Starting point
Sales, campaigns, customer behavior, website activity, inventory, and technical health spread across six separate systems.
the intelligence layer behind a complex ecommerce operation
Thousands of products, one view that keeps up
project at a glance
What we built
A custom application that combines AI with verified inputs to produce one dependable daily briefing, focused reports, and practical next actions.
Who uses it
Leadership and teams across marketing, sales, ecommerce, and operations.
Our role
Business discovery, strategy, data architecture, UX, engineering, AI, integrations, launch, and ongoing technical operation.
An established dental supplier had operational data spread across six systems, each showing only part of the business. Node59 unified that fragmented evidence in one application with AI support for the decisions each team needed to make. The shared view was designed to handle the daily activity of a complex ecommerce operation without asking teams to reconstruct the same picture from disconnected sources.
Bringing every signal together
On a schedule, the application collects fresh records from the connected sources, sanitizes them, checks expected coverage, and converts them into stable facts with exact values, units, periods, identities, and origins. The evidence is then organized for leadership, sales, customer behavior, marketing, website experience, stock, demand, and market activity. Each team sees what matters to its responsibilities while keeping the shared source context available for closer review.
Facts before conclusions
Each weekday morning, exact figures, comparisons, and charts are calculated before AI interprets them. Every number in the guidance comes from a controlled reference, so the model cannot freely type or alter a figure. The briefing explains priority actions and conditions worth watching. AI can connect verified findings, but cannot invent causes, intentions, results, or commercial stories. A separate review corrects or withholds unsupported statements.
Looking beyond the store
The application reads current public pages from selected competitors and the storefront, including linked promotions and campaigns built around imagery, then compares featured items, offers, merchandising, and seasonal activity. Older captures remain available as reference but are excluded from current recommendations. It also connects stock, fulfilled sales, open demand, incoming products, arrival timing, selling speed, and recorded cost. Direct calculations identify what to promote, protect, or investigate, while AI explains the strongest actions.
Read access keeps people in control
Connected systems grant read access only. The application can study orders, customers, campaigns, products, website behavior, stock, and technical reliability, but cannot alter them. It can recommend an investigation, test, repair, update, or monitoring decision while people remain responsible for the next action. During a refresh, the previous complete set remains visible until every required area collects, analyzes, validates, and publishes successfully, preventing a partial or inconsistent picture.
A clearer way to run the day
The result is a shared operating rhythm rather than another dashboard. Leadership, marketing, sales, ecommerce, and operations begin with the same dependable picture, can inspect supporting figures, understand why a change deserves attention, and choose the next move without rebuilding the story from separate tools. Plain language helps less technical users, source context supports experienced staff, and Node59 continues to operate and evolve the product through daily use.
Inside the engine room
Scheduled ingestion and deterministic calculations turn source records into structured evidence for the AI to use in each briefing.
Evidence architecture
Incoming records are sanitized before storage. Each observation receives a stable identity, exact value, unit, reporting period, and canonical reference, letting generated explanations use verified figures without rewriting them.
Time and coverage control
Completed reporting periods are aligned across sources. Partial coverage is recorded, incompatible windows are not compared, and current conditions are never presented as historical trends.
Grounded generation
AI receives only the facts relevant to each area. Claims, figures, and names enter the report through controlled references, while unsupported causes, intentions, and outcomes are rejected.
Quality review and correction
A separate review checks every statement written by the model. Validation failures trigger focused correction attempts, and content that still fails never reaches the published briefing.
Complete set publication
Collection and analysis run as one coordinated refresh. A new set replaces the previous version only when every required report succeeds, preventing incomplete or mixed updates.
Traceable operations
Every collection, model request, retry, validation problem, and failure is recorded with its timing and status. Operators can inspect freshness and run health without exposing sensitive commercial information.