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AI for SaaS scale-ups.

We build internal AI ops, GTM enablement, and support deflection while your dev team keeps shipping. We are the implementation layer between your product roadmap and your back-office.

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Zero engineering hours required Mutual NDA in 24h Cancellable monthly

What does AI implementation look like for a SaaS scale-up?

Three layers, all internal-facing. GTM enablement: outbound research, lead enrichment, account briefs, call summarisation. Support deflection: tier-one resolution via a RAG agent grounded in your docs and ticket history. Internal ops: onboarding, finance ops, people ops. We build and run all three. Your engineers stay on the roadmap.

AI consultant vs in-house AI hire vs pulling engineers off the roadmap

The three real options for a scaling SaaS. We are direct because the comparison matters.

DimensionAI consultant (THL)In-house AI hirePull engineers off roadmap
CostA$2,000 a month, with builds quoted per phaseA senior salary plus on-costsOpportunity cost of slipped roadmap
Speed to first deployWeek 1Month 4 to 6Whenever the sprint ends
Roadmap impactZeroZeroDirect: features slip
Ongoing riskWe rotate out cleanlySingle point of failureInternal AI becomes a side project nobody owns
Vendor lock-inArchitecture survives swapsOften vendor-shapedUsually tightly coupled

How a SaaS scale-up engagement runs

Discovery
Week 1

NDA signed. We map GTM, support and internal-ops workflows. Audit your stack (CRM, helpdesk, docs, comms) and find the three highest-leverage builds.

GTM layer
Weeks 2 to 3

Account research agent, lead enrichment into HubSpot or Attio, call-summarisation into CRM. SDRs and AEs walk into every meeting prepped.

Support layer
Weeks 3 to 5

RAG agent grounded in your docs and ticket history. Deployed inside Intercom or Zendesk. Handles tier-one, escalates the rest with full context.

Ops + fractional
Ongoing

Onboarding, finance ops, people ops automations. We stay on as fractional AI ops and update the playbook quarterly as models evolve.

What an engagement looks like

This example is hypothetical, based on the pattern of our SMB and growth-fund deployments. (No client identifying details: we treat scale-up engagements as confidential by default.)

EXAMPLE A Series B vertical SaaS: $14M ARR, 60 staff, 6 engineers, no AI lead
GTM layer
Account research agent
Pulls signals from LinkedIn, Crunchbase, Apollo and the customer's own product analytics. Drops a one-page account brief into HubSpot before every discovery call.
Call summarisation
Gong or Fireflies transcript → structured CRM notes, next-step suggestions, and a draft follow-up email. AE edits, never starts from blank.
Support layer
Grounded RAG agent
Trained on the help-centre and 18 months of resolved tickets. Sits inside Intercom. It handles tier-one questions (password resets, billing, integrations) and deflects ~35% of volume after week 4.
Escalation with context
When it can't answer, it hands off to a human with the full conversation, customer plan and tagged knowledge-base articles already attached.
Ops layer
Onboarding + finance + people ops
New-hire setup, invoice processing, contractor management, weekly metrics digest. It runs on n8n + Claude with HRIS and accounting integrations and needs zero engineering time.

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Common questions from SaaS founders and ops leads

What does AI implementation look like for a SaaS scale-up?

Three layers, all internal-facing. GTM enablement (outbound research, lead enrichment, account briefs, call summarisation). Support deflection (a RAG agent grounded in your docs and ticket history). Internal ops (onboarding, finance, people). We build and run all three without touching your product surface.

Will you build AI features into our product?

No. That is your engineering team's job, and we are explicit about that boundary. Product AI requires deep model context, eval pipelines, and ownership of customer outcomes. That is roadmap work, not consulting work. We handle the internal-facing AI so every revenue, support and ops dollar goes further while your engineers stay focused.

How is this different from hiring an internal AI lead?

An internal AI lead costs a senior salary plus on-costs and takes months to hire. We scope and quote each phase before it starts, then the fractional deployed engineer runs it at A$2,000 a month + GST.

Do you work with our existing stack or replace it?

We work inside your stack. HubSpot or Attio for CRM, Intercom or Zendesk for support, Linear or Jira for engineering, Notion or Confluence for docs. We add an orchestration layer (n8n, Claude or ChatGPT Teams, a vector store) on top of the tools your team already runs on.

What does this cost for a Series A or B company?

Custom builds are quoted per phase, and we scope and price each phase before it starts. The fractional deployed engineer is A$2,000 a month + GST, cancellable monthly. The architecture stays yours, so swapping a vendor is a config change.

We’ve measured 14 fintechs against the questions buyers ask AI engines when they shortlist software. AI named the average fintech in 15.7% of answers, and 4 of the 14 were never named.

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Adjacent reading

AI Ownership Readiness Check

Six questions, scored instantly in your browser. Use it with your leadership team before a kickoff call.

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State of AI Readiness: Australian SMB 2026

First-party survey of 54 Australian SMBs. The base rate for any scale-up benchmarking itself.

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