When the answer is build, our lab builds it.
Advice comes first: we help leadership teams decide what AI to build, what to buy and what to skip. The lab is for when the answer is build. When a pilot earns it, the same team takes it from an MVP to production inside your CRM, ERP, cameras or data stack, so nothing gets lost between the recommendation and the build. If another team is a better fit, we help you choose them and review their work.
| MVP | Usually about two weeks, answering the key technical and business questions |
|---|---|
| Integrates with | Your CRM, ERP, cameras or data stack |
| Ships with | Monitoring, evaluations and an audit trail |
| Deploys to | On-prem, your VPC or the cloud |
| Your team | Works alongside ours, with training for the builders who will run it |
AI agents
Find the workflows worth automating and validate one with a working prototype. We build conversational and multi-step agents for sales, support, onboarding and internal work, on web, WhatsApp, your CRM or a custom interface. Every agent ships with written permissions, an audit trail and a safety test suite. The governance and policy side is on the agentic AI consulting page.
Sales assistant on WhatsApp
Answers product questions, qualifies the lead, books a store visit and writes the record back to the CRM. Anything outside policy goes to a person.
Safety and evaluation
Every release is tested against adversarial prompts and policy rules before customers see it.
| Test suite | Cases | Status |
|---|---|---|
| Prompt injection | 140 | pass |
| Discount abuse | 62 | pass |
| PII leakage | 88 | pass |
| Off-topic drift | 45 | review |
| Hallucinated stock | 51 | pass |
Computer vision
Test whether the cameras you already have can measure footfall, dwell time, shopper movement and shelf health, or catch tool breaks, abnormal vibration and part-quality problems on the shop floor. Pipelines run at the edge or in the cloud.
Tool-break event log
Camera and vibration signals combined to flag a broken tool before the next batch of bad parts.
Store floor dashboard
Footfall, dwell and zone heatmaps from existing CCTV, so merchandising decisions start from how shoppers actually move.
Machine learning
Find the customers at risk of leaving and work out which retention offers are worth testing. We build churn, recommendation, demand forecasting, RFM and predictive maintenance models, and measure lift against a holdout before recommending a rollout.
Insights that land in tools teams already use
Churn scores, RFM segments and demand forecasts delivered into the CRM, the campaign tool or the replenishment sheet. The best AI products disappear into the workflows people already have.
| RFM segment | Customers | Churn risk |
|---|---|---|
| Champions | 3,120 | low |
| Loyal, lapsing | 5,486 | medium |
| At risk, high value | 1,204 | high |
| New, one visit | 9,870 | medium |
Enterprise ready
For teams with security reviews and compliance owners, the controls are built in from the first sprint.
The builds above are illustrative demos with made-up data. They are not client deployments. Client names and figures are shared on request, under NDA.
Have something worth building?
Start with a free advisory session. If the use case holds up, we scope a validation sprint, and we will tell you if a vendor or another team would build it better.
Book a free advisory session