An AI-native CRM, designed from the data model up
A CRM where intelligence is part of the data model, so every record can be summarised, prioritised and acted on from the first day.
- Fits
- Sales organisations with field teams
- Platforms
- Android, iOS and web
- Layers involved
- Applications, Cloud and agents
- Engagement model
- Fixed-scope project or product partnership
A representative scenario that shows how we approach this kind of product, rather than an account of a specific engagement.
The scenario
Sales teams spend a large part of each day keeping records current and remembering to follow up. The more an AI assistant can rely on records that were designed for it to reason over, the more of that work it can take on. This scenario starts from a simple question: what does a CRM look like when summaries, priorities and next actions are part of the data model itself?
Objectives
- Reduce manual data entry for the sales team
- Draft summaries and follow-ups automatically after every interaction
- Launch on Android, iOS and web at the same time, at full parity
Design targets
- Summary response
- Target under two seconds
- Data ownership
- Customer-controlled and exportable
- Platforms
- Three, at parity from launch
- AI quality
- Evaluation set agreed before build
Our approach
This scenario involves two of the five layers: the applications layer and the cloud and agents layer. On-device AI, firmware, and hardware and sensors do not apply, because the product lives entirely in software.
One product on Android, iOS and web
What we build
A single feature specification, a shared design system and a shared acceptance test suite, with native implementations on each platform.
Key decision
Parity is planned from the data model rather than screen by screen, so the three platforms stay aligned as the product grows.
AI capabilities built into the data model
What we build
Summaries, embeddings and suggested next actions are stored as versioned fields on each record, refreshed by background enrichment jobs and exposed to agents as typed tools.
Key decision
Precomputing AI fields keeps every screen fast and makes inference costs predictable, instead of calling a model on every page load.
Delivery path
An indicative sequence. The length of each phase depends on the product, and we agree it with you during discovery.
- Phase 1
Discovery and data model
Map the sales workflows, agree how success will be measured and design the AI fields into the schema.
- Phase 2
Core platform
Build the pipelines, records, permissions and background enrichment that keep AI fields current.
- Phase 3
Applications
Build Android, iOS and web against one specification, verified by the shared acceptance suite.
- Phase 4
Evaluation and launch
Run quality gates against the evaluation set, then release in stages with monitoring in place.
Outcomes the approach is designed to deliver
Follow-ups drafted after every call
Summaries and next actions are prepared for review as soon as an interaction is logged.
Less time spent on data entry
Records are enriched in the background, so the team records less by hand and spends more time selling.
One product on three platforms
Every feature ships to Android, iOS and web together, from the first release onwards.
Key takeaway
Building the evaluation set early gives every later feature a clear quality benchmark to meet before it ships.