Offer ladder

Packages for moving from expert workflow to working system

Argonous does not sell generic AI programmes. We package the work into bounded steps: find the right workflow, prove it with a pilot, build the system, then keep it improving.

Start with the sprint Compare packages

Bounded before broad

We start with one useful workflow, clear owners, and a measurable definition of success.

Governance by design

Sensitive data, review points, and failure modes are designed into the workflow from the start.

Human judgement stays explicit

AI handles the repeatable load while humans own exceptions, approvals, and business context.

Working systems over decks

The goal is useful software, a clearer operating rhythm, and fewer manual bottlenecks.

The sellable packages

Each package has a different job. The sequence keeps the work grounded so a website form, sales call, or referral can turn into a clear next step without inventing a bespoke engagement every time.

Start here

AI Opportunity Sprint

Find the first workflow worth building around.

For founders and operators who can feel the opportunity but do not yet have a clean build target. We map the expert workflow, identify the highest-value bottleneck, and turn the opportunity into a practical first sprint.

Best for: Teams choosing where AI should enter the business first.

Shape: Focused diagnostic and implementation brief

Scope the first sprint

What you get

Workflow map with the decision points, handoffs, data sources, and failure modes made explicit
Ranked opportunity list with one recommended first sprint
Build, buy, or defer recommendation for each major opportunity
Pilot brief with scope, inputs, risks, success criteria, and owner requirements

Good first targets

Founder wants to move quickly but avoid a vague AI initiative
Operations team has several manual bottlenecks and needs a first target
Leadership needs a buildable plan before committing to a pilot

Prove it

Workflow Automation Pilot

Turn one repeated workflow into a measured pilot.

For teams with a clear workflow that is repeated often enough to matter. We build a contained pilot that handles real work, keeps humans in the review path, and makes the business case visible before a larger build.

Best for: Manual triage, research, document handling, intake, routing, or repetitive operational decisions.

Shape: Contained pilot with real workflow data and clear success criteria

Pilot one workflow

What you get

Pilot workflow with the core integrations or handoff points connected
Human review lane for judgement calls and exception handling
Baseline and pilot measurements for time, quality, throughput, or error reduction
Pilot report with what worked, what failed, and whether to scale

Good first targets

Inbox or form intake triage
Document extraction and routing
Internal research or preparation workflows
Repeated QA, review, or reconciliation tasks

Scale what works

Intelligent System Build

Build the production system after the pattern is proven.

For workflows that have moved beyond prototype logic. We turn a validated pilot or mature process into a durable AI-assisted system with interfaces, integrations, observability, and operating ownership.

Best for: Teams ready to move from pilot to production-grade internal software.

Shape: Production build with operating model and handover

Plan a system build

What you get

Production workflow architecture with the right model, data, and integration boundaries
Application, dashboards, or internal tool surface for operators
Evaluation, audit, and monitoring hooks appropriate to the workflow risk
Handover docs and operating rhythm so the system can be managed after launch

Good first targets

Internal operating system for a high-value workflow
AI-assisted review, preparation, or decision support tool
Workflow platform connecting multiple existing systems
Founder-led product or internal tool build with AI at the core

Create pipeline

Outreach Engine Pilot

Build a repeatable AI-assisted engine for research, prep, and follow-up.

For founder-led sales where better preparation and cleaner follow-up matter more than volume. We build a controlled engine for ICP research, account notes, message drafts, and task flow while keeping final judgement human-owned.

Best for: Founder sales, specialist B2B services, and relationship-led outbound.

Shape: Research and outreach workflow pilot with human approval gates

Pilot an outreach engine

What you get

ICP and account research workflow that produces usable prep, not generic summaries
Message draft and follow-up flow aligned to your actual positioning
Operating dashboard or task rhythm for review, outreach, and next action
Quality controls that prevent spammy volume from replacing good judgement

Good first targets

Founder-led outbound prep
Referral and warm-intro follow-up
Account research before calls
Sales ops for a small expert team

Keep improving

Managed Evolution Retainer

Keep shipped systems useful as the business changes.

For systems that need active ownership after launch. We monitor performance, tune workflows, manage model and integration drift, and keep a practical improvement backlog tied to business use.

Best for: Teams that want a shipped system to keep improving without adding a full internal AI function.

Shape: Ongoing technical and operating support for live AI-assisted systems

Discuss managed evolution

What you get

Regular review of system behaviour, edge cases, costs, and operator feedback
Model, prompt, workflow, and integration tuning as conditions change
Improvement backlog connected to business priorities
Operational reporting that keeps owners clear on value, risk, and next steps

Good first targets

Post-pilot optimisation
Production system monitoring and tuning
Workflow expansion across a team
Ongoing evaluation and governance support

How the sequence works

The ladder is designed to make the next move obvious. Start small enough to learn quickly, then expand only when the workflow has proved it deserves a system.

Map the work

Clarify the workflow, the human judgement inside it, the data it touches, and the business cost of leaving it manual.

Choose the first package

Start with the Opportunity Sprint unless the pilot target is already obvious and well-owned.

Pilot against real inputs

Use the actual workflow, not a demo-only sample, so quality and edge cases are visible early.

Build or stop deliberately

If the pilot proves the pattern, move to a system build. If not, capture the lesson and avoid sunk-cost drift.

Common questions

How to choose the right first package.

Do we always start with the AI Opportunity Sprint?

Usually, yes. If the workflow, owner, inputs, and success criteria are already clear, we can move straight into a pilot. If not, the sprint prevents vague scope and wasted build time.

What makes a good pilot candidate?

A good candidate is repeated often, has identifiable inputs and outputs, carries enough business value to matter, and has a human owner who can judge quality during the pilot.

When does a pilot become a system build?

When the pilot proves the workflow pattern and the business wants it to become part of normal operations. That is when we add stronger interfaces, observability, integration depth, and operating ownership.

Know the workflow that should go first?

Send the workflow, the current pain, and what happens if it keeps running manually. We will recommend the right first package.

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