AI-Powered Strategy for CPG Leaders

We identify the key business processes holding your team back and automate them through a custom hosted application — delivered for less than the cost of a new headcount. Streamline manual activities, automate data processes, and speed execution times with purpose-built solutions that are unique to your company’s operations.

• Process discovery starts with the work as it is actually done — the spreadsheets, the email threads, the re-keying — not with an off-the-shelf module you have to bend your business around.
• AI-assisted development compresses build timelines from quarters to weeks, so a working application reaches your team while the problem is still the priority.
• Language models handle the messy middle: reading documents, classifying exceptions, and drafting the routine correspondence that used to consume an analyst’s afternoon.
• Hosted, monitored, and iterated by us. You get the capability without the headcount, the infrastructure, or the maintenance backlog.

One application, aimed at one expensive process, paying for itself well inside the first year.

Trade is often the second-largest line on a CPG P&L and the hardest to attribute. We build the measurement layer first — clean baselines, true lift, and cannibalization by account and event — then put an AI planner on top of it.

• Machine-learned baselines separate genuine incrementality from seasonality, pantry loading, and competitive activity, so lift numbers survive scrutiny.
• Scenario agents simulate an entire promotional calendar — depth, frequency, mechanic, and account mix — and return the plan that maximizes profit rather than volume.
• Language models read deal sheets, customer contracts, and event confirmations into structured terms, so planned and actual spend reconcile without manual entry.
• Post-event analysis writes itself: a plain-language readout for every event, with a recommendation for the next cycle already drafted.

A trade calendar you can defend line by line, and spend that migrates toward the events with proven return.

Deductions arrive as a flood of retailer remittance detail that rarely matches your invoice. Most teams write off anything small because research costs more than the claim is worth. AI changes that arithmetic.

• Document AI reads remittance files, bills of lading, proof of delivery, and promotional agreements in whatever format the retailer sends — PDF, EDI, or portal export.
• Matching models tie each deduction to its originating order, shipment, and trade event, then classify it as valid, invalid, or duplicate with a confidence score.
• Agentic workflows assemble the dispute packet, draft the retailer-specific narrative, and file it — routing only genuinely ambiguous cases to an analyst.
• Root-cause clustering surfaces the upstream errors generating repeat deductions, so the volume falls instead of merely clearing faster.

Recovery on claims you were writing off, a shorter cash conversion cycle, and a deductions queue that stops growing.

Shipment history alone cannot see a promotion, a weather swing, or a competitor’s reset. We build the forecast on the signals that actually move consumption, then wire it directly into production and replenishment planning.

• Gradient-boosted and deep-learning models blend POS, shipments, promotional calendars, pricing, weather, and category trend into item-store or item-DC forecasts.
• Probabilistic outputs give a range rather than a point, so safety stock and service targets are set against real risk instead of a flat rule of thumb.
• Exception agents read overrides and demand signals, explain in plain language what changed, and escalate only what genuinely needs a planner’s judgment.
• New and short-history items forecast from attribute-based analogs, so a launch does not wait a year for its own data.

Fewer stockouts on the items that matter, less obsolescence on the ones that don’t, and a plan operations can actually run to.

Freight cost is not set once at contract time. It is decided in hundreds of small daily choices about mode, carrier, consolidation, and timing — each too minor to review, and collectively worth several points of margin.

• Rate models benchmark every lane against the market, so you know which contracts have drifted out of line well before renewal.
• Optimization engines consolidate orders, select mode and carrier, and build multi-stop loads against real service commitments and appointment windows.
• Language models audit carrier invoices against the contract and the delivery record, catching detention, accessorial, and re-weigh charges that would otherwise be paid without question.
• Predictive ETA and exception agents flag at-risk loads early enough to protect OTIF and avoid retailer fines.

Lower landed cost per case, with on-time performance improving rather than being traded away for it.

Procurement savings hide in two places: documents nobody has time to read, and spend nobody has classified. AI is unusually well suited to both.

• Language models extract pricing terms, indexation formulas, minimum volumes, rebate tiers, and renewal dates from every supplier agreement into a register you can query.
• Spend classification models clean and categorize AP detail automatically, exposing tail spend, maverick buying, and duplicate suppliers.
• Should-cost and commodity models tie input pricing to published indices, so you know immediately whether a supplier increase is justified.
• Sourcing agents draft RFPs, normalize bid responses onto a common basis, and summarize supplier risk from public filings, news, and your own performance history.

Renewals stop arriving as surprises, and negotiations start from evidence instead of assertion.

You already pay for syndicated data. The constraint is rarely the data itself — it is access. Brand and sales teams wait in an analyst queue for answers they need in the meeting they are walking into.

• Harmonization pipelines reconcile syndicated, POS, and internal shipment data onto one product and customer hierarchy, so the numbers agree across sources.
• A retrieval-augmented language layer answers plain-language questions about share, distribution, velocity, and price gaps — and shows the query and source rows behind every answer.
• Anomaly detection watches for distribution voids, price gaps, and share shifts, then pushes the alert rather than waiting to be asked.
• Business reviews and category decks generate from live data with the narrative already drafted, ready for a human to sharpen.

Answers in minutes instead of days, and analysts returned to the work that actually needs their judgment.