AI that operates
in your distribution.
Not in a pilot phase.
A dedicated AI team that embeds inside your organisation, works with your ERP and your data, and stays until the workflow is live. First measurable result in two to four weeks.
Enterprise AI projects do not fail for lack of ideas. They fail for lack of delivery ownership.
Most distributors at this scale already have data, systems, and an AI initiative. What they lack is a team that owns the path from experiment to daily operation.
The pilot that never becomes production
Proofs of concept validate the technology and end in a presentation. Nobody owns the integration, the change management, or the outcome. The pilot result stays on a slide.
Fragmented ownership, no accountability
The AI vendor owns the model. The system integrator owns the connection. Internal IT owns the ERP. Nobody owns the revenue outcome. When something breaks, everyone has a handover document.
Integration timelines that consume the ROI
Building ERP connectors, channel integrations, and data pipelines from scratch takes months. By the time the infrastructure exists, the business case has been revisited three times and the champion has changed roles.
Our team works inside your organisation. Not beside it.
The AI Lab is not an advisory engagement. Our engineers embed in your operational environment, work with your teams and your systems, and are accountable to a production outcome - not a deliverable document. We do not leave when the prototype is done.
Dedicated AI team, embedded in your operation
Engineers who work as an extension of your organisation. They attend your operational meetings, use your data, and are measured by the same outcomes your business is measured by. No account manager in the middle.
Platform that already runs in distribution
B2B Hive, ERP connectors, WhatsApp and Viber channel integrations, buyer behavior models - already in production at scale distribution clients. Your engagement starts at integration, not at infrastructure.
Production ownership, not prototype handover
We define success as a working AI workflow, in your production environment, processing real transactions. When that is achieved, we expand. We do not hand over documentation and disengage.
Four ways the AI Lab is structurally different from every alternative you are evaluating.
Enterprise distributors at this scale typically compare us against big consulting firms, specialised AI agencies, and the option of building an internal team. Here is what each comparison misses.
You do not pay to build what already exists.
Consulting firms and AI agencies start your engagement by assembling integration infrastructure. ERP connectors, channel APIs, buyer data models. That is six to twelve weeks of your budget before any AI is written. Our platform already has this infrastructure in production. Your engagement starts at the application layer.
Distribution domain knowledge that is already calibrated.
Buyer ordering behaviour, ERP schemas across major platforms, route and territory logic, promotional mechanics, dormancy patterns - this is not something we are learning alongside your engagement. It is the foundation our models are built on. The difference shows in how quickly recommendations become accurate and how few false starts occur.
Accountability to production, not to a report.
A consulting firm's deliverable is a strategy document. An internal team's deliverable is a roadmap. Our deliverable is a working AI workflow processing your transactions. The commercial structure of the AI Lab is tied to production outcomes, not to time and materials. That changes what we are incentivised to do.
Your investment feeds a platform that keeps improving.
A structured portion of every AI Lab engagement - scoped from day one, not retrospectively - is abstracted into the B2B Hive platform. This means the capabilities you commission continue to develop and improve after your engagement concludes, without requiring additional investment. The system does not stop when the sprint does.
Six operational domains. Each with a measurable revenue or cost outcome.
Use cases are not feature descriptions. They are business problems with a quantifiable before and after.
Order generation and replenishment automation
AI-triggered reorder prompts at the buyer level, timed from ERP consumption data. Fewer missed between-visit orders. Higher capture from the existing buyer base without rep involvement.
Customer communication on WhatsApp and Viber
Structured, ERP-triggered buyer conversations via the channels buyers already use. Order confirmation, status updates, reorder prompts, and promotion delivery - without manual rep effort per message.
Long-tail and dormant account activation
Systematic identification of buyers who have gone quiet, with a structured three-step reactivation sequence. Long-tail SKU recommendations from peer-group ordering patterns. Revenue recovered from accounts that would otherwise fall out of the active base.
Basket completion and real-time recommendations
ERP-aware basket suggestions delivered at the point of order confirmation. Each recommendation is buyer-specific, not category-generic. Acceptance rates that consistently outperform broadcast recommendation systems.
Internal workflow and coordination automation
Reduction in manual coordination overhead across routes, depots, and teams. AI-structured communication for dispatch, exception handling, and cross-department handoffs - without adding headcount.
Product data enrichment and catalog intelligence
Structured product attributes and category tagging generated from unstructured inputs at catalog scale. Better catalog data improves recommendation accuracy, search relevance, and downstream reporting - without manual data entry.
At this scale, the return profile is disproportionate.
At the operating scale of a distributor with several hundred to several thousand active buyer accounts and a broad product catalog, even single-digit percentage improvements in order frequency, basket value, or operational throughput represent a material revenue and margin event. The mechanisms being unlocked - between-visit demand capture, basket completion, account reactivation - already exist in the buyer base. The question is not whether there is value in activating them. The question is whether the delivery model is capable of getting there.
A structured AI Lab engagement is not a cost line. It is an investment with a return that compounds as each workflow matures and expands to additional routes, categories, and markets. Each deployment phase provides a validated ROI basis for the next. The commercial structure of the engagement is calibrated to this logic.
The AI Lab is selective about which organisations we take on.
The delivery model we use requires a specific foundation. We have calibrated our capacity to work with organisations where both the infrastructure and the ambition are in place. Below are the criteria that define a strong fit.
Revenue scale: upper range of your market
Typically distributors with annual revenue in the nine-figure range, or the clear category leader at regional scale. The AI Lab is designed for operational complexity that justifies embedded delivery.
Several hundred to several thousand active buyer accounts
The mechanisms the AI Lab activates - between-visit ordering, dormancy recovery, basket completion - produce material outcomes at this buyer base scale. Below this threshold, the economics are different.
Accessible ERP with order history and product catalog
We require at least 90 days of order history and a structured product catalog as the data foundation. API access is preferred; structured CSV export is supported for all major platforms.
Internal IT or data function that can support integration
Our team manages the AI development. Your internal team manages ERP access and internal approvals. We need a counterpart, not a full data engineering team.
Leadership-level sponsorship for the initiative
Production AI requires decisions that span departments. An engaged sponsor at CEO, COO, or equivalent level is not optional. It is a prerequisite for delivery speed.
Three ways to work together. Scope is defined together, before the engagement begins.
Every engagement is scoped based on your operational environment, use case complexity, and internal readiness. The structures below describe the typical shape of each commitment level. No public pricing: enterprise distributors of this profile expect a custom commercial structure, and we deliver one.
One clearly scoped priority use case, delivered to production. Shared AI engineering capacity and platform support. Designed to establish integration, validate the model in your environment, and produce a measurable result before any larger commitment is made. Minimum six months.
Continuous AI rollout across two to three active workflows. A dedicated AI engineering team operating on a structured sprint cadence, with product and project support throughout. For organisations moving from a single proof of concept to an ongoing operational AI programme. Minimum twelve months.
A full enterprise AI programme across multiple operational domains in parallel. Dedicated senior AI execution team, architecture leadership, and executive steering. Advanced integrations, multi-domain rollout, and a co-designed strategic roadmap. Minimum twelve months.
Scope, team size, and duration are defined together after an initial scoping conversation. Commercial terms are structured around the engagement, not a published rate card.
Active deployments. Anonymised by client request. Concrete by outcome.
Names are not published. Operational specifics and outcome figures are real.
Regional distributor, 180 active buyer accounts
WhatsApp-driven AI ordering workflow in production. Between-visit order capture implemented and measured. Dormant account reactivation sequence active. Full five-lever revenue model deployed across all buyer segments.
Distributor, 240 active buyer accounts, mixed categories
Basket completion recommendations and long-tail SKU activation deployed in parallel. Highest long-tail contribution in sample. Dormancy recovery rate 38% within 21 days of initial outreach. Seasonal SKU sell-through: 41% versus 12% in prior year with rep-only model.
HoReCa distributor, 95 restaurants, hotels, and cafes
Morning order cycle automation implemented after NORA engagement analytics identified 07:30 as peak conversion window. Premium category promotion engine deployed. Highest promotional conversion rate in sample across craft beer, spirits, and wine categories.
Delivery methodology
All engagements operate on two-week sprint cycles. Each sprint produces a working output - not a status update. Phases: use case scoping, ERP integration and data validation, workflow development and testing, production launch, adoption and expansion. No intermediary presentation layers.
Measurement approach
All uplift figures are measured against a 90-day pre-deployment baseline, with seasonal controls applied where multi-year ERP history is available. Success criteria are agreed before the engagement begins, not defined after the results come in. Every engagement is independently measurable.
Four steps from first conversation to first result in production.
There is no discovery phase that costs you three months before work begins. The scoping conversation is the starting point, not the workstream.
Initial conversation
We understand your operational environment, your data foundation, and the problem you want to solve. You understand our model and what we need from your side to deliver. 45 minutes. Structured agenda.
Use case scoping
Together we identify the highest-value AI application given your data maturity and operational complexity. We define success criteria, confirm the integration scope, and agree the delivery structure before work starts.
Embedded sprint delivery
Our team is in. Two-week cycles. ERP integration, workflow development, testing, production launch. Your teams are involved as needed, not burdened with project management overhead. We own the delivery.
Production, measurement, expansion
Live workflow. Outcomes tracked against the agreed baseline. A clear expansion path to the next use case, the next market, or the next operational domain - with validated ROI at each step as the basis for the next decision.
If your organisation has the scale
and ambition for this, the next step
is a short conversation.
Tell us about your distribution operation. We will confirm whether there is a structural fit and set up a 45-minute scoping call with the right people on both sides. No sales process. No deck. A direct conversation about your operational reality and whether the AI Lab can change it.
We respond to every enquiry within one business day. Conversations are conducted under NDA by default.