Content production
On-brand copy variations for campaigns, product content and lifecycle messages.
Relevant at scale
AI for Marketing & Customer Engagement
Segmentation, content production and lifecycle messaging grounded in your data and brand rules.
What this is
AI helps marketing most in two places: producing on-brand variations faster, and deciding who should receive what. We build both, with human approval on anything customer-facing and consent handling built into the sending path rather than bolted on.
The problem
What this service addresses
If more than one of these is familiar, there is usually a case worth scoping.
- Campaign content production is the bottleneck on everything the team wants to test.
- Segmentation is coarse, so most recipients get messages that do not apply to them.
- Lifecycle messaging is the same for every customer regardless of behaviour.
- Nobody can tell which content or segment actually drove the result.
Scope
What we deliver
Every item below is a deliverable you receive, not an activity we perform.
- Content generation grounded in your brand guidelines, product data and prior approved work
- Approval workflow so nothing customer-facing publishes unreviewed
- Behaviour-based segmentation built from your transactional and engagement data
- Lifecycle messaging across email, WhatsApp and in-app channels
- Consent and preference enforcement in the sending path
- Attribution reporting by content, segment and channel
Capabilities
What the system does
Segmentation
Audiences built from actual behaviour and transaction history rather than static lists.
Lifecycle messaging
Triggered sequences for onboarding, retention, winback and renewal.
Channel orchestration
Coordinated email, WhatsApp and in-app messaging with frequency capping.
Localisation
Market-appropriate variants reviewed by your local teams, not raw machine translation.
Measurement
Results attributed by content variant and segment so testing informs the next round.
Applied
Where it is used
Each of these is a bounded job with a measurable result, which is how we prefer to scope a first build.
01
Campaign content variants
Produces subject lines, body copy and ad variants from one approved brief.
02
Product content at scale
Generates descriptions and metadata from specification data across a large catalogue.
03
Lifecycle sequences
Onboarding, retention and winback messaging triggered by observed behaviour.
04
Abandoned journey recovery
Re-engages abandoned carts and incomplete signups with relevant context.
05
Audience discovery
Surfaces behavioural segments worth targeting that manual analysis missed.
Architecture
How it is put together
An indicative architecture. The final design follows your systems, data boundary and constraints.
-
01
Data sources
CRM, commerce platform, product catalogue, web analytics and engagement history.
-
02
Brand and content layer
Guidelines, approved examples and product data as grounding for generation.
-
03
Generation and approval
Draft, review, approve and publish states with versioning.
-
04
Segmentation engine
Behavioural and transactional rules producing addressable audiences.
-
05
Delivery
Email, WhatsApp and in-app sending with consent and frequency enforcement.
-
06
Measurement
Variant and segment-level attribution feeding the next iteration.
Security & governance
Controls we build in
Access, privacy and oversight are part of the build rather than a later hardening phase.
- Human approval required before customer-facing content is published.
- Consent and preference checks enforced in the sending path, not in campaign configuration.
- Frequency capping across channels to prevent over-contacting.
- Claims and pricing drawn only from approved sources; the system will not improvise an offer.
- Unsubscribe and opt-out honoured immediately across every channel.
Delivery
Our approach
How we run the engagement
Discovery
Process walkthroughs, data review, constraints and a written scope you sign off before build.
Architecture
Data model, integration boundaries, hosting and the security model, agreed up front.
Proof of concept
A narrow build on your real data against an agreed quality bar, so the go-ahead is evidence-based.
Development
Two-week increments, each ending in a working deployment and a review.
Testing
Automated coverage on critical paths, evaluation sets for AI behaviour, acceptance testing with your users.
Deployment
Staged rollout into your infrastructure with monitoring and a tested rollback path.
Monitoring & support
Quality, cost and latency tracking after launch, with handover documentation or an ongoing retainer.
Proven work
Products we have built and sold
We are product builders as well as a services team. These are our own commercial platforms, not client references.
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SIZH Virtual Agent
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A configurable multivendor service-booking marketplace with agentic onboarding, matching and support.
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Questions
AI for Marketing & Customer Engagement — FAQs
The questions we are asked most often about this service.
Will AI content sound generic?
Ungrounded, yes. We ground generation in your approved prior work, brand rules and product data, and keep human approval in the loop. The aim is to speed up production of your voice, not replace it with a neutral one.
Does this replace our marketing platform?
No. We integrate with the platform you use for sending and consent management, and add generation, segmentation and measurement around it.
How do you handle consent?
Checks sit in the sending path, so a message cannot go out to someone who has not consented or has opted out, regardless of how the campaign was configured.
Can it work across languages?
Yes, with local review. We generate market variants and route them to your local teams for approval rather than publishing raw translations.
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