Description

A focused course, tailored for you

Productising Internal AI Tools: From Internal Build to Customer-Facing SLA Product

Name: Productising Internal AI Tools: From Internal Build to Customer-Facing SLA Product
Price: 199 USD
Availability: InStock

Take an internal AI tool from prototype to customer-facing product in 12 weeks. Pricing, SLA, governance, observability, billing, support.

Every SaaS firm has 5+ internal AI tools that solve real customer problems but live in internal-only mode. Productising them into customer-facing SLA products is the highest-leverage path to incremental ARR in 2026. Here's the 12-week build playbook.

$199 one-time

Tailored to your situation. Access within 24 hours. 30-day money-back.

Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.

Why this course

Every SaaS firm has internal AI tools that solve real customer problems: summarisation, classification, search, recommendation, agent-augmented workflows. Most live in internal-only mode. Customer interest is documented. Internal-build cost is sunk. The path to incremental ARR is productisation: turn the internal tool into a customer-facing SLA product.

Productisation is the highest-leverage path because: (1) the AI capability already exists, (2) customer demand is documented through internal-tool usage signal, (3) the engineering team understands the failure modes, (4) the customer-facing version reuses the inference infrastructure.

But productisation is more than 'turn on the API'. It requires: pricing model (per-call vs per-token vs per-seat vs per-outcome), SLA design (uptime, latency, accuracy, support response), governance (data residency, model versioning, drift management, customer-controlled redaction), observability (per-customer usage analytics, cost-per-customer, success-rate dashboards), billing (metered billing infrastructure, usage caps, overage handling), support (tier-1, tier-2, escalation, AI-specific runbooks), security and privacy (data isolation, BAA for healthcare, GDPR DPA), legal (terms of service AI clauses, IP indemnification, training-data assurances), and the GTM motion (positioning, pricing tier, sales enablement).

This course teaches the 12-week productisation build: pricing model design, SLA decomposition, governance framework, observability architecture, billing infrastructure, support model, security and privacy, legal templates, and GTM motion. Twelve modules, each ending with a deliverable artefact. Plus a hand-built implementation playbook for your specific internal-tool productisation.

What you walk away with

A documented pricing model with three options (per-call, per-token, per-seat or hybrid).
An SLA decomposition with uptime, latency, accuracy, and support tiers.
An AI governance framework with model versioning and drift management.
A per-customer observability architecture.
A metered-billing infrastructure design.
An AI-specific support runbook library.
A security and privacy framework (data isolation, BAA, GDPR).
A legal-terms template library.
A GTM motion with positioning and pricing tier.
A 12-week productisation plan.

The 12 modules

Module 1. Productisation diagnostic and target-customer fit

Diagnose your internal AI tool's productisation fit: customer-demand signal strength, technical-capability maturity, competitive-positioning, pricing-power assessment, and the build-vs-partner decision. Build the productisation prioritisation matrix across your AI tool portfolio. Three worked examples of internal-tool productisation paths at SaaS firms. Deliverable: productisation prioritisation matrix and target-tool selection.

Module 2. Pricing model design

Build the pricing model: per-call (transactional), per-token (LLM-aligned), per-seat (capability-as-feature), per-outcome (value-aligned), hybrid. Pricing-power analysis: willingness-to-pay model, cost-to-serve model, competitive benchmarking, packaging tiers (Starter, Pro, Enterprise), volume discounts, and the prepay-vs-postpay decision. Deliverable: pricing model document with three options and recommendation.

Module 3. SLA design and decomposition

Build the SLA: uptime (99.5% to 99.99% by tier), latency (p50, p95, p99 by use case), accuracy (use-case-specific quality metric), support response (24/7, 8x5, BH), credit model (uptime credit, latency credit). AI-specific SLA challenges: stochastic-output accuracy, drift management, deprecation windows. The SLA contract template. Deliverable: SLA design document and contract template.

Module 4. AI governance and model lifecycle

Build the AI governance framework: model-versioning (semantic versioning for models), deprecation policy, drift-detection and remediation, customer-facing changelog, model-evaluation pipeline (offline and online), and the model-rollback mechanism. The customer expectation that 'the AI doesn't change without notice' shapes governance. Three governance patterns at peer SaaS firms. Deliverable: AI governance framework.

Module 5. Per-customer observability architecture

Build the observability architecture: per-customer usage metrics, per-customer quality metrics (success rate, escalation rate, satisfaction), cost-per-customer (token cost, inference cost, infrastructure cost), customer-facing dashboard, and the internal-team dashboard. The observability that supports customer-success conversations and per-customer profitability. Deliverable: observability architecture.

Module 6. Metered billing infrastructure

Build the metered-billing infrastructure: usage-event capture, aggregation pipeline, billing-engine integration (Stripe, Chargebee, Metronome, Orb, Lago, Recurly, Zuora), invoice generation, payment reconciliation, usage caps and overage handling, refund handling, and dunning. The billing engine that handles per-customer per-token complexity. Deliverable: billing infrastructure design.

Module 7. Support model and AI-specific runbooks

Build the support model: tier-1 customer-success triage, tier-2 product-engineer escalation, tier-3 platform-engineer escalation. AI-specific support categories: model-output disputes, accuracy concerns, latency complaints, billing disputes, drift questions, prompt-engineering support. The AI-specific runbook library. Deliverable: support model document and runbook library. Three worked examples drawn from real implementation packages plus the conversation-script for the next sponsor meeting that lands the artefact for review.

Module 8. Security and privacy framework

Build the security and privacy framework: data isolation (per-customer logical or physical separation), data-residency options (US, EU, Apac), data-retention controls (customer-configurable), encryption (at rest, in transit, customer-managed keys), HIPAA BAA template, GDPR DPA template, SOC 2 control mapping, and the customer-audit pack. The customer-facing security documentation. Deliverable: security and privacy framework.

Module 9. Legal terms template library

Build the legal-terms template library: AI-specific terms of service (output ownership, prompt-input data usage, opt-out for training, customer-confidentiality), AI-specific indemnification (IP, hallucination, accuracy), training-data assurances (no customer data without consent, opt-out mechanism), and the data-processing terms. Three legal-template patterns from SaaS firms with productised AI. Deliverable: legal-terms template library.

Module 10. GTM motion and positioning

Build the GTM motion: positioning statement, ideal-customer-profile, competitive battlecards, sales enablement (demo script, ROI calculator, case studies), pricing tier mapping to ICP, customer-success-led growth model, and the marketing motion (content, events, partnerships). The GTM motion that aligns to the pricing tier and SLA design. Deliverable: GTM playbook.

Module 11. Launch readiness and ramp

Build the launch-readiness pack: limited-access beta design, customer-cohort selection, success-criteria definition, GTM launch sequence (private GA, public GA, broad GA), pricing-roll-out, customer-migration from internal to product, and the post-launch ramp (60-day, 90-day, 180-day milestones). Deliverable: launch-readiness pack. Three worked examples drawn from real implementation packages plus the conversation-script for the next sponsor meeting that lands the artefact for review.

Module 12. Your 12-week productisation plan

Week-by-week plan with weekly deliverables. Weeks 1-2: productisation diagnostic + pricing model. Weeks 3-4: SLA design + AI governance framework. Weeks 5-6: observability architecture + billing infrastructure design. Weeks 7-8: support model + security and privacy framework. Weeks 9-10: legal terms + GTM motion. Weeks 11-12: launch readiness + private beta launch. Deliverable: full productisation pack.

How this addresses your situation

Specific modules that map to what you said you are dealing with.

Modules 1 to 3 cover diagnostic, pricing, and SLA design.

Modules 4 to 6 cover AI governance, observability, and billing infrastructure.

Modules 7 to 9 cover support, security and privacy, and legal terms.

Modules 10 to 12 cover GTM, launch readiness, and the 12-week plan.

What you get with this course

The 12-module course delivered as text plus downloadable templates.
Templates for pricing model, SLA contract, AI governance framework, observability architecture, billing infrastructure, support runbooks, security and privacy framework, legal-terms library, GTM playbook, launch-readiness pack.
A hand-built implementation playbook generated for your specific internal-tool productisation.
Three worked examples of internal-tool productisations at SaaS firms.
Scripted talking points for the executive-team productisation pitch.

What you will have in hand by Day 1, Week 1, Month 1

Day 1: Productisation diagnostic completed.

Week 4: Pricing model + SLA design + governance framework approved.

Week 8: Observability + billing + support + security designed.

Week 12: Private beta launching with first customer cohort.

Before and after

Before

Your firm has 5+ internal AI tools that customers want. Internal-only mode. ARR opportunity is documented but unrealised. Productisation roadmap does not exist.

After

A documented productisation pack is in place. Pricing, SLA, governance, observability, billing, support, security, legal, GTM are all designed. Private beta is launching with first cohort.

What happens if you do not address this

Internal AI tools that solve customer problems and are not productised leave incremental ARR on the table. Competitors with similar internal tools that productise first capture the market share.

Who it is for

For product managers, product engineers, technical leads, and platform managers at SaaS firms productising internal AI tools.

Who this is NOT for. Pure research roles. Firms with no AI tools in scope. Firms with no SaaS customers.

How it arrives

Text-based course via LMS, plus downloadable templates and the hand-built implementation playbook.

Time investment. Roughly 16 hours of reading and 100 to 200 hours of team effort across the 12-week build.

Why $199 is the right number

External AI-productisation consultants charge $200K-$1M for engagements. Specialist product-led-growth firms (Reforge, Pendo) charge $100K-$500K. $199 buys the focused playbook plus the implementation document for your specific internal-tool productisation.

FAQ

Will this replace hiring a productisation consultant?

Partially. It teaches you the productisation playbook. You may still want specialist support for pricing-power analysis on novel AI capabilities.

What if our internal tool is built on a foundation model (OpenAI, Anthropic, Google)?

Module 4 covers foundation-model-vendor governance and the AI-vendor pass-through dynamics.

Does this cover usage-based forecasting and the deferred-revenue accounting?

Module 6 covers metered-billing forecasting; deeper accounting treatment warrants CFO engagement.

What about the build-vs-buy productisation question?

Module 1 covers build-vs-buy-vs-partner productisation paths.

What is in the implementation playbook for me specifically?

A productisation diagnostic tailored to your specific internal AI tool; pricing-model options with recommendation; a 12-week build plan with milestones.

30-day money-back guarantee. If after a week of working through the materials this is not what you needed, reply to the receipt email and a full refund is processed. No questions, no forms.

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.