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Offers

Paid, bounded steps from AI opportunity to operating capability.

Argonous packages advisory, automation, intelligent systems, and agent-based operating capability into clear commercial steps. The fit call decides whether a paid sprint or scoped pilot is worth doing.

Start with a Sprint Compare Offers

Commercial Path

  1. 1 Fit call confirms the workflow, owner, payment path, and useful first engagement.
  2. 2 Paid AI Opportunity Sprint or scoped pilot clears before meaningful preparation starts.
  3. 3 Build capacity is reserved only after signed scope and required payment gate.
  4. 4 Delivery ends with working artifacts, review evidence, operating notes, and a decision on managed evolution.

Service Architecture

The offers sit on four capabilities.

The package changes by entry point, but the operating model stays consistent: advisory clarity, controlled automation, useful systems, and agent-native delivery with human approval.

AI Advisory and Consulting

Decide where AI should apply, what should stay human-owned, and what first move is worth paying for.

Workflow Automation

Turn repetitive expert work into controlled systems with data structure, review states, exception paths, and operating notes.

Intelligent Systems

Build custom AI-assisted products and internal systems with QA, state, evaluation, release gates, and handover discipline.

Agent-Based Operating Capability

Use agents, context, memory, review loops, and reporting as business infrastructure rather than one-off prompt use.

Offer Ladder

Every offer answers the commercial questions upfront.

Each package states the buyer state, problem, output, Non-Scope, payment gate, artifact, and next action so the engagement does not drift into unpaid consulting or vague transformation work.

Entry

AI Opportunity Sprint

Discover the deployment-ready workflow: the work, risks, data, integrations, and adoption path worth paying for.

Start with a Sprint

Buyer State

SME leader, director, partner, owner, operator, or funded founder who knows AI matters but cannot yet identify the best deployment-ready workflow, risk, or build path.

Problem

AI interest is high, but the deployment target is vague. Without a paid diagnostic, the buyer risks tool-chasing, unpaid solution architecture, or a build scoped before integration complexity, data readiness, state, trust, and adoption constraints are understood.

Output

Opportunity Map, Workflow Map, Risk Register, Deployment-Readiness View, Ranked Options, Build Recommendation, and Next-Phase Brief.

Client Artifact

Argonous AI Opportunity Brief, including the recommended deployment stage and readiness constraints.

Non-Scope

No production build, live automation, data migration, procurement support, or team-wide AI training unless separately scoped.

Payment Gate

Paid upfront before preparation begins.

Entry or Phase 1

Workflow Automation Pilot

Deploy one bounded AI-assisted workflow with review states, exception paths, and operating notes.

Scope a Pilot

Buyer State

Team with one repeatable, painful workflow that has a clear owner, visible operational value, and enough data or source material to deploy a bounded AI-assisted workflow.

Problem

Work is trapped in inboxes, spreadsheets, manual review, brittle tools, or handoffs that slow the team down. The deployment challenge is turning that mess into a workflow with ownership, state, review, and exceptions.

Output

Working Bounded AI-Assisted Pilot, Human Review Loop, Exception Path, State Notes, Source-Provenance Notes, Operating Notes, and Pilot Acceptance Checklist.

Client Artifact

Workflow Pilot Pack: Process Map, Pilot Runbook, Acceptance Checklist, and Handover Notes.

Non-Scope

No full department rollout, unmanaged production dependency, broad data cleanup, ERP replacement, or unlimited integrations.

Payment Gate

Deposit or upfront phase payment clears before build capacity is reserved. Final handover, source transfer, or production transition is gated by cleared balance.

Premium Build

Intelligent System Build

Build durable AI capability with production discipline, evaluation, state, and handover.

Discuss a Build

Buyer State

SME leader, product lead, director, operator, owner, partner, or funded founder where AI capability is central to a product, service, or internal operating system.

Problem

The buyer needs more than automation. They need a durable intelligent system that reasons over context, manages state, handles edge cases, evaluates output, and can be operated safely.

Output

Phase-Gated Production System, Architecture Brief, Evaluation and QA Loop, State and Data Model, Deployment Notes, Operating Runbook, and Handover Pack.

Client Artifact

Intelligent System Build Pack: architecture brief, acceptance criteria, QA evidence, deployment notes, and runbook.

Non-Scope

No full build without a paid sprint or scoped pilot; no unsupported regulated claims, indefinite feature backlog, 24/7 managed support, or transfer of reusable Argonous methodology IP unless separately agreed.

Payment Gate

A paid sprint or scoped pilot must complete first. Build phase starts only after SOW signature and required payment clears. Source, production transfer, and final handover are gated by cleared balance.

Focused Wedge

Outreach Engine Pilot

Create agent-based operating capability for research, drafting, follow-up, and approval-led outreach.

Build a Cockpit

Buyer State

SME sales lead, managing director, creative operator, services firm, owner, partner, or funded founder who needs structured lead research and outreach without losing human approval.

Problem

Lead context, prioritisation, drafting, follow-up state, and tone memory are scattered. Manual outreach is slow, but unapproved external sending is too risky. The gap is agent-based operating capability, not another generic outreach tool.

Output

Tenant-Scoped Outreach Cockpit, Lead Import Pattern, Context Preservation, Draft Workflow, Approval Gate, Follow-Up State, and Cost Visibility.

Client Artifact

Outreach Engine Pilot Pack: lead artifact manifest, approval workflow, draft protocol, tenant notes, and operating checklist.

Non-Scope

No external sending without human approval, no scraping without lawful basis, no LinkedIn automation, no CRM replacement, no multi-user SaaS product, and no cold email campaign management unless separately contracted.

Payment Gate

Pilot fee paid upfront or deposit clears before setup. Private lead data enters the system only after SOW, data handling, approval rules, and direct-cost treatment are agreed.

Continuity

Managed Evolution Retainer

Keep deployed systems improving, monitored, and aligned as workflows and trust boundaries change.

Evolve the System

Buyer State

Client with a live or recently delivered AI workflow, automation, outreach cockpit, or intelligent system.

Problem

Systems decay without monitoring, review, updates, and disciplined improvement. The first build creates capability, but compounding value needs continuity.

Output

Monthly Review Cadence, Backlog Triage, Improvement Releases, Monitoring Review, Documentation Updates, and Strategic Guidance.

Client Artifact

Monthly Evolution Ledger: decisions, changes shipped, risks, usage notes, and next-month backlog.

Non-Scope

No new major build stream, 24/7 support, uptime commitment beyond contract, unlimited feature requests, or unfunded emergency work.

Payment Gate

Monthly in advance. First month paid before availability is reserved. Work pauses automatically if the monthly invoice is overdue.

Fit Logic

Choose the smallest paid step that can test the pattern.

Argonous does not publish exact public pricing until approved. The public commitment is the sequence: fit call, paid entry offer, signed scope, payment gate, build, review, and handover.

Start with the Sprint

Use the AI Opportunity Sprint when the business knows AI matters but the workflow, risk, or build path is still unclear.

Move Straight to Pilot Only When Owned

A pilot is appropriate when one painful workflow already has a clear owner, real inputs, and visible operational value.

Build After Proof

Production build work follows an evidenced workflow pattern, not a generic desire to add AI.

Retain Only What Needs Evolution

Managed Evolution is for shipped systems that need monitoring, improvement, and documentation discipline.

Delivery Sequence

  1. 1 Ground in source material.
  2. 2 Map workflow and judgment points.
  3. 3 Decide what AI can prepare.
  4. 4 Build the controlled system.
  5. 5 Review, test, and label uncertainty.
  6. 6 Handover with operating notes.
  7. 7 Evolve from real use.

Trust Boundaries

  • Human approval: humans approve pricing, publishing, relationship actions, sustainability claims, commercial commitments, and release decisions.
  • Proof discipline: no invented metrics, no unapproved names, no logos, screenshots, quotes, or client claims without recorded permission.
  • Data reality: we map provenance, sensitivity, uncertainty, ownership, and handoffs before building around them.
  • Operator-led delivery: we advise, build, test, document, and hand over systems a real team can operate.

Bring one workflow or strategic AI question.

The fit call decides whether there is enough owner clarity, payment path, and risk boundary for a paid sprint or scoped pilot.

Request a Fit Call