Where AI pays off in consumer goods — and how to make it work
We help consumer goods companies find the tasks where AI earns its keep, build the models and agents that do them, and run everything inside your security model. Predictive models where the answer is a number, language models where it is text or an action, a platform when the whole company needs AI, and consulting when the question is "where do we start".
The map: every function, two kinds of AI
Most companies do not lack AI tools. They lack a clear view of where AI changes the number. For every function in a consumer goods company: what a language model does and what data science does.
| Function | Language models & agents reads · writes · answers · acts | Data science predicts · optimises · detects |
|---|---|---|
| R&D and new products | search across specifications, regulations and past formulations; consumer research summarised into a brief | formulation cost modelling; launch success and cannibalisation prediction; sensory and preference analytics ● |
| Procurement | tender and contract analysis; supplier correspondence drafted and tracked | raw material price forecasting; should-cost models; supplier risk scoring |
| Manufacturing and quality | incident investigation assistant ●; regulations and SOP assistant ●; shift reports summarised | predictive quality; anomaly detection on line and sensor data ●; yield and waste prediction |
| Supply chain and logistics | order and shipment questions answered from the systems; exceptions explained and routed | demand forecasting with 20+ signals ●; inventory optimisation; route, load and carrier optimisation; delivery-in-full prediction |
| Sales and key accounts | one picture of a customer from five systems ●; meeting pack by template; drafts to retailers; CRM in the messenger ●; calls summarised into the CRM ● | customer segmentation; next-best-action for the account; listing-gap and churn detection |
| Trade marketing and revenue management | "why did this promo miss" answered with drivers; post-evaluation narratives written from the numbers | promo uplift and baseline ●; price elasticity ●; promo and trade-terms optimisation; assortment and shelf optimisation |
| Marketing and consumer insight | reviews and social listening analysed at scale; research synthesis; content drafts in brand voice | marketing mix modelling; consumer segmentation; test-and-learn analytics |
| Finance and planning | variance explained by driver; the monthly pack that assembles itself; policy questions answered from the documents | driver-based forecasting; cost anomaly detection; cash forecasting |
| HR | CV parsing ●; response triage and first screening ●; employee surveys with follow-up questions ●; onboarding assistant | attrition risk; workforce planning |
| Customer service and B2B orders | order status and claims handled in chat; inbound e-mail classified and routed ◐ | claims and returns prediction; service-level risk |
| Legal and compliance | contract clause extraction and comparison; regulation tracking; document graph ◐ | sensitive data detection in text ● |
| Everyone | corporate AI chat ●; meeting notes ●; document summaries ●; assistants on a team's own knowledge base ●; support bot ● | an AI analyst over the data warehouse ● |
● done before · ◐ in development · Not sure where to start? That is the first service.
Four services, one team
AI consulting
Where to apply AI in your business and in what order. An opportunity map by function, data readiness, business cases, a roadmap, and the governance to run AI responsibly. AI literacy for leadership teams.
Entry: AI opportunity assessment, 2–4 weeksData science
Predictive and optimisation models trained on your history: forecasting, uplift and elasticity, segmentation, anomaly detection, optimisation. Backtested against your current method, written back into your processes, retrained with monitoring.
Entry: model sprint, 4 weeks, one target metricLanguage models and agents
Assistants on your knowledge, agents for a role that read your systems and act in them with confirmation, automation of document and communication flows, voice and calls. Answers come from your data through a governed dictionary of metrics.
Entry: pilot, 12 weeks, fixed priceAI platform
A corporate AI environment in your perimeter: one gateway to frontier, open and local models; a chat for every employee; a builder for assistants; API keys with usage per department; guardrails and sensitive data detection. The foundation the other three run on.
Entry: platform deployment, 8–12 weeks to first usersAcross all four: production and run by the same team, or a dedicated AI team on your roadmap.
Frontier when it pays. Smaller when it is enough. Local when it must.
- Two families, one rule.A predictive model where the answer is a number; a language model where the answer is text or an action. Both chosen by a benchmark on your tasks, not by fashion.
- Frontier modelsfor reasoning-heavy work — complex documents, multi-step agents — where quality moves the result.
- Smaller and open modelsfor the volume work — classification, extraction, routine answers — where they pass the benchmark at a fraction of the cost.
- Local modelswhere the data cannot leave your perimeter.
- One gateway routes every task to its model,tracks cost per answer and lets you switch providers without rebuilding anything.
- The cheapest model that passes the benchmark wins.
The agent sees what you see. Nothing more.
- Role model built in.The agent works under the rights of the person asking — single sign-on, roles, row- and column-level access. A key account manager's agent sees that manager's customers, not the company.
- Actions need a confirmation.Reading is default; writing to your systems is a draft until a person confirms it. Every action leaves a receipt.
- Audit of everything.Every question, query, action and model call is logged and attributable.
- Sensitive data stopped at the exit.Access keys and personal data are detected before anything leaves the perimeter.
- Your perimeter or your cloud.Models and data stay where your policy says. No traffic to a model provider unless you allow it.
Your systems stay where they are
- Connectorsto ERP, CRM, planning, trade promotion, warehouse and transport systems, data platforms, retailer portals and files. Read by default, write with confirmation.
- Tools through MCP.Every connector and action is exposed through the Model Context Protocol, so any agent — ours, or the copilot you already have — can use it under the same role model.
- One semantic layer underneath.A dictionary of metrics, hierarchies and master data mappings, owned by your analyst, so the agent, the dashboard and the forecast give the same number.
- No second data warehouse.We connect to your data and build meaning on top; we do not move your data around.
From the map to production
- AI opportunity assessment2–4 weeks. Interviews across functions, systems and data, a shortlist with a business case each, a roadmap, a first step at a fixed price.
- Model sprint or pilotData science: 4 weeks, one metric, backtest, go / no-go. Language models: 12 weeks, one role, real users, quality targets in the contract.
- Production and runHardening, SSO and audit, more functions, monitoring and retraining, support with response times.
- Dedicated AI teamA lead, engineers, a data scientist as needed, monthly. On our platform or in your perimeter. What is built for you is yours.
Work we can talk about
- AI platform · a large enterprise.One gateway to many models, a chat for every employee, an assistant builder, usage per department, sensitive data detection at the exit. In production.
- Language models · everyone.A support bot that resolves routine requests itself — access, DNS names, accounts — or files a ticket to the right team; a regulations assistant in the portal; the CRM in the messenger and calls summarised into the customer card.
- Data science · supply chain and trade.Demand forecasting from the shelf with 20+ signals and the forecast decomposed by driver; promo uplift and price elasticity models in production.
Questions we hear first
That is the first service. The assessment ends with a map of your functions, a shortlist with business cases and a first step at a fixed price.
No. One gateway routes each task to the model that passes the benchmark at the lowest cost. You can change the mix later without rebuilding.
No. It works under the user's own rights and logs every query. Writing to a system requires a confirmation.
Yes. Our connectors and actions are exposed through MCP, so other agents can use them under the same rules.
Map your AI use cases
Two to four weeks, every function, a shortlist with business cases and a first step at a fixed price.