Plain-language cross-app agent operations portal

For

Operations leads and small teams running repetitive work across several connected business apps

Solves

Repetitive tasks are spread across disconnected apps, and existing automation tools each cover only part of the job, so teams rent several subscriptions and still coordinate by hand.

Delivers

Reviewed agent workflows with audit trails

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$13,000 for the MVP, $44,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce manual coordination across connected apps while keeping every automated action reviewable.

1. Create agents from plain-language task descriptions.
2. Connect third-party apps and authorize actions.
3. Run agents automatically in the background.
4. Schedule runs and trigger them on app events.
5. Support loops, conditionals and error handling.
6. Show a visual canvas of workflow steps before and after execution.
7. Offer pre-built templates for common scenarios.
8. Save and reuse workflows as playbooks.
9. Produce multiple output types from one workflow.
10. Start agents at limited trust and promote them manually.
11. Encrypt credentials and purge secrets from agent memory.
12. Accept a user-supplied LLM API key.
13. Keep audit trails for every AI action.
14. Detect repetitive tasks from observed team behavior.
15. Let users choose or override the AI model.
16. Generate draft standard operating procedures alongside workflows.

Everything these tools do, in one app

• Plain-language agent creation Users describe desired tasks or workflows in natural language to create AI agents without coding or complex setup.Found in DryMerge, Sidekick, Everyday and 7 more
• Multi-app integrations Connects to various third-party applications to perform actions across different services.Found in DryMerge, Sidekick, Everyday and 5 more
• Autonomous execution Agents run tasks automatically in the background without constant human intervention.Found in DryMerge, Zapier Agents, Tasklet and 1 more
• Scheduling and triggers Agents can run on a schedule or be triggered by events across connected applications.Found in DryMerge, Tasklet, maia.is your AI that gets things done and 1 more
• Complex logic support Handles advanced workflow logic such as loops, conditionals, and error handling without manual configuration.Found in Sidekick, Zapier Agents, Tasklet
• Visual workflow representation Provides a visual canvas or plan showing the steps of the workflow for review before or after execution.Found in Sidekick, maia.is your AI that gets things done
• Pre-built templates Offers ready-made templates to help users get started quickly with common automation scenarios.Found in Zapier Agents, Tasklet
• Reusable playbooks Allows users to save and reuse workflows as playbooks for recurring tasks.Found in Leapility
• Multi-output generation One workflow can produce multiple types of outputs such as text, scripts, or simple web content.Found in Leapility
• Trust levels Agents start with limited permissions and can be manually promoted to higher trust levels as users become comfortable.Found in BetterClaw
• Secrets auto-purge API keys and credentials are encrypted and automatically removed from agent memory after a short time.Found in BetterClaw
• Bring-your-own-key Users can supply their own LLM API key to power the agents.Found in BetterClaw
• Compliance and audit trails Provides SOC 2 Type II certification and full audit trails for every AI action.Found in Hapax
• Workflow monitoring Connects to existing tools to detect repetitive or automatable tasks from observed team behavior.Found in Hapax
• AI model selection Allows users to choose or override the underlying AI model to suit task requirements.Found in DryMerge
• SOP generation Automatically generates draft standard operating procedures alongside workflows.Found in Colleague Ninja

What goes in, what comes out

The workflow

1. In
   Start with

   Plain-language instructions, connected app permissions and observed team behavior

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect plain-language instructions

4. 3

   Connected app permissions and observed team behavior

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Reviewed agent workflows with audit trails

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed app set and permission model; final approval of consequential actions remains human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Agent builder and instructions, Workflow canvas and run monitor, Approvals and audit log. Use a list of agents and playbooks, a central canvas showing steps and triggers, and a right-hand panel for permissions, trust level and run history. Let users compare a draft plan with an executed run. Display draft, awaiting approval, running, completed and failed states. Provide a client preview link with comments anchored to the relevant step. Make the task-specific outcome reviewed agent workflows with audit trails visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, agent versions, connected app credentials, trust levels, approval states, usage allowances, run limits, export history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Customer-owned app accounts, authorized team activity data and permitted research sources. Cloud storage, app connectors and export destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: create agents from plain-language task descriptions; connect third-party apps and authorize actions. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will operations leads and small teams running repetitive work across several connected business apps use it to solve "repetitive tasks are spread across disconnected apps, and existing automation tools each cover only part of the job, so teams rent several subscriptions and still coordinate by hand"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Completed task runs per operations hour and correction rate after agent action.
4. Measure, then decide. Track completed task runs per operations hour and correction rate after agent action; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One fixed app set and permission model; final approval of consequential actions remains human. Implement one approved input format, a bounded representative case set and the first two task modules: create agents from plain-language task descriptions; connect third-party apps and authorize actions. Support the third module with operator review: run agents automatically in the background. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed agent workflows with audit trails. Retain the explicit scope boundary: One fixed app set and permission model; final approval of consequential actions remains human.

What the build depends on. Agent builder, app connector library, run scheduler, approval queue and tested export formats. High-fidelity production requires specialist operations QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed app set and permission model; final approval of consequential actions remains human.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined agent package. Offer a monthly production allowance after repeat demand. Quote complex multi-app or high-volume automation separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed agent workflows with audit trails. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce manual coordination across connected apps while keeping every automated action reviewable. Demonstrate a concrete reviewed agent workflows with audit trails using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Operations leads and small teams running repetitive work across several connected business apps professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed agent workflows with audit trails from a small authorized input set, with a transparent calculation of completed task runs per operations hour and correction rate after agent action and no promised savings.

The first 30 days

1. Week 1: interview five operations leads and small teams running repetitive work across several connected business apps and inspect a recent example of repetitive tasks spread across disconnected apps and existing automation tools each covering only part of the job.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure completed task runs per operations hour and correction rate after agent action, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Completed task runs per operations hour and correction rate after agent action. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Completed task runs per operations hour and correction rate after agent action; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed agent workflows with audit trails. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved agent configurations, connected app permissions and review examples, together with reliable delivery for a narrow operations niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for operations leads and small teams running repetitive work across several connected business apps. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

DryMerge, Sidekick, Everyday, Zapier Agents, Colleague Ninja, Tasklet, Leapility, maia.is your AI that gets things done, BetterClaw and Hapax. Compare this product with the buyer's present method on completed task runs per operations hour and correction rate after agent action. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, app integration maintenance, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed agent workflows with audit trails. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, permission scope and audit accuracy. Named owners approve consequential actions and external integrations. One fixed app set and permission model; final approval of consequential actions remains human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.