Capabilities
What we build, end to end.
AI Workflow Audit
Mapping every manual process and identifying automation candidates with ROI estimates.
Agent Framework Deployment
Configuring reusable AI agents for your agency's specific workflows: content generation, client reporting, data processing, research.
SOPs for AI-Augmented Delivery
Written playbooks for how each role should use AI tools, what quality gates apply, and how to maintain compliance.
Evaluation & Monitoring
Dashboards tracking agent performance, error rates, human-in-the-loop triggers, and cost per task.
Team Training & Enablement
Hands-on workshops (not slide decks) that build capability in your existing staff.
Ongoing Optimisation
Monthly refinement of workflows, new capability deployment, and performance benchmarking against your own baseline.
How it changes
The operational shift
Before · Traditional
Brief received
Manually interpreted
Research
4-8 hours per project
Content draft
Senior writer, 1-2 days
Revisions
2-3 rounds typical
Delivery
3-5 days later
Avg. delivery: 3-5 days · High marginal cost per client
After · AI-Native
Brief received
Parsed by agent
Research
Agent: minutes, not hours
Content draft
AI-generated, reviewed
QA + sign-off
Human-in-the-loop
Delivery
50-70% faster turnaround
50-70% faster turnaround · Lower marginal cost per client
Financial impact
The business case for AI-native operations
It changes the unit economics of an agency. Same team, more clients, lower cost per output, higher margins.
Capacity model
Client capacity per full-time employee
AI integration unlocks non-linear capacity gains. Same headcount, dramatically more output.
Traditional
+AI tools (assisted)
+AI workflows (integrated)
AI-native (full stack)
Illustrative model based on agency delivery benchmarks. Results vary by workflow type and team.
Time per task
Hours saved per task per project
Manual workflow vs AI-augmented. Estimates based on typical agency delivery cadence.
The economics
Marginal cost per additional client
As an AI-native platform scales, the marginal cost of each additional client drops steeply, stabilising at a fraction of the traditional rate.
Financial impact
Revenue and margin expansion
Illustrative model: same headcount, AI-native delivery. Revenue grows modestly; margin expands significantly.
Traditional services
Revenue index: 100
AI-native platform
Revenue index: 130
Technical PM automation
How the PM role changes
A technical PM today spends the majority of their time on coordination and documentation overhead. AI-augmented delivery shifts that time toward strategic decisions and client relationships.
Manual · Today
Action items + ticket creation
PM manually extracts action items from call notes and types each into Linear or Jira post-meeting, often hours later, with context lost
Knowledge base sync
Decisions, design rationale, and meeting context sit in scattered notes or emails, rarely reach the wiki, never searchable later
PM tool synchronisation
PM manually updates ticket statuses, sprint boards, and milestone trackers based on verbal updates in standups and ad-hoc Slack messages
Client status reports
Weekly reports written from scratch: PM pulls data from Linear, GitHub, analytics, then drafts narrative, formats, and sends. 3-4 hours per cycle
Risk and blocker detection
PM tracks velocity manually in standups, notices slippage only after it compounds, so the client is flagged late and trust eroded
Onboarding documentation
New team members handed a stale Confluence link and left to reconstruct context from old Slack threads and PR comments
Overhead dominates, strategic work crowded out
AI-Augmented
Action items + ticket creation
Agent parses meeting transcript, extracts action items with owners and due dates, and creates assigned tickets in Linear or Asana automatically
Knowledge base sync
Agent writes structured decision logs to Notion or Confluence after each meeting, linking artefacts, context, and owners so institutional knowledge compounds
PM tool synchronisation
Standup transcripts and commit events trigger automatic status updates across Linear, GitHub Projects, or Jira, so boards stay current without manual input
Client status reports
Agent fetches live data from all project tools, drafts a branded status report structured around client KPIs, ready for PM review and send in minutes
Risk and blocker detection
Agent monitors sprint velocity, open blockers, dependency chains, and SLA timelines continuously, surfacing risks before they become delays
Onboarding documentation
Agent auto-generates role-specific onboarding docs by synthesising codebase structure, project history, ADRs, and team wiki into a navigable brief
PM focuses on decisions and client relationships
Sectors we transform agencies for
Transforming operations for
How we engage
From audit to live operation.
1-2 weeks
Workflow Audit
Map every manual process, identify automation candidates, and estimate the ROI per workflow. Deliverable: a prioritised transformation roadmap.
2-4 weeks
Deploy & Train
Configure agent frameworks around your delivery stack, write the SOPs, stand up evaluation pipelines, and train your team hands-on.
Ongoing
Scale & Optimise
Expand from the workflows that demonstrably work, tracking throughput per head and cost per deliverable in the evaluation dashboards.
Agency Transformation FAQ
Frequently asked questions
What does "deploying an operating system into your agency" actually mean?
Five concrete things: configuring AI agent frameworks around your delivery workflows, automating internal workflows, building SOPs for AI-augmented delivery, establishing evaluation and quality pipelines, and training your team to operate AI-natively. It is a way of working, not a single tool.
Will this replace my team?
No. It multiplies them. The outcome we build for is throughput per head: an agency operating AI-natively serves 3-5× more clients with the same headcount. People move up the stack to judgment, quality, and client relationships; the AI absorbs the production drag.
Does it work with our existing tools and workflows?
Yes. The operating system is configured around your delivery stack and how your teams already hand off work, not a rip-and-replace. We map your workflows first, then deploy against them.
How long does the transformation take?
Weeks, not quarters. We start with a workflow mapping, deploy the first AI-augmented workflows quickly, and expand from what demonstrably works, with evaluation pipelines tracking quality and cost from the first deployment.
How do you measure whether it worked?
Throughput per head, cost and time per deliverable, and output quality, tracked in the same evaluation pipelines we deploy as part of the operating system, so ROI is measured, not asserted.
Do we need in-house engineers to run it?
No. Principals configure and deploy the system, and your team is trained to operate it day-to-day. The goal is an agency that runs AI-natively itself, not a permanent dependency on us.
Proof points
Agency Transformation · Singaporedxdy
Collaborating with dxdy (Bayseian Partner Company) on internal AI transformation and client delivery across MarTech, FinTech, and beyond. Transforming agency operations and building AI-powered solutions for their client portfolio.
Let's transform your agency
We'll map your workflows, show you where AI compresses cost and time, and deploy the operating system that gets you to 3-5× throughput per head.