Add an AI delivery layer — so capacity and depth stop being a hiring race.
02Do the work of a whole agency, as one person, without building the machine yourself.
03Punch above your size — a consumer-grade model wrapped in a real ecosystem.
04Senior-led AI capability that stands up in days, not quarters — with ownership built in.
Hire a dedicated marketer or team — AI-superpowered — from a simple starter plan upward.
02Scoped to a specific project or goal, priced to the outcome you want to hit.
03Pay for defined work, gated on a quality bar you set — flexible and lean.
Dashboarding and reporting operated as the decision surface of marketing — not as a vanity layer. Looker, Power BI, Tableau, warehouse-native — built around the KPI tree, not around the chart library. Built with you, owned by you. The panels that drive decisions, the ones that don't, killed.
Something changed. For a decade, dashboards were a delivery: hire a BI consultant, ship 40 panels, sign off. Six months later, the team uses two of them and the rest are noise. The fix was never “more panels.” The fix is starting from the KPI tree: what decisions does this dashboard need to drive, and which metrics inform each one. AI didn't change that — but it did make warehouse-native, semantically modelled dashboarding economically feasible at the depth a senior data team would build.
Forty panels, every metric the platform can produce, charted somewhere on the page. Impressive in a screen share; ignored on Monday morning. The team uses two panels; the rest are noise. The problem isn't the BI consultant's skill — it's that they were never asked to design the KPI tree first. Without that tree, every dashboard is a chart-library showcase instead of a decision surface. Until eighteen months ago, the senior-data-team work required to build dashboards around a real KPI tree was uneconomical for most companies. AI changed that.
KPI-tree design was an enterprise discipline. Building the cause-and-effect tree from top-line metric to tactical input, modelling it semantically so every dashboard reads from the same definitions, killing panels nobody uses — this was a senior data-team job that priced out of mid-market budgets. Most companies got platform-shaped consultants who shipped chart-library showcases instead.
KPI-tree design becomes affordable at depth. Semantic modelling, metric-definition automation, SQL generation for warehouse-native dashboards, anomaly detection by default, panel-usage analysis to kill the noise — held in one operating system, directed by senior people. Built with you, directed alongside you, owned together. Every panel earns its place. Every dashboard drives a decision.
An AI ecosystem is AI configured by fundamentals, directed by senior people, and stitched into a system that holds knowledge, sequence, and preparation in place — as opposed to AI used as a standalone tool. Dashboarding is the decision surface; the layers below are what make every panel earn its place. Hover or tap any segment to see what lives in it.
Hover or tap any layer or segment — its details appear right on the diagram.
For a decade, dashboarding capped out at chart-library showcases. AI changed the ceiling — above-average implementation with AI-assisted SQL and faster panel builds is what every BI consultant now claims. But that ceiling is commodity. The new game — dashboards built around a real KPI tree, every panel earning its place — is what only an AI ecosystem reaches.
We own the number you're judged on — end to end, with skin in the game. The strategy is built with you, the system is owned by you, the outcome is shared between us.
Reciprocity. A 20-year operating ethic embedded in the name: build for others as you would for yourself. It's why the work is honest.
Frameworks · 50+ industries of pattern recognition · full-stack delivery. The AI ecosystem makes senior depth scalable. Human-in-the-loop pushes it above the commodity ceiling.
Fixed-plus-variable on every engagement, a portion held against quality. Plus the 5-Day Experience — we deliver the strategy before you commit. We go first because we're confident.
Every BI consultant promises dashboards that drive decisions. The real question is whether they design the KPI tree first or just ship chart-library showcases. Don't leave it to chance. Experience the work in five days — on your top-line metric, your drivers, your decisions.
A 60-minute call. We agree your top-line KPIs, your current reporting state, and what decisions you want the dashboards to drive. The Day 0 call is with the operator who will run the work — not a sales lead.
We produce a KPI tree spec for your business, an executive dashboard wireframe, a marketing operating dashboard wireframe, and a metric-definition glossary — on your real business. Built with you, in dialogue.
You see the KPI tree, the wireframes, the glossary. You judge whether every panel earns its place. If yes, we talk about an engagement. If no, you keep the work — no charge, no obligation.
Sample Documents are anonymised client-facing deliverables — the actual artefacts a dashboarding and reporting engagement produces.
The full KPI tree a senior data lead would design: top-line metric, driver tier, tactical inputs, ownership map, panel-to-decision crosswalk. Built so every dashboard reads from the same definitions.
View sample →The dashboard layout a senior data lead would design for an executive audience: top-line metric foregrounded, three driver panels, contextual annotations, narrative panel for the quarterly story.
View sample →The actual Day-5 artefact from a mid-market dashboarding engagement: KPI tree, executive dashboard wireframe, operating dashboard wireframe, metric glossary.
View sample →Selectivity is part of the work. Here's who a dashboarding engagement is built for — and who it isn't.
KPI tree design, semantic modelling, dashboard wireframe, executive pack, panel audit and kill-list. The work that turns “we have 40 dashboards nobody reads” into “the team opens the same dashboard every Monday.”
Panel-usage analysis, quarterly KPI-tree evolution, metric-definition maintenance, anomaly detection, board-cycle reporting cadence. The work that keeps dashboards from drifting back into the vanity layer they came from.
A dedicated dashboarding team operating your decision surfaces — KPI tree, dashboards, executive pack, quarterly evolution. The full AI ecosystem behind them. Owned alongside you.
Recurring · from a starter plan upwardA defined dashboarding project (KPI tree rebuild, executive dashboard, board reporting overhaul, warehouse-native migration) priced to the outcome. Goal-aligned economics with a portion held against quality.
Goal-pricedThe lightest engagement: discrete tasks — a panel audit + kill-list, a single decision dashboard, a board-pack template — priced and delivered task by task. The simplest way to co-create a first piece of work.
Per-task pricingWork-first. We deliver, then charge. A portion held against quality, every engagement.
A vanity dashboard shows everything — every metric the platform can produce, charted somewhere on the page, often with comparison bars and trend lines for visual completeness. A decision dashboard shows the few metrics that change actions, arranged so the cause-and-effect is legible at a glance. The first looks impressive in a screen share. The second changes what someone does Monday morning. Most dashboards are the first because building the first is faster — nobody designed the KPI tree underneath.
Looker Studio (formerly Data Studio), Looker (enterprise), Power BI, Tableau, Hex, Mode, Metabase, Sigma, Domo, Klipfolio, Superset, and warehouse-native (BigQuery, Snowflake, Redshift, Databricks) dashboarding. The platform is the canvas; the KPI tree underneath is the work.
A KPI tree is the structured hierarchy from your top-line metric (revenue, ARR, pipeline) down through its operating drivers (channel mix, conversion rate, retention) to the tactical inputs that affect each driver. Every dashboard panel should answer the question: which node in the tree am I monitoring, and what decision does this drive? Dashboards built around a KPI tree are decision surfaces. Dashboards built around chart libraries are vanity layers. Most agencies build the second.
A BI consultant configures the platform and builds the dashboards. We operate dashboarding as the decision surface of marketing — the KPI tree is the design constraint, the platform is the canvas, and the work continues after launch: maintaining data quality, evolving the metrics as the business evolves, killing panels nobody uses, adding panels that hold someone accountable. BI consultants ship dashboards. We operate decision surfaces.
Yes — executive and board reports are dashboards with the audience in mind. The KPI tree is the same; the framing changes: which metrics belong in a quarterly board doc, which annotations explain the variance, what narrative ties the numbers to the strategy. We deliver executive packs alongside operational dashboards because the same underlying measurement should drive both surfaces.
Closely. Dashboards read from data; data quality is upstream of dashboard quality. Most engagements that start with “our dashboards are useless” trace back to data issues nobody owned — the analytics & tracking work and the dashboarding work are often the same engagement scoped differently. We do both; the same operator directs the full path from event capture to decision panel.
Three things. AI made warehouse-native dashboarding economically feasible — SQL generation, semantic-layer modelling, and metric-definition automation collapsed the cost of building correct dashboards. AI also made anomaly detection a default expectation. And the cookie deprecation broke half the canonical marketing dashboards, forcing rebuilds across the industry. Dashboarding that ignores these is on the 2022 playbook.
Three engagement models — Build a dedicated team, Project & goal-based, Task & quality-driven. All three operate work-first with fixed-plus-variable economics — we deliver, then charge; a portion is held against quality. Specific pricing is configured per engagement and shared on the Day 0 call.
Deep, decision-focused pages built for specific platform × audience combinations.
Built TechShu over 15+ years across 50+ industries. Now operating techshu.ai — the AI-era avatar — where the AI ecosystem does the heavy lifting and senior judgement directs every output. The 5-Day Experience is run personally; the Day 0 call is the same person who will direct the KPI-tree work and the dashboards that sit on it if you decide to engage.
Every BI consultant promises dashboards that drive decisions. The real question is whether they design the KPI tree first — or just ship more panels. Don't leave it to chance. Experience the work. If the decision-surface depth isn't real, you keep the KPI tree and the wireframes — and we move on.