No-code and low-code AI workflow platform
Pronto helps teams design, test, and run AI-powered business processes with approved model providers, tools, knowledge, files, approvals, and audit trails in one platform.
A request, case, document, project, ticket, or event enters the workflow.
Models use approved tools, files, knowledge, and policies instead of uncontrolled prompts.
The run produces an answer, decision, file, task, or record with traceability and review.
The real adoption gap
Serious teams need more than prompts and demos. They need a safe way to connect AI to processes, systems, people, and governance.
Every team should not have to assemble model access, servers, storage, search, files, tools, approvals, and audit from scratch.
Operations, education, innovation, and public-sector teams need a practical builder surface while IT keeps control of credentials, data, and deployment.
If nobody can see what ran, which evidence was used, and who approved sensitive actions, AI work remains difficult to scale.
What you get
Pronto gives organizations one place to build, test, deploy, and operate AI workflows without hiding the operational controls.
Turn repeatable document, intake, triage, review, and support work into inspectable workflows.
Connect approved providers and credentials instead of spreading raw keys across teams.
Give employees, students, or innovation teams a safe environment to learn AI engineering through real workflows.
Review runs, tool use, evidence, approvals, outputs, errors, and usage from product surfaces designed for governance.
What teams can build
Use Pronto for domain-specific processes where AI must read, reason, act, ask for approval, and leave an auditable trail.
How it works
Builders work visually, operators keep control, and runtime execution remains self-contained.
Map the business process, agents, decision points, inputs, outputs, and user-facing experience.
Attach approved documents, databases, APIs, search, email, and other systems through explicit configuration.
Use approved commercial, local, national, university-hosted, or self-hosted inference routes where they are configured.
Control who can run, review, approve, access files, use tools, and see results.
Run the workflow before production and inspect inputs, steps, evidence, tool use, failures, and outputs.
Run the workflow in a governed runtime and expose an app, API, form, or operator view when needed.
Platform model
Pronto is not a single chat screen. It separates authoring, execution, and presentation so organizations can build quickly without losing operational control.
Design workflows, connect tools and Resources, configure model providers, set policies, test runs, and publish deployments from one governed workspace.
Builders get speed without bypassing approval, validation, or deployment discipline.
Execute workflows in a self-contained runtime that owns tool calls, uploads, approvals, runtime ledgers, outcomes, audit, and policy checks.
Operations get durable runtime truth instead of fragile browser or prompt-side automation.
Expose the workflow as a focused end-user app, operator queue, admin surface, or conversation interface without making the app own execution truth.
Users get a clean product surface while platform teams keep execution boundaries intact.
Platform capabilities
Pronto keeps powerful AI workflow features understandable for non-technical builders while preserving technical control for platform teams.
Design multi-step AI processes without writing custom orchestration code for every use case.
Use approved documents, policies, templates, and uploaded files with access control and safe previews.
Connect APIs, databases, search, email, and business systems through explicit tool permissions.
Give teams controlled access to approved model providers with scoped credentials and visibility.
Require review before sensitive actions such as sending, publishing, deleting, or writing records.
See what ran, which tools were used, what evidence was cited, and what output was produced.
Start quickly with SaaS, or use dedicated, hybrid, private-cloud, or on-prem options when policy requires it.
Major modules
Pronto brings workflow design, runtime execution, Resources, managed inference, channels, approvals, and audit into one operating model.
No-code and low-code workflow authoring, testing, validation, versioning, and deployment.
Governed access to approved commercial, local, university-hosted, national, or self-hosted model endpoints.
Approved documents, files, policies, templates, search projections, previews, and runtime-safe evidence handling.
Explicitly configured APIs, databases, search, HTTP, MCP servers, and business-system actions with tool-level permissions.
Approval, pause/resume, resolver routing, and operator action for sensitive workflow steps.
Role-aware end-user, operator, and admin surfaces for deployed workflows and work items.
Run history, tool activity, evidence, outcomes, safe transparency, and operational diagnostics.
Tenant isolation, roles, quotas, entitlements, credentials, and deployment controls.
Managed inference
Pronto can connect workflows to approved model providers, local inference services, commercial APIs, university-hosted models, or self-hosted compatible endpoints through explicit configuration.
Quota, budget, usage visibility, and revocation depend on what the selected provider or gateway can enforce. Pronto should label those capabilities honestly instead of promising unlimited or automatic enforcement.
Platform admins can register permitted inference services and expose safe connection values to builders.
Teams use governed credential references instead of copying provider keys into prompts, files, or user interfaces.
Workflows can be configured for commercial APIs, local endpoints, national infrastructure, or self-hosted services where available.
Model names, supported surfaces, expiry, limits, and budget behavior must be explicit so builders know what can be relied on.
Deployment and channels
A workflow can be exposed through Pronto App, API/webhook entry points, scheduled or event-driven triggers, and configured channel connectors. Channel availability depends on the deployed configuration and connector readiness.
Launch a branded end-user or operator application for chats, cases, approvals, files, outcomes, and work queues.
Start workflows from existing systems, forms, CRMs, service desks, portals, and event pipelines through controlled runtime endpoints.
Connect Telegram bot workflows where a conversational channel is the right user experience.
Use WhatsApp Cloud or Twilio-style connector paths where the organization has the required business account and credentials.
Run periodic checks, intake sweeps, reminders, and background processes without making a user keep a browser open.
Route approvals, review tasks, escalations, and outcomes to the right user or operator surface.
Channels are explicit deployment surfaces, not hidden model tools. They require configured connectors, credentials, policies, and readiness checks.
Learning and experimentation
Pronto supports universities, accelerators, internal AI academies, enterprise innovation teams, and government skills programs that need practical AI building, not only course completion.
Build a simple workflow that accepts input, calls an approved model, and returns a structured result.
Connect a model to an approved API, database, search index, or file workflow under explicit permissions.
Create a document workflow with citations, bounded evidence, and a human review step.
Turn student, startup, or internal innovation ideas into working workflow prototypes with traceable runs.
Governance
The goal is not to restrict experimentation. The goal is to make it safe, measurable, and operationally credible.
Keep organizations, teams, projects, and environments separated by clear access boundaries.
Control who can build, deploy, approve, administer, view outputs, or access sensitive surfaces.
Track and limit usage where enforcement is supported, and keep budget behavior visible.
AI workflows can only use tools and systems that were deliberately configured and linked.
Users and agents should not see provider keys, storage keys, internal hosts, or infrastructure identifiers.
Runtime records support debugging, review, evidence, approval history, and operational accountability.
Deployment options
Different organizations need different operating models. Pronto can be positioned for SaaS pilots, dedicated environments, hybrid setups, or controlled infrastructure depending on policy and scale.
Who it is for
The same primitives can support business operations, education, public-sector programs, startup acceleration, and enterprise platform teams.
Automate internal processes with approvals, evidence, audit, and controlled integration access.
Give learners and mentors a hands-on AI workbench without each institution running its own stack.
Support governed experimentation, digital services, transparent workflows, and controlled provider access.
Turn AI product ideas into workflow prototypes, evaluation dossiers, and demo-ready outputs faster.
Control model access, credentials, quotas, integrations, data boundaries, and deployment choices.
Designed for controlled adoption
When customer proof is still pilot-led, the product should earn trust through architecture, boundaries, and transparent operating claims.
Providers supply inference; Pronto does not depend on hidden provider-hosted tools.
Workflows use explicit tools, configured data sources, and declared credentials.
Runtime execution, uploads, approvals, and outcomes are owned by the runtime layer.
Production deployments fail fast when required infrastructure is missing.
Agents see handles, metadata, and bounded evidence, not infrastructure identifiers.
Deployment options can evolve from SaaS pilot to dedicated or private infrastructure.
Common questions
Clear answers before a technical evaluation.
No. Chat can be one presentation surface, but the product is a workflow platform for connecting models, tools, files, knowledge, approvals, and audit around repeatable business processes.
No. Workflows can be configured for approved providers and compatible endpoints. Any specific provider route must be configured, credentialed, and honestly labeled for its capabilities.
Yes, the builder is no-code and low-code. Technical teams still control infrastructure, credentials, deployment policy, and integration boundaries.
Yes. Pronto can be used as a hands-on AI workflow lab for universities, internal academies, accelerators, and government skills programs.
On-prem or private-cloud deployment is a possible operating model, but it is not trivial. It requires clear responsibility for databases, storage, search, inference, identity, backups, monitoring, support, and upgrades.
The fastest proof is not a generic AI demo. Pick one real process, connect the required systems safely, test it with traceability, and decide whether it should move to production.
We can discuss SaaS, dedicated, hybrid, or on-prem paths depending on your governance requirements.