Retail Intelligence
Meets the Power
of AI
Inspinium combines deep retail analytics — econometrics, purchase structure, volume transfer and promo intelligence — with end-to-end AI engineering to turn your data into decisions that drive real commercial growth.
Retail Intelligence.
AI makes it unstoppable.
Retailers are sitting on rich transactional data but lack the analytical depth to act on it. Inspinium pairs proven retail science with AI automation to close that gap — fast.
Retail Intelligence
Core Offering- Econometric price & demand modelling
- Purchase structure & basket analysis
- Volume transfer & cannibalism modelling
- Promo pattern recognition & ROI
- Category & assortment optimisation
- Actionable insights in days, not months
End-to-end AI
Amplified by AI- ML pipeline migration to AI
- Custom LLMs on your proprietary data
- RAG systems for real-time knowledge
- Multi-agent orchestration & DAG pipelines
- AI strategy & executive roadmaps
- MLOps upgraded to AIOps at scale
Retail intelligence &
AI transformation
Deep retail analytics as our core specialisation — powered by end-to-end AI engineering.
Econometrics
Rigorous statistical modelling of price elasticity, demand drivers, and market-mix effects — giving retailers a quantified view of what actually moves volume.
- Price elasticity & cross-elasticity
- Market-mix modelling (MMM)
- Demand forecasting & scenario planning
Purchase Structure
Decode how shoppers build their baskets — understanding category interdependencies, trip missions, and assortment gaps to optimise range and placement.
- Basket & trip-mission analysis
- Category role & interdependency mapping
- Assortment rationalisation
Volume Transfer Matrix
Model exactly where volume goes when SKUs are added, delisted, or repriced — predicting cannibalism, switching, and incremental gain before you act.
- SKU-level switching & cannibalism models
- New product launch impact simulation
- Range rationalisation trade-off analysis
Promo Pattern Recognition
AI-driven analysis of promotional response patterns across products, retailers, and time — identifying optimal promo depth, timing, and frequency to maximise ROI.
- Promo uplift & halo/cannibal detection
- Optimal depth & frequency modelling
- Post-promo demand void analysis
Custom Customer Analytics
Bespoke customer intelligence built around your loyalty data and commercial objectives — from segmentation and CLV modelling to personalised targeting and retention strategy.
- RFM & behavioural segmentation
- Customer Lifetime Value (CLV) modelling
- Churn prediction & retention analytics
- Next-best-action & personalisation
ML Pipeline Migration
We audit your existing ML infrastructure and migrate it to AI-native pipelines — preserving business logic while unlocking generative capabilities.
- Architecture assessment & roadmap
- Pipeline re-engineering
- Data strategy modernization
Custom LLM Solutions
Fine-tuned large language models trained on your proprietary data — delivering domain-specific accuracy that generic models can't match.
- Model selection & fine-tuning
- RLHF & alignment
- Evaluation & red-teaming
RAG System Design
Retrieval-Augmented Generation systems that ground your AI in real-time enterprise knowledge — eliminating hallucinations and keeping answers factual.
- Vector DB architecture
- Semantic search pipelines
- Knowledge graph integration
Multi-Agent Orchestration
Autonomous multi-agent pipelines that coordinate specialist agents via a central supervisor — with DAG-based sequencing, concurrent execution, and checkpoint/resume for enterprise reliability.
- Supervisor + specialist agent patterns
- DAG pipeline orchestration
- Checkpoint, resume & LLM error diagnosis
- Human-in-the-loop review gates
AI Strategy Consulting
A clear, phased roadmap for your AI adoption — aligned to your business objectives, risk profile, and existing tech stack.
- Executive workshops
- Use-case prioritization
- ROI modeling & KPI setting
MLOps → AIOps
Upgrade your MLOps infrastructure to support AI workloads — monitoring, versioning, and continuous evaluation at LLM scale.
- LLMOps toolchain setup
- Prompt versioning & CI/CD
- Cost & latency optimization
From raw retail data to
commercial impact in
4 phases
One seamless delivery framework covering both Retail Intelligence and AI engineering — from first brief to live, self-improving systems.
Discovery & Data Audit
We map your retail data landscape — transactional history, promotional records, category structure, and existing analytics — while scoping the highest-value Retail Intelligence and AI opportunities specific to your business.
Model Design & Architecture
Our team designs the econometric and AI architecture in parallel — selecting the right modelling approaches for purchase structure and volume transfer, alongside the LLM, RAG, or agent framework that fits your use case.
Build, Train & Integrate
Retail Intelligence models are calibrated against your data — elasticities estimated, volume transfer matrices built, promo patterns identified. Simultaneously, AI components are developed and integrated with your existing systems. Weekly demos throughout.
Deploy, Activate & Optimise
Models and AI systems go live with full monitoring and alerting. We run activation workshops so your commercial teams can act on insights immediately — then stay on for continuous model refresh and capability expansion.
Where Retail Science
meets AI engineering
Inspinium was founded by a team of retail analytics specialists and AI engineers who saw the same problem repeated across the industry — retailers had rich transactional data but lacked the analytical depth to turn it into confident commercial decisions.
Our Retail Intelligence practice brings rigorous econometrics, purchase structure analysis, volume transfer modelling, and promo pattern recognition to bear on your data — giving your commercial teams the clarity to act, not just report.
Our End-to-end AI practice then amplifies that intelligence — automating insight generation, embedding models into workflows, and building the AI infrastructure that keeps your retail decision-making ahead of the competition.
Retail depth, not generic AI
Every model we build is grounded in retail commercial reality — not off-the-shelf templates.
Science-led, commercially focused
Rigorous methodology that connects directly to revenue, margin, and volume outcomes.
Capability transfer built in
We embed knowledge into your team so the intelligence stays long after we leave.
Built from real
FMCG experience
Before founding Inspinium, our founder spent years embedded inside one of the world's leading FMCG companies — personally delivering the analytics that now underpin every service we offer.
"Across the beverages and snacking categories, I built price elasticity models covering 600+ SKUs — giving the commercial team quantified impact forecasts before any price move was made. The models ran live for two annual planning cycles, replacing spreadsheet gut-feel with decisions the business could defend to the board."
"I identified that over 30% of promotional investment across personal care and household categories was generating zero incremental volume — it was pure forward buying. The Volume Transfer Matrix I built then showed exactly where cannibalism was occurring across 8 adjacent categories during range rationalisation. Combined, these two workstreams redirected millions of promotional pounds to mechanics that actually grew the category."
"I architected and delivered a full agentic AI system for a large retail operation — a multi-agent pipeline where a planner agent decomposed complex commercial queries, routed sub-tasks to specialised tool-use agents, and synthesised outputs into boardroom-ready insight reports. The system integrated live EPOS data retrieval, econometric model inference, and a RAG layer over four years of category knowledge — all orchestrated autonomously with human-in-the-loop review at critical decision gates. End-to-end latency dropped from three analyst days to under four minutes, while model-grounded recommendations replaced opinion-led category reviews entirely."
Transparent pricing.
No surprises.
Every engagement is scoped to your exact data and category needs. These tiers give you a starting point.
Ideal for category teams wanting to prove the value of advanced analytics on a single category or promotional period before committing to a broader programme.
- 1–2 category scope
- Price elasticity & demand baseline
- Promo uplift & ROI decomposition
- Purchase structure diagnostic
- 4–6 week fixed delivery
- Volume Transfer Matrix
- AI automation layer
For retailers and FMCG teams ready to deploy the full retail analytics suite across multiple categories — with AI-powered insight automation built in.
- Full econometrics & MMM build
- Purchase structure & basket analysis
- Volume Transfer Matrix (full range)
- Promo pattern recognition engine
- AI insight automation layer
- 90-day post-delivery support
For large retailers, buying groups, or FMCG suppliers managing complex multi-retailer data estates, AI engineering needs, and continuous model refresh at scale.
- Multi-retailer & multi-category estate
- Bespoke modelling & AI architecture
- Custom LLM or RAG system build
- On-site workshops & team enablement
- Embedded analyst & AIOps setup
- Priority SLA & continuous refresh
All prices in GBP. Engagements are fixed-scope, fixed-price — no surprise billing. Day rates available from £2,500/day for T&M engagements. Book a free discovery call to get an exact quote for your category estate.
Get early access to
Inspinium
We're onboarding a limited number of clients in our founding cohort. Early access members receive preferred pricing, direct access to our founding team, and co-development input on our platform.
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Priority onboarding Skip the queue when we open to the public
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Founding member pricing Lock in rates 30% below public pricing
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Free discovery call 60-min session to explore your retail analytics gaps and AI automation opportunities
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Weekly AI briefings Curated insights from the Inspinium engineering team
Reserve your spot
Common questions
The minimum we need is transactional sales data at SKU level — weekly or daily — ideally covering at least two years to capture seasonal patterns. Promotional mechanic data (dates, depth, mechanic type) and basic product hierarchy are also needed for promo modelling. For market mix modelling we'll also use market share, media spend, and external factors like weather or economic indicators. We'll audit your data in the discovery call and identify any gaps before scoping begins.
Yes — and combining both sources is where the most accurate models come from. Retailers typically hold EPOS data with full basket and channel context, while manufacturers often have shipment, distribution, and media investment data. We routinely blend these with third-party panel data (e.g. Kantar, Nielsen) to build the richest possible picture of purchasing behaviour and channel dynamics.
We sign full NDAs and Data Processing Agreements before any data is shared. Retailer EPOS data is commercially sensitive and we treat it accordingly — it is never used outside the scope of your engagement, never shared across clients, and deleted at project close unless you request archival. We support secure cloud environments and can operate within your own infrastructure if required by your data governance policies.
We use held-out test periods and out-of-sample validation — never just in-sample R². Our elasticity models consistently achieve 90–95% accuracy on validation sets. Volume Transfer Matrices are validated against observed switching during actual range changes or de-lists. We report confidence intervals, not just point estimates, so your commercial team can make decisions with a clear understanding of model uncertainty.
A Discovery engagement (single category diagnostic) typically takes 4–6 weeks from data receipt to final output. A full Intelligence build covering multiple categories and a AI automation layer typically runs 10–14 weeks. Enterprise programmes are phased and scoped individually. We commit to fixed timelines in writing before any work begins — and we don't start the clock until the data is in.
Every engagement includes a written scope with defined deliverables, model accuracy benchmarks, and commercial impact targets. If we don't hit the agreed metrics, we continue working at no additional charge until we do. We include a post-delivery support period in every engagement so your team can embed the outputs confidently — we don't hand over a model and disappear.