Keep your BI tools. Make them actionable.
Scoop is not a dashboard replacement. It's an AI-powered performance management layer that uses your existing BI data to proactively diagnose issues, generate action plans, and close the loop on execution: automatically, at every location, every cycle.
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We trained it once. Now it handles 95% of our diagnostic work without us.
Dashboards answer 'the what'. The hard part is 'what now?'
Your BI stack surfaces the numbers. Scoop combines them with the operating knowledge inside your best people, codified into the model, then runs it automatically: screening every location every cycle, diagnosing the cause, delivering role-specific action plans, and tracking whether they moved the needle.
Same infrastructure, more outcomes, less inbox.
You built the data infrastructure, defined the KPIs, and trained the teams. Scoop sits on top of all of it: no new reports, no data plumbing, no rebuild required. It takes your existing investment further: turning your data into action plans for every manager, every cycle, automatically.
Diagnostic interruptions drop out of your queue
Managers get the analysis from Scoop, not from you. No more ad-hoc "why is location X down?" requests.
No new reports. No new plumbing.
Scoop maps to your existing BI data. If the metrics are there, we’re good. Your setup stays exactly as it is.
Your KPIs generate action, not just numbers
The infrastructure you built gets used at every level of the business, every cycle, not just by the people who know how to use a dashboard.
Focus on what only you can do
Strategic analysis, new data questions, higher-value work, not "can you pull this for me?"
How Scoop is architected
You know what an LLM can do. So do we: we've been running one in production for years. The part that took time was everything else: the three proprietary layers that make it consistent, scalable, and right for your specific operation.
Not just your KPIs: your best practices for interpreting them. What patterns matter, which drills to run, how findings translate into action. The operating knowledge inside your best people, structured so the AI can apply it consistently.
The execution engine: dimensional analysis, peer set creation, period-over-period comparisons, ML-driven variance detection, fiscal vs. calendar awareness. Capabilities a typical BI team wouldn't have: all callable by the AI.
Decides which investigation paths the AI takes: encoded with the techniques an experienced BI analyst would use to explore and diagnose data. Every run is reproducible and auditable.
Scoop uses multiple LLMs for different tasks that bring specific data science and ML capabilities to the Tooling layer; giving Scoop capabilities beyond what typical commercial LLMs can handle.
Scoop has spent years ensuring outputs match your existing BI reports, handling edge cases across industries and data environments, building the release gates that keep every finding tied to source data, and making the whole system governable and secure at enterprise scale. That's what you're buying, not just the model.
· Tribal knowledge, codified
Not just your KPIs: your best practices for interpreting them. What patterns matter, which drills to run, how findings translate into action. The operating knowledge inside your best people, structured so the AI can apply it consistently.
· The diagnostic harness
Decides which investigation paths the AI takes: encoded with the techniques an experienced BI analyst would use to explore and diagnose data. Every run is reproducible and auditable.
· Analytical execution engine
The execution engine: dimensional analysis, peer set creation, period-over-period comparisons, ML-driven variance detection, fiscal vs. calendar awareness. Capabilities a typical BI team wouldn't have: all callable by the AI.
· Multi-model AI + data science
Scoop uses multiple LLMs for different tasks that bring specific data science and ML capabilities to the Tooling layer; giving Scoop capabilities beyond what typical commercial LLMs can handle.
Scoop has spent years ensuring outputs match your existing BI reports, handling edge cases across industries and data environments, building the release gates that keep every finding tied to source data, and making the whole system governable and secure at enterprise scale. That's what you're buying, not just the model.
The stack, layer by layer.
Layers 1 and 2 are where the rest of the market operates. Layers 3 through 7 are the product.
Semantic foundation
What your business words mean, executably. Datasets, entity identity, your organisational hierarchy, your fiscal calendar, and your governed business definitions with their formulas. Everything above this layer inherits its correctness. If you already govern definitions in Snowflake Horizon, Fabric IQ, dbt or DataHub, Scoop consumes yours rather than duplicating it.
Deterministic query engine
Every number in every Scoop output is computed by a query engine, not written by a model. Fiscal-calendar-aware time series, stock-versus-flow metric semantics, peer-percentile methods pinned by formula. The AI selects from governed queries. It never writes one freehand and never does the arithmetic.
Investigation content
The layer nobody else sells. Probes, pattern detectors, diagnostic theory, severity rules, safety nets, and spawn routing — all versioned, auditable configuration. Not a prompt in one person's head. In a production deployment this runs to tens of investigation contexts, dozens of probes, and hundreds of governed business definitions.
Orchestration harness
Running tens of thousands of queries per cycle reliably and affordably is an engineering problem, not a prompting problem. A checkpointed multi-stage pipeline with resume, per-task model routing with fallbacks, deterministic replay, and hard cost caps. Screening is universal and cheap. Investigation is expensive and targeted. This is the difference between a pilot you can afford and a rollout you cannot.
Trust and release gates
Every output is swept mechanically before a human sees it. Evidence rules, language contracts, shape contracts, regression gates. A finding that does not clear the evidence bar is withheld, not softened. In a dashboard, a wrong number looks wrong. In AI prose, wrong reads fluent and confident. This is the layer that exists because of that asymmetry.
Delivery and hierarchy synthesis
One investigation, transformed for each level of your management structure. Flagged locations roll up as full synthesis. Healthy ones roll up as metrics only. Systemic patterns are separated from isolated issues. A store manager gets three actions. An executive gets a network pattern. Same underlying investigation.
Action and the decision trace
Actions come from a governed catalogue, not free text. They carry identity across cycles: new, continuing, reinforce, resolved, declined. Progress is measured by replaying the exact frozen query that set the baseline, never by asking a model to recall a number. Every cycle leaves a decision trace: what was found, what evidence supported it, what was recommended, what was done, and what happened to the number afterwards.
Few AI projects are high ROI, timeboxed, accurate. Pick all three.
AI analytics projects are hard to execute: ensuring output accuracy, maintaining data quality, governing security, codifying tribal knowledge, and managing inference costs at scale are non-trivial problems, especially across large organizations with multiple product lines and segments. We've done the hard work over 4 years already. Talk to us about how.
We typically get to pilot in around 4 weeks
Results in the first reporting cycle, not the tenth
Inference costs are carefully scoped and included in our pricing. No surprises
You're running a business. Scoop creates clear actions that generate impact, and tracks them
Your data stays in your control.
Scoop does not share your data or your operations with anyone outside the teams and systems required to deliver your implementation. We maintain strict separation between client engagements. Scoop became SOC 2 certified in 2025.
Book a discovery call
Reach out to start a two-way conversation about your industry, your performance variations across locations, and whether Scoop is the right fit for you.