer), MFA enforcement, refresh-token rotation, configurable session lockout, and a workspace-wide revoke-all-sessions action.
Encrypted secrets
Workspace secrets vault with envelope encryption (Fernet today, KMS-ready). Tool nodes reference secrets by name — credentials never enter prompts, exports, or logs.
Row-level security
Postgres RLS enforces workspace isolation on every query. The app role cannot bypass RLS; cross-workspace data exposure is structurally impossible, not just policy-enforced.
Audit trail
Every mutation — agent edits, deployments, secret reads, member role changes — lands in an immutable audit log. Filter by actor, action, target, and time range — the audit evidence your security review will ask for.
Usage metering & budgets
Daily LLM spend caps per AI Agent, per-workspace conversation counts, and Prometheus metrics for runtime sessions, turns, and tool calls. No surprise bills, no silent failures.
Sandboxed code execution
Python nodes run with a strict per-node timeout and session-scoped variables only. No filesystem, no outbound network unless explicitly proxied through a tool node.
founding design partners
We're taking on a small group of founding customers.
Convoship is new, and we're deliberate about our first enterprise deployments. Instead of a wall of logos we haven't earned yet, we're inviting a limited cohort of design partners to build with us directly — and lock in founding terms.
Limited cohort. We'd rather serve a few teams exceptionally than many poorly.
- A direct line to the founder and engineering — not a ticket queue
- Hands-on onboarding: we help import and ship your first agents with you
- Real influence on the roadmap — your use cases shape what we build next
- Founding pricing, locked for the life of the partnership
- A named security and architecture review before you commit
Built for enterprise teams.
Convoship is sold on annual agreements with the security, scale, and support your organization expects. Every workspace ships on the Enterprise plan — no agent count caps on imports or sessions.
Enterprise
Customannual agreement
For regulated industries and high-volume conversational programs.
- Unlimited agents, imports, and runtime sessions
- Workspace roles, MFA, and audit log
- Encrypted workspace secrets vault
- WhatsApp, voice, webhooks, and web embed
- AI Agents, workflows, and knowledge (RAG)
- Dedicated onboarding and support
enterprise faq
The questions your security and procurement teams will ask.
Straight answers, including the ones most vendors dodge. If something here matters to your evaluation, raise it on the call — we'd rather over-explain than oversell.
Where does our data go, and which AI provider do you use?
Conversation extraction and runtime reasoning use Anthropic's Claude API (Sonnet for extraction, Haiku for latency-sensitive paths). Your agent configuration and conversation data live in your workspace, isolated at the database level by Postgres row-level security. We don't sell data, and we don't use your data to train models.
Are you SOC 2, ISO 27001, or HIPAA certified?
Not yet — we're a new platform and we won't claim a certification we don't hold. What we can show you today is the actual control set: row-level tenant isolation, MFA, immutable audit logs, envelope-encrypted secrets, and sandboxed code execution. We're glad to walk your security team through our architecture and our roadmap toward formal attestation. Full detail is on our Trust page.
Can we self-host or deploy in our own VPC?
Yes. Convoship runs as a containerized stack (FastAPI, Postgres, Redis) and supports self-hosted and VPC deployments, including self-hosted vision endpoints for on-prem import. Cloud, self-hosted, and hybrid are all on the table for design partners.
How do you handle secrets and third-party credentials?
Tool integrations reference secrets by name from a workspace vault with envelope encryption (KMS-ready). Credentials never enter prompts, exports, or logs, and the application database role cannot bypass workspace isolation.
What about PHI and other regulated data?
Guardrails can restrict agent outputs to approved language, and you can design flows that keep sensitive values out of LLM prompts — capture them and pass them through tool calls instead. We review your specific compliance requirements during onboarding rather than wave them away.
What does pricing look like?
Convoship is sold as an annual enterprise agreement with no caps on agents, imports, or runtime sessions. Founding design partners lock in founding pricing. Talk to us about your volume and deployment model.
What support and SLA do we get?
Design partners get a direct line to the founder and engineering, hands-on onboarding, and a response commitment we'll put in writing in your agreement. Formal SLA tiers will follow as we scale.
✣ sketch -> bot
Sketch your bot.
Ship it in five minutes.
See how your team can go from whiteboard sketch to production agent in a single working session.